appendix b26 - the run-time model€¦ · appendix b26 – the run time model this appendix...

Appendix B26 – The Run Time Model

This appendix contains 4 reports that document the development of the run-time model for estimating

BRT run-times. The run-time model uses highway journey times from AIMSUN along with patronage

estimates from the multi-modal model to forecast accurate run-times for the BRT service.

� Sheffield City Centre AIMSUN Model Validation Report (March 2010)

� Lower Don Valley Model Update / BRT North (October 2010)

� BRT North Run-Time Modelling (July 2011)

� Northern Route Base Run-Time Model Validation Report (September 2011)

Sheffield City Council syITS Microsimulation Modelling Framework Sheffield City Centre Aimsun Model Validation Report Black

REV A

Sheffield City Council syITS Microsimulation Modelling Framework Sheffield City Centre Aimsun Model Validation Report

March 2010

This report takes into account the

particular instructions and requirements

of our client.

It is not intended for and should not be

relied upon by any third party and no

responsibility is undertaken to any third

party Ove Arup & Partners Ltd

Admiral House, Rose Wharf, 78 East Street, Leeds LS9 8EE

Tel +44 (0)113 2428498 Fax +44 (0)113 2428573 www.arup.com

Job number 207585-15

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Ove Arup & Partners Ltd Rev A 25 March 2010

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Document title Sheffield City Centre Aimsun Model Update File reference

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Draft 1 04/06/09 Description First draft

Prepared by Checked by Approved by

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Rev A 25/03/10 Filename 2010-03-25_LWC.City Centre Model Validation Report.Draft

Description Updated to reflect conversion to Aimsun v6.1


Name Adam Smout / Lung-wen Chang

Andrew Bradshaw Paul Irwin

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Issue Document Verification with Document �

Sheffield City Council syITS Microsimulation Modelling FrameworkSheffield City Centre Aimsun Model Validation Report


Ove Arup & Partners Ltd Rev A 25 March 2010

Contents

Page

1 Introduction 1

1.1 Background to the City Centre Model Development 1

1.2 Study Objectives 2

1.3 Report Structure 2

2 Model Network Update 3

2.1 Network Extent 3

2.2 Network Modifications 4

2.3 Modifications to Centroid Connectors 4

2.4 Traffic Demand 4

2.5 Modelled Base Year and Time Periods 5

2.6 Traffic Signals 5

3 Model Calibration 6

3.1 Introduction 6

3.2 General 6

3.3 Calibration of the Mesoscopic Model 7

3.4 Calibration of the Microscopic Model 9

3.5 Link Characteristics 10

3.6 Trip Generation 11

3.7 Vehicle Characteristics 12

3.8 Simulation Step – Reaction Time 12

3.9 Car Following and Lane Change Models 13

3.10 Demand Data Calibration 13

3.11 Final Calibrated Cordon Flows 14

4 Validation 17

4.1 General 17

4.2 Link Flow Validation 17

4.3 Queue Length Validation 21

5 Summary 22

Appendices

Appendix A

Matrix Calibration

Appendix B

Validation Results



Ove Arup & Partners LtdRev A 25 March 2010



Page 1 Ove Arup & Partners LtdRev A 25 March 2010

1 Introduction

1.1 Background to the City Centre Model Development

In 2003, Ove Arup and Partners Ltd (Arup) was commissioned by Sheffield City Council

(SCC) and Hammerson UK Properties plc to develop a microsimulation traffic model to

investigate a range of transport issues in the Broad Lane area and to assess the traffic

impact on Rockingham Street, between Broad Lane and Division Street of the principal car

park for the proposed New Retail Quarter (NRQ) development.

SCC identified the need to expand the study area covered by the microsimulation model to

encompass the whole of Sheffield city centre up to and including the Inner Ring Road, to

enable an investigation of the wider impact of the City centre masterplan proposals.

The Aimsun model has subsequently been used to assist the City Council in identifying the

combined impact of several proposed and committed developments and highway schemes.

As part of this work the following models were developed:

• A validated base year 2002 microsimulation traffic model of Sheffield city centre within

the Inner Ring Road for the AM peak hour, the PM peak hour and an average inter-peak

hour;

• A future year 2004 model, incorporating the proposed highway schemes such as Eyre

Street, Charter Row, Granville Square, and Sheaf Square proposals;

• A future year 2005 model, incorporation the various committed development proposals

including St Paul’s Place, Leopold Street and City Hall developments;

• A future 2007 model, incorporating the various committed development proposals

including Sheffield Digital Campus and proposed highway schemes, in particular, the

North Inner Ring Road.

The matrices used in the 2002 based year AIMSUN model were created by cordoning the

existing Strategic Sheffield and Rotherham District Saturn model (developed initially by

Halcrow, and then by MVA). These matrices were then adjusted to match observed traffic

counts. All future year matrices were created using the development data provided in

Transport Assessments and a spreadsheet developed to ‘assign’ the new trips to external

origins and destinations around the model’s cordon.

The 2007 future year model was used to test and to refine the highway changes associated

with the proposed NRQ and multi-storey car park on Eyre Street and West Street. In 2005,

Arup were appointed by SCC to update the ‘future year’ 2005 model (network and matrices)

to become a new base year model, using observed data to validate the model. Then to

create a 2007 future year Aimsun model to assess the impact of several proposed schemes

including the New Retail Quarter (NRQ) and the relocation of the markets.

In September 2009, SRTM2 was updated to (now known as SRTM3) and Arup was

commissioned by SCC under the syITS Micro-simulation framework to update, calibrate and

validate the Sheffield city centre Aimsun model in line with the current highway layout, signal

timings and traffic management measures and with demand that is consistent with SRTM3.

Arup has now been commissioned to update the model to reflect the conversion to Aimsun

v6.11.

1 Version 6.1.2 (R4624)




1.2 Study Objectives

The key objective of this study is to update the base year 2008 model microsimulation traffic

model of Sheffield city centre for the AM peak hour, the PM peak hour and an average inter-

peak hour.

1.3 Report Structure

This report sets out the stages of updating the Aimsun microsimulation traffic model, and

explains the process of the calibration and validation of the model.

Section 2 describes the model extent and the data sources used. Section 3 explains the

calibration of the microsimulation model and the validation is detailed in Section 4. The

conclusions of the report are summarised in Section 5.




2 Model Network Update

2.1 Network Extent

The extent of the Sheffield City centre Aimsun model remains the same and covers the

highway network within and including the Inner Ring Road. Figure 2.1 shows the study

area.

Figure 2.1: Extent of the Sheffield City Centre Model




2.2 Network Modifications

As part of the previous update work, a number of modifications were made to the network

within the model to make it consistent with the current highway layout. These changes

included:

• The Granville Square improvements and the provision of an additional westbound lane

St Mary’s Road;

• Removal of a northbound lane on Arundel Gate adjacent the Millennium Galleries;

• Amended coding on Mowbray Street and Burton Road to correct the lane usage;

• Changes in lane allocations northbound on Corporation Street;

• Additional entrances and exits for centroid 102 near The Wicker;

• Removal of a inbound lane and addition of a bus lane on Brook Hill;

• Addition of two entrances for centroid 10; and

• Addition of a pedestrian crossing on Penistone Road.

A number of further modifications have also been coded in the model, as follows:

• Installation of a signal-controlled pedestrian crossing on Hanover Way, to the north of the

junction with Broomhall Street;

• Addition of an inbound bus lane on Ecclesall Road; and

• Revised link structure at Moore Street roundabout, consistent with the Ecclesall Road

Aimsun model.

2.3 Modifications to Centroid Connectors

A comparison between the centroid connectors in Aimsun and the zone connectors in

SRTM showed that there were a number of inconsistencies with regards to where the

demand is loaded onto the highway network, particularly within the city centre. Some

modification of centroid connectors was undertaken for centroids within the city centre to

make the model consistent with STRM3. For clarity, centroid connectors to the following

centroids have been modified: 10, 12, 13, 15, 16, 18, 21, 24, 26, 27, 28, 35, 37, 38, 40, 41,

42, 43, 44, 49, 50, 61, 64, 65, 66, 67, 85 and 100.

2.4 Traffic Demand

The updated base year origin-destination (O-D) data was obtained from SRTM3 and input to

the Aimsun model. The STRM3 cordon was created by MVA and they provided 15 output

matrices (five vehicle types/ user classes, for the three time periods).

The Aimsun traffic demand is split into three vehicle types – private cars, light goods

vehicles (LGVs) and other goods vehicles (OGVs). The SRTM3 output matrices were

therefore factored from passenger car units (pcus) to vehicles per hour (vph) for each of the

three vehicle types as follows:

• Private cars – these are equivalent to the sum of user classes UC1, UC2 and UC3 in

SRTM3, assuming that one private car is equivalent to one pcu;

• LGVs – these are the equivalent of user class UC4 in the SRTM3, assuming that one

private car is equivalent to one pcu;

• OGVs - these are the equivalent of user class UC4 in the SRTM3, assuming that one

private car is equivalent to 1.7 pcu;




2.5 Modelled Base Year and Time Periods

The Aimsun base model year (2008) and time periods are consistent with SRTM3. The time

periods are as follows:

• AM peak (0800 to 0900 hours);

• Inter peak (a typical off-peak hour averaged from the time period 1000 to 1200 hours and

1400 to 1600 hours);

• PM peak (1700 to 1800 hours).

2.6 Traffic Signals

The existing Aimsun model updated by MVA was assumed to have up-to-date traffic signal

timings. However, during the calibration process, traffic signal timings that appeared

incorrect were reviewed and updated, as necessary, in consultation with SCC.




3 Model Calibration

3.1 Introduction

Model calibration is the process of adjusting the parameters of the model to ensure that

simulated traffic flows, routes and travel behaviour correspond with observed behaviour.

Validation is the process of checking model output against an independently observed

dataset to ensure that the calibrated model is robust.

3.2 General

The Sheffield City Centre Aimsun model has been calibrated and validated at Dynamic

Mesoscopic and Microscopic simulation level.

3.2.1 Mesoscopic simulator

The Mesoscopic simulator models the vehicle individually, but it simplifies the vehicle

behaviour using, for example, simplified car following and lane changing models. Dynamic

user equilibrium is used in the mesoscopic simulation for the Sheffield City Centre Aimsun

model. Dynamic user equilibrium allows the result to converge, and output with a single

result. This result is used to create a path assignment file which is then read into the

microscopic simulation.

3.2.2 Microscopic simulator

In the microscopic simulator, the behaviour of each vehicle in the network is continuously

modelled throughout the simulation period. It includes the vehicle behaviour and other

elements of the system that change continuously. Continuous changes often results in route

choice “flipping”, meaning, vehicles change their route choice completely to an alternative

route from one ‘sub-period’ to the next. Fixing a percentage of vehicles to use the

mesoscopic path assignments in the microscopic simulation is a way of avoiding such a

problem. Dynamic traffic assignment is used in the microscopic simulation for the Sheffield

City Centre model. Thus, the result of each microscopic simulation varies. The simulation

results contain ten replications and the averages are reported.

3.2.3 Calibration of the Models

A number of features within the Aimsun models (meso and micro) need calibrating to ensure

the best representation of the network and traffic demand. The calibration parameters in the

model include:

• Cost function;

• Mesoscopic dynamic user equilibrium model;

• Microscopic route choice model;

• Link characteristics;

• Demand release rate profile;

• Vehicle characteristics;

• Simulation step and reaction time;

• Road capacity.

The calibration of the mesoscopic and microscopic models is discussed in detail in the

following sections.




3.3 Calibration of the Mesoscopic Model

3.3.1 Cost Function

Vehicle assigned throughout the network are based on lowest cost route from a link to a

destination zone. Route cost is measured in terms of the time of a vehicle takes to travel

through the network, the distance travelled and any other applied costs. There are several

Cost Functions programmed in to Aimsun, and it is also possible to use ’user defined’ cost

functions to further refine the model. Although throughout the development of the model

different user defined cost functions were used, the final Cost Function used in the model is

the default function.

The default cost function uses both link capacity and user defined costs to determine the

costs (travel times) on each link in the network having regard to the level of traffic on the

network. The weighting applied to the link capacity and user defined costs can be altered,

which in turn will affect link costs and hence the path assignment. By altering these

parameters, the route choice model can be calibrated.

3.3.2 Mesoscopic Dynamic User Equilibrium

The mesoscopic simulator has been used to determine a Dynamic User Equilibrium (DUE)

in terms of route choice. In static user equilibrium, the journey times on all the routes

actually used are equal, and less than those which would be experienced by a single vehicle

on any unused route. In a DUE, the journey paths are able to change over time to react to

changes in levels of congestion, but, at any point in time, a user equilibrium is established.

In Aimsun, DUE is achieved through an iterative process by the application of a modified

version of the Method of Successive Averages (MSA). In each iteration, the solution should

converge towards an equilibrium solution. The measure used to define how close the

modelled traffic flows are to a user equilibrium is the Relative Gap, which is the ratio of the

total excess cost with respect to the total minimum cost if all travellers had used shortest

paths. The default stopping criteria for convergence in Aimsun is a Relative Gap of 1.5%

and this stopping criterion was used for the Sheffield City Centre Aimsun model.

3.3.3 Final DUE Parameter Settings

Table 3.1 illustrates the final values for the user defined settings in the mesoscopic DUE

model for the Sheffield City Centre Aimsun model and Figures 3.1, 3.2 and 3.3 show the

DUE convergence for each of the three time periods.

Table 3.1: Mesoscopic DUE settings

AM IP PM

Meso interval 1 hour 1 hour 1 hour

Meso output statistics 1 hour 1 hour 1 hour

Meso cycle time 15 mins 15 mins 15 mins

Capacity Weight 1 1 1

User Defined Cost Weight 0 0 0

Numbers of iterations to achieve relative gap at 1.5% 10 13 9




Figure 3.1: AM peak DUE convergence

Figure 3.2: Inter peak DUE convergence




Figure 3.3: PM peak DUE convergence

The above graphs show that each DUE assignment has converged to an acceptable level

without any oscillation occurring.

The path assignments for each of the three time periods were outputted to a file for use in

the microscopic simulation.

3.4 Calibration of the Microscopic Model

3.4.1 Microscopic Route Choice Model

The route choice model defines the drivers’ decision of which path to take from a set of

alternatives, connecting one origin to one destination, depending on the cost calculation by

the cost function. The ‘standard’ route choice models within Aimsun include:

• Fixed (time);

• Binomial;

• Proportional;

• Logit;

• C-Logit.

This study uses C-Logit model, as the network includes many alternative and overlapping

paths between origins and destination. There are global parameters within the C-Logit

model, which require calibration as follows:

• Cycle time – length of one calculation interval;

• Initial K-SPs – number of paths used during the warm-up period;

• Maximum number of routes – number of routes for each O-D pair to which vehicles are

assigned;

• Scale factor – Affects the stochastic assignment, a small scale factor will give a large

variability about the true route costs, whereas a large scale factor will give a small

variability about the true route costs;




• Beta factor – Affects the stochastic assignment, different value of beta factor will have

effect on the route choice decision. The higher the beta factor is, the more likely the

drivers’ choose non-overlapped longer paths than heavy overlapped shortest paths;

• Gamma factor – Also affects the stochastic assignment.

These parameters were calibrated as shown in Table 3.2 below.

Table 3.2: C-Logit Calibrated Parameter Values

C-Logit Model Parameter Final Calibrated Value used

Cycle time 5 minutes

Initial K-SPs 3

Maximum number of paths 3

Scale factor 7

Beta factor 1

Gamma factor 1

The calibrated parameter values used in the cost function for the microscopic route choice

model are as shown in Table 3.3.

Table 3.3: Cost Calibrated Parameter Values

Parameter AM IP PM



3.4.2 Use of Mesoscopic DUE Paths

In order to increase stability and reduce the occurrence of “route flipping” within the

microsimulation model, a proportion of vehicles were assigned to use the DUE path

assignments from the Mesoscopic simulator, with the rest following paths determined by the

dynamic traffic assignment. These proportions were determined separately for each peak

period following sensitivity testing and are as shown in Table 3.4.

Table 3.4: Proportions of Vehicles in the Microscopic Model Following DUE Paths

and DTA Paths

AM IP PM

Meso DUE path assignment 10% 10% 10%

3.5 Link Characteristics

3.5.1 Link Speed and Capacity

It is possible to define road types in Aimsun. Each road type is defined by the speed limit

and the capacity of the link. The speed limit of each link was informed by information

received from SCC. The ‘capacity’ of each section is used as a factor in the cost function.

Thus, this variable is essential to calibrate the model to obtain a good fit to the observed

data.

The existing Sheffield City Centre Aimsun model already has a detailed road hierarchy.

During the updating process, minor numbers of links were modified. Details of the link are

shown in Table 3.5 below;




Table 3.5: Link Categories

Name Speed (kph)

Capacity (vehs/lane/hour)

Description

Urban Road_65

65 2,200 Used on the ring road on sections with a 40 speed limit

Urban Road_50

50 1,800

Used on the ring road on sections with a 30 speed limit, plus key city centre routes such as Charter Row, Arundel Gate and Broad Lane

Urban Street 1 48 1,600 Used on sign posted through routes such as Rockingham Street

Urban Street 2 45 1,200 Other roads

Urban Street 3 42 500

Urban Street 4 30 300 Used on most minor roads to discourage the use as a through route

3.5.2 Visibility

The visibility parameter can be used to control the behaviour at the end of a link. This was

used extensively in the calibration of the model, to further slow vehicles approaching priority

junctions, and to control the capacity of roundabout approaches.

3.5.3 Yellow Box Speed

The yellow box junction will prohibit a vehicle from entering the junction area whenever the

vehicle is moving at a speed below this parameter. This facility simulates the effect of slow

moving traffic on the main road allowing traffic from a minor side road, can be used to avoid

gridlock which often occurs in many microsimulation models, and can be used to adjust the

relative capacity of approaches.

3.6 Trip Generation

When loading a traffic demand into the simulation model a number of different models can

be used to determine the headway between two consecutive vehicle arrivals. The trip

generation model is a global parameter that is set on a per scenario basis. In the Sheffield

City Centre Aimsun model, the ‘Exponential’ distribution has been used.

Five types of traffic generation are available in Aimsun: exponential uniform, normal,

constant and ASAP. Figure 3.4 illustrates the trip generation profile for each type of

distribution. Clearly, the ASAP distribution is not appropriate for this model and was

therefore discounted. Sensitivity testing of the other distributions was undertaken to

determine which best reflected reality. It was found that the exponential distribution gave the

most realistic results, as it allows for more variation in arrivals and also accounts for some

platooning of arrival traffic.




Figure 3.4: Trip Generation Comparison

3.7 Vehicle Characteristics

There are several vehicle characteristics specified in the model. The mean, standard

deviation, maximum and minimum values, as well as types and limits of distribution are

carefully defined. The characteristics can be broadly split into two categories, vehicle

properties, and driver characteristics. Vehicle properties include size, maximum speed and

maximum acceleration and driver characteristics include speed acceptance, minimum

distance between vehicles and maximum give way time. All of these characteristics were

carefully considered and modelled as accurately as possible based on guidance from TSS

(Aimsun model developers) and previous modelling work carried out in the Sheffield area.

3.8 Simulation Step – Reaction Time

The simulation step is the system update time interval and at every simulation step, the

state of all the elements of the system is updated. The reaction time is a global parameter

that controls the time it takes a driver to react to the changing speed of the preceding

vehicle. It is equal to, or a factor of, the simulation step, and affects the car following and

lane change models.

The reaction time at stop is also a global parameter and controls how quickly vehicles react

once they are stationary. This therefore influences the capacity of priority and signal-

controlled junctions and has a strong influence on queuing behaviour.

All parameters were used in the calibration and validation processes in the Sheffield City

Centre Aimsun model are shown in Table 3.6.




Table 3.6: Simulation Step and Reaction Time Parameters

Time Period Reaction Time

(Related to Simulation Step)

Reaction Time at Stop

AM Peak (0800 to 0900 hours) 0.7 0.7

Inter Peak (1400 to 1500 hours) 0.7 0.7

PM Peak (1700 to 1800 hours) 0.7 0.7

3.9 Car Following and Lane Change Models

Both car following and lane changing are global modelling parameters for which it is

possible to alter the default settings. The Minimum Headway + Deceleration estimation car

following model was used in Sheffield City Centre Aimsun model. The lane changing model

is a decision process and the factors of the model include percent overtake and percent

recover. In the Sheffield City Centre Aimsun model, none of the values were changed from

these default settings, which are shown in Table 3.7.

Table 3.7: Lane Change Model Parameters

Percentage Overtake Percentage Recover

90% 95%

3.10 Demand Data Calibration

Calibration of the demand data (matrices) formed a substantial part of the overall calibration

process. The modelled flow data were collected by the detectors and compared with the

observed data provided by MVA. The data comparison includes consider the absolute

differences, the percentage differences and the GEH statistic.

The GEH statistic is used in traffic engineering as a ‘goodness of fit’ test to compare two

sets of traffic volumes. It is a self-scaling formula, mathematically similar to the chi-squared

test, which reduces the pitfalls that occur when using a simple percentage comparison. The

criteria recommended in Design Manual for Roads and Bridges (DMRB) Volume 12 for a

model to be calibrated / validated is that 85% of link flows should have a GEH statistic of

less than 5 and 100% of links should have a GEH statistic of less than 10.

Table 3.8 below shows the percentage of links with a GEH statistic of less than 5.0 for each

of the peak periods modelled before link flow calibration. The table shows that the initial

modelled results varied greatly from the observed data and were significantly below the DfT

criteria. Thus, demand data calibration was necessary.

Table 3.8: Initial Results before Calibration

Criteria / Target Weekday Modelled Hour

AM IP PM

GEH GEH<5 50.44% 39.83% 54.39%

3.10.1 Cordon Demand Data Calibration

Although the SRTM3 has been validated, the output derived is different from the observed

traffic flow. Thus, where observed traffic counts on the cordon were available, they have

been used to calibrate the amount of traffic entering and leaving the boundary of the Aimsun

model.




The numbers of trips in the O-D matrix were compared with the number of observed trips

crossing the outer cordon. Many of the trips in the OD matrix differed greatly when

compared to the observed data. Details of the changes made to the matrices (AM peak, PM

peak and inter peak) are shown in Tables A1, A2 and A3 in Appendix A.

3.10.2 Internal demand data calibration

Occasionally, individual cell values (or full row or column totals) for the internal centroids

were amended to match the observed flows for these links. Details of these adjustments,

along with the justification, are shown as Tables A4, A5 and A6 in Appendix A.

3.10.3 Total Demand Before and After Calibration

The total demand in the matrices before and after the model calibration is shown in Tables

3.9 and 3.10. The tables show that the demand data calibration process has slightly

reduced the overall demand, which is primarily associated with reducing cordon entry flows

to match both observed traffic counts and queuing behaviour.

Table 3.9: Total Demand (before calibration) in Model

Time Period Car LGV OGV

AM Peak (0800 to 0900 hours) 17,564 2,403 704

Inter Peak (1400 to 1500 hours) 13,478 2,883 872

PM Peak (1700 to 1800 hours) 19,157 1,406 143

Table 3.10: Total Demand (after calibration) in Model


AM Peak (0800 to 0900 hours) 17,558 2,300 718

Inter Peak (1400 to 1500 hours) 13,041 2,785 882

PM Peak (1700 to 1800 hours) 18,957 1,445 180

3.11 Final Calibrated Cordon Flows

After matrix calibration, the modelled (average of ten model runs) and observed cordon

traffic flows were compared using the GEH statistic. Table 3.11 shows the percentage of

cordon links in the three modelled time periods that have a GEH statistic of less than 5.0

and confirms that the cordon traffic flows are calibrated to the DMRB criteria. The detailed

inbound results are shown in Table 3.12 and the outbound results are shown in Table 3.13.

Table 3.11: Cordon Inbound

Cordon Criteria / Target Weekday Modelled Hour

AM IP PM

Inbound GEH<5

96% 96% 100%

Outbound 96% 100% 96%




Table 3.12: Cordon Inbound

Detector Observed Flow Modelled Flow Difference GEH

Ref Location AM IP PM AM IP PM AM IP PM AM IP PM

1108-1098 Granville Road 809 493 654 808 502 700 -1 9 46 0.0 0.4 1.8

1183-1175 Burton Road 418 358 386 308 339 393 -110 -19 7 5.8 1.0 0.4

1201-1134 Meadow Street 307 125 145 287 114 167 -20 -11 22 1.2 1.0 1.8

1207-2098 Bolsover Street 538 321 346 562 357 362 24 36 16 1.0 2.0 0.8

1211-2099 Brook Hill 967 929 819 872 996 755 -94 67 -64 3.1 2.2 2.3

1320-2076 Moore Street 1197 1016 916 1220 1034 968 23 18 52 0.7 0.6 1.7

1374-1107 Queens Road 872 650 687 834 632 605 -37 -18 -82 1.3 0.7 3.2

1377-1138 Furnival Road 317 278 312 271 193 339 -46 -85 27 2.7 5.5 1.5

1379-1456 Leveson Street 260 189 233 256 188 236 -3 -1 3 0.2 0.1 0.2

1449-1195 Penistone Road 2117 1435 1426 2033 1470 1367 -83 35 -58 1.8 0.9 1.6

1483-1346 Bramall Lane 1036 691 685 1074 732 747 38 41 62 1.2 1.5 2.3

1489-1117 Broad Street 370 253 366 364 279 305 -6 25 -61 0.3 1.6 3.3

1584-1095 Edmund Road 175 73 67 225 80 65 50 8 -2 3.6 0.9 0.2

2035-2036 Broomspring Lane 29 41 41 30 38 46 1 -3 5 0.2 0.4 0.7

2036-2035 Broomspring Lane 22 42 33 34 16 17 13 -26 -16 2.4 4.9 3.3

2037-1147 Wharf Street 5 12 36 7 14 34 2 2 -2 0.7 0.6 0.3

5010-2009 Sheffield Parkway 3047 1634 1955 2873 1698 1998 -174 64 43 3.2 1.6 1.0

5116-1362 Shoreham Street 594 152 133 617 175 133 23 23 0 1.0 1.8 0.0

7370-7359 Sutherland Road 132 69 113 119 81 123 -13 12 11 1.2 1.4 1.0

7390-7528 Burngreave Road 214 213 217 249 266 242 35 53 25 2.3 3.4 1.7

7529-7528 Rutland Road 313 238 222 255 244 206 -58 6 -17 3.5 0.4 1.2

8851-1456 Attercliffe Road 385 319 384 417 338 420 32 19 36 1.6 1.1 1.8

8870-1385 Savile Street 673 405 661 566 378 553 -107 -28 -107 4.3 1.4 4.4

8872-7359 Carlisle Street East 436 306 427 382 313 392 -54 7 -34 2.7 0.4 1.7

9732-1222 Glossop Road 310 280 271 329 315 309 20 34 38 1.1 2.0 2.2




Table 3.13: Cordon Outbound

Detector Observed Flow Modelled Flow Difference GEH

Ref Location AM IP PM AM IP PM AM IP PM AM IP PM

1091-9210 Fornham Street 280 268 264 216 263 234 -63 -5 -30 4.0 0.3 1.9

1095-1584 Edmund Road 48 33 26 39 58 48 -9 26 22 1.4 3.8 3.6

1098-1108 Granville Road 246 180 284 263 236 318 17 56 34 1.1 3.9 2.0

1099-1375 Farm Road 221 257 550 188 240 544 -33 -17 -6 2.3 1.1 0.2

1107-1374 Queens Road 462 536 935 412 569 878 -50 33 -56 2.4 1.4 1.9

1116-1117 Park Square 116 147 150 139 161 154 23 13 4 2.0 1.1 0.4

1117-1489 Broad Street 125 138 160 139 161 154 14 23 -6 1.2 1.9 0.4

1131-5012 Sheffield Parkway 2213 1899 3195 2045 1913 3011 -168 13 -184 3.6 0.3 3.3

1134-1201 Meadow Street 141 195 346 154 245 329 13 50 -17 1.1 3.4 0.9

1138-1377 Furnival Road 217 238 178 152 185 216 -64 -53 38 4.7 3.6 2.7

1147-2037 Castlegate 31 7 8 35 19 7 4 12 -1 0.7 3.3 0.3

1172-7529 Pitsmoor Road 138 188 344 130 195 321 -8 8 -23 0.7 0.6 1.2

1175-1183 Burton Road 279 223 254 254 246 261 -24 23 8 1.5 1.5 0.5

1212-8058 Brook Hill 1029 840 1127 960 802 1095 -69 -38 -32 2.2 1.3 1.0

1222-9732 Glossop Road 397 319 333 349 298 309 -48 -22 -24 2.5 1.2 1.4

1362-5116 Shoreham Street 119 194 301 128 234 354 9 40 53 0.8 2.7 2.9

1384-7592 Savile Street 638 464 665 515 542 626 -122 77 -39 5.1 3.5 1.5

1456-1379 Leveson Street 347 264 354 347 256 340 0 -8 -14 0.0 0.5 0.7

1456-8851 Attercliffe Road 319 272 220 282 307 223 -37 35 4 2.1 2.1 0.2

1501-1123 Park Square 288 334 538 255 311 435 -33 -23 -103 2.0 1.3 4.7

2077-1592 Ecclesall Road 990 891 1044 881 818 1004 -109 -73 -40 3.6 2.5 1.2

2088-1484 Bramall Lane 499 792 1115 455 829 1039 -45 37 -76 2.1 1.3 2.3

2098-1207 Bolsover Street 350 337 587 351 328 499 1 -9 -88 0.1 0.5 3.8

7359-7370 Sutherland Road 54 71 120 64 66 136 11 -4 16 1.4 0.5 1.4

7528-7390 Burngreave Road 240 240 296 249 281 287 8 41 -9 0.5 2.5 0.5

7528-7529 Rutland Road 376 310 457 286 279 372 -90 -31 -85 4.9 1.8 4.2




4 Validation

4.1 General

The validation process determines whether the simulated model is an accurate

representation of the observed situation by comparing modelled output data with observed

data. The validation results are an average of ten model runs for each modelled period, as

each model replication is unique.

The following data was used to validate the model:

• Link flow validation;

• Queue lengths.

4.2 Link Flow Validation

4.2.1 GEH Statistics

The primary validation of the model concentrated on ensuring that the model outputs agreed

within accepted levels of confidence with the observed traffic counts. The approach adopted

was to validate modelled link flows using available traffic count data. The GEH statistic was

chosen as the measure of ‘goodness of fit’ of the model. In accordance with DMRB Volume

12 Section 2 Part 1 Chapter 4, the aim is to achieve 85% of the validation links to have a

GEH statistic of less than 5.

The locations of the detectors used in the validation are shown in Figure 4.1 below.




Figure 4.1: Locations of Validation Detectors

4.2.2 Validation Summary Results

The full results of the validation can be found in Tables B1, B2 and B3 in Appendix B. In

summary, the three time periods were, in general, shown to validate to a high degree of

accuracy, meeting the selected DMRB criteria and targets, as mentioned previously. A

summary of these results is provided in Table 4.1.

Table 4.1: Validation Summary Results

Criteria / Target

Weekday Modelled Hour

AM IP PM

GEH GEH<5 85% 87% 88%




4.2.3 Regression Analysis

As well as considering the GEH statistic, DMRB volume 12 also recommends the use of

regression analysis to compare how well the observed and modelled data are correlated.

The regression analysis calculates the correlation coefficient (R), which can be used to

measure the goodness of model fit. A correlation coefficient of 1.0 would denote a perfect fit

and DMRB advises that the correlation co-efficient should be greater than 0.95.

Figures 4.2, 4.3 and 4.4 illustrate the regression lines in AM peak, inter peak and PM peak

respectively and Table 4.2 summarises the values of the correlation coefficient, R. The

table shows that the validation links in all three periods have a correlation co-efficient that

exceeds the DMRB guidance for validation.

Table 4.2: Correlation Coefficient

AM IP PM

Correlation coefficient, R

0.981 0.983 0.987

Figure 4.2 AM Peak Regression Line




Figure 4.3: Inter Peak Regression Line

Figure 4.4: PM Peak Regression Line




4.3 Queue Length Validation

No surveyed queue length data is available, but the queue length validation was undertaken

by eye by both members of the Arup Transport Planning team and members of the SCC

team using combined professional judgement and knowledge of the observed situation.

This formed a valuable part of the model validation process. Generally, the locations and

lengths of queues are considered reasonable.




5 Summary

This report outlines the data update, calibration and validation of the microsimulation model

of the Sheffield City Centre model. The microsimulation model matrices were extracted from

SRTM3 by applying the cordoning technique.

The model has been built and validated in three time periods, AM peak (0800 to 0900

hours), inter peak (averaged from the time period 1000 to 1200 hours and 1400 to 1600

hours), and PM peak (1700 to 1800 hours).

The model was calibrated by adjusting the global and local parameters to ensure the model

represents the network and the traffic demand. The cost function and the route choice

models were also carefully calibrated.

The validation of the model compared the modelled and observed link flows at number of

locations, as well as modelled and observed journey times. In all time periods, more than

85% of validation links had a GEH statistic of less than 5. Considerable effort was also

made to reduce very poorly validating links to achieve a GEH of less than 10, in order to

produce a robust model. All links achieve a GEH statistic of less than 10 in the AM and PM

peak hour models, with 98% of links achieving a GEH statistic of less than 10.

Additionally, a regression analysis has been undertaken which shows that validation links

within all three time periods have correlation coefficients with values well above the 0.95

value required by DMRB.

The assignment and queue lengths were also validated by both members of the Arup and

SCC teams who have local knowledge of Sheffield city centre and the final base model is

considered an acceptable representation of the observed traffic conditions during the three

modelled time periods.

Appendix A

Matrix Calibration



Page A1 Ove Arup & Partners LtdRev A 25 March 2010

Table A1: AM Peak Cordon Demand Data Calibration

Centroid Number of trips

From To Observed Original Amended Change in percentage Reason

AM

Peak

604 2117 2334 2081 see table A7 To adjust virtual queue

604 621 add additional 200 trips

To adjust virtual queue

604 642 NA 390 257

Multiply by 0.80 and round to nearest integer to reduce all traffic to 1000, and add 200 to C642

To reduce a virtual queue

614 132 154 114 Multiply by 0.75 and round to nearest integer

To match with observed number



617 673 380 603 Multiply by 1.58 and round to nearest integer.













to create a virtual queue

630 872 see below**







Page A2 Ove Arup & Partners Ltd Rev A 25 March 2010


to increase a virtual queue







608 138 13 130 Multiply by 10 and round to nearest integer















Table A2: Inter Peak Cordon Demand Data Calibration



Inte

r P

ea

k






















Table A3: PM Cordon Demand Data Calibration



PM

Pea

k






























Table A4: AM Peak Internal Matrices Demand Data Calibration


From To Observe

d Original Amended

Change in percentage

Reason A

M P

eak

47/48 280 127 279 Multiply by 2.2 and round to nearest integer

To match with observed data

Table A5: Inter Peak Internal Matrices Demand Data Calibration


From To Observed Original Amended Change in percentage

Reason

Inte

r P

ea

k



Table A6: PM Peak Internal Matrices Demand Data Calibration


From To Observed Original Amended Change in percentage

Reason

PM

Pe

ak






Table A7: AM Matrix adjustment from centroid 604

From 604

Original Amended

To centroid car lgv ogv car lgv ogv

16 109 3 0 94 3 0

30 28 3 1 24 3 1

53 10 0 0 9 0 0

54 1 1 0 1 1 0

55 0 0 0 0 0 0

56 11 0 0 9 0 0

60 219 7 0 188 6 0

61 44 2 2 38 2 2

84 0 0 0 0 0 0

602 111 40 9 95 34 8

628 3 0 0 3 0 0

631 2 0 0 2 0 0

632 14 4 6 12 3 5

634 68 48 5 58 41 5

635 12 1 0 10 1 0

636 0 0 0 0 0 0

637 0 0 0 0 0 0

639 122 19 11 105 16 9

640 48 1 0 41 1 0

641 25 1 0 22 1 0

642 454 68 10 390 58 9

645 0 0 0 0 0 0

Table A8: AM Matrix adjustment from centroid 630

From 630

Original Amended

To centroid car lgv ogv car lgv ogv

19 0 0 0 5 0 0

63 14 0 0 140 0 0

41 7 0 0 70 0 0

65 1 0 0 10 0 0

66 0 0 0 5 0 0

Appendix B

Validation Results



Page B1 Ove Arup & Partners Ltd Rev A 25 March 2010

Table B1: Validation Results

Detector AM IP PM

Ref Location Observed Modelled GEH Observed Modelled GEH Observed Modelled GEH

1456-8851 Attercliffe Road 319 282 2.1 272 307 2.1 220 223 0.2

8851-1456 Attercliffe Road 385 417 1.6 319 338 1.1 384 420 1.8

2037-1147 Blonk Street 5 7 0.7 12 14 0.6 36 34 0.3

1147-2037 Blonk Street 31 35 0.7 7 19 3.3 8 7 0.3

1207-2098 Bolsover Street 538 562 1.0 321 357 2.0 346 362 0.8

2098-1207 Bolsover Street 350 351 0.1 337 328 0.5 587 499 3.8

1483-1346 Bramall Lane 1036 1074 1.2 691 732 1.5 685 747 2.3

2088-1484 Bramall Lane 499 455 2.1 792 829 1.3 1115 1039 2.3

1117-1489 Broad Street 125 139 1.2 138 161 1.9 160 154 0.4

1489-1117 Broad Street 370 364 0.3 253 279 1.6 366 305 3.3

1211-2099 Brook Hill 967 872 3.1 929 996 2.2 819 755 2.3

1212-8058 Brook Hill 1029 960 2.2 840 802 1.3 1127 1095 1.0

2035-2036 Broomspring Lane 29 30 0.2 41 38 0.4 41 46 0.7

2036-2035 Broomspring Lane 22 34 2.4 42 16 4.9 33 17 3.3

7389-7528 Burngreave Road 593 535 2.5 530 561 1.4 703 661 1.6

7528-7390 Burngreave Road 240 249 0.5 240 281 2.5 296 287 0.5

1175-1183 Burton Road 279 254 1.5 223 246 1.5 254 261 0.5

1183-1175 Burton Road 418 308 5.8 358 339 1.0 386 393 0.4

8872-7359 Carlise Street East 436 382 2.7 306 313 0.4 427 392 1.7

1184-9717 Corporation Street 1666 1565 2.5 1295 1347 1.4 1865 1914 1.1





1129-1128 Culters Gate 2096 2038 1.3 954 1093 4.3 1559 1424 3.5

1131-1132 Culters Gate 923 816 3.6 798 780 0.6 1101 1176 2.2

1132-1129 Culters Gate 1672 1827 3.7 814 854 1.4 1066 937 4.1

1138-1127 Culters Gate 2107 1703 9.3 1672 1674 0.0 2343 2552 4.2




Detector AM IP PM


2028-1137 Culters Gate 1950 1920 0.7 1056 1104 1.5 1649 1428 5.6

2069-1569 Culters Gate 1942 1633 7.3 1438 1531 2.4 2242 2343 2.1

1501-1123 Duke Street 288 255 2.0 334 311 1.3 538 435 4.7

2077-1592 Ecclesall Road 990 881 3.6 891 818 2.5 1044 1004 1.2

1320-2076 Ecclesall Road 1197 1220 0.7 1016 1034 0.6 916 968 1.7

1095-1584 Edmund Road 48 39 1.4 33 58 3.8 26 48 3.6

1584-1095 Edmund Road 175 225 3.6 73 80 0.9 67 65 0.2

1352-2085 Eyre Street 224 192 2.2 442 418 1.2 652 487 6.9

1099-1375 Farm Road 221 188 2.3 257 240 1.1 550 544 0.2

1091-9210 Fornham Street 280 216 4.0 268 263 0.3 264 234 1.9

1138-1377 Furnival Road 217 152 4.7 238 185 3.6 178 216 2.7

1377-1138 Furnival Road 317 271 2.7 278 193 5.5 312 339 1.5

2005-9711 Gibraltar Street 1121 998 3.8 660 615 1.8 692 691 0.0

9719-9711 Gibraltar Street 801 682 4.4 714 675 1.5 945 1043 3.1

1222-9732 Glossop Road 397 349 2.5 319 298 1.2 333 309 1.4

9732-1222 Glossop Road 310 329 1.1 280 315 2.0 271 309 2.2

1098-1108 Granville Road 246 263 1.1 180 236 3.9 284 318 2.0

1108-1098 Granville Road 809 808 0.0 493 502 0.4 654 700 1.8

1322-2074 Hanover Way 1004 1139 4.1 1171 1342 4.8 1416 1413 0.1

2075-1599 Hanover Way 1481 1871 9.5 1181 1407 6.3 1088 1315 6.5

1134-4008 Hoyle Street 1170 1047 3.7 1142 1000 4.3 1154 1164 0.3

1189-1135 Hoyle Street 1559 1446 2.9 1404 1209 5.4 1567 1411 4.0

1379-1456 Leveson Street 260 256 0.2 189 188 0.1 233 236 0.2

1456-1379 Leveson Street 347 347 0.0 264 256 0.5 354 340 0.7

1134-1201 Meadow Street 141 154 1.1 195 245 3.4 346 329 0.9

1201-1134 Meadow Street 307 287 1.2 125 114 1.0 145 167 1.8

9711-2005 Moorfields 802 755 1.7 803 734 2.5 1185 1222 1.1

9714-9725 Mowbary Street 2071 1700 8.6 1632 1605 0.7 2297 2073 4.8

9724-1168 Mowbray Street 1157 885 8.5 874 915 1.4 1026 836 6.2




Detector AM IP PM


9726-9832 Mowbray Street 1401 1195 5.7 1101 975 3.9 1720 1542 4.4

1135-1206 Netherthorpe Road 1220 1150 2.0 1344 1122 6.3 1460 1352 2.9




1116-1117 Park Square 116 139 2.0 147 161 1.1 150 154 0.4

1449-1195 Penistone Road 2117 2033 1.8 1435 1470 0.9 1426 1367 1.6

1190-1197 Shalesmoor 1154 1006 4.5 668 614 2.1 628 692 2.5

1197-1191 Shalesmoor 853 741 4.0 889 733 5.5 1365 1209 4.3

1172-7529 Pitsmoor Road 138 130 0.7 188 195 0.6 344 321 1.2

1107-1374 Queens Road 462 412 2.4 536 569 1.4 935 878 1.9

1374-1107 Queens Road 872 834 1.3 650 632 0.7 687 605 3.2

7528-7529 Rutland Road 376 286 4.9 310 279 1.8 457 372 4.2

7529-7528 Rutland Road 313 255 3.5 238 244 0.4 222 206 1.2

1324-2070 Saint Mary's Gate 2378 2495 2.4 1593 1830 5.7 1551 1745 4.8

1347-2091 Saint Mary's Gate 1448 1473 0.6 1784 1927 3.3 2323 2327 0.1

2071-1335 Saint Mary's Gate 1466 1452 0.4 1654 1781 3.0 1942 1906 0.8

1095-1362 Saint Mary's Road 1363 1680 8.1 1055 1201 4.3 1242 1553 8.3

1097-1094 Saint Mary's Road 2538 2542 0.1 1730 1900 4.0 1949 2202 5.5

1343-2087 Saint Mary's Road 1952 2237 6.2 1177 1325 4.2 1274 1495 6.0

1362-1343 Saint Mary's Road 2042 2242 4.3 1196 1325 3.6 1362 1536 4.6

2086-9769 Saint Mary's Road 1265 1485 5.9 1297 1413 3.1 1774 1807 0.8

2090-1331 Saint Mary's Road 2086 2308 4.7 1436 1615 4.6 1590 1806 5.2

9769-1362 Saint Mary's Road 1368 1475 2.8 1214 1381 4.6 1799 1806 0.1

2091-2092 Saint Mary's Road 2293 2530 4.8 2244 2500 5.3 2814 2926 2.1

1384-7592 Savile Street 638 515 5.1 464 542 3.5 665 626 1.5

8870-1385 Savile Street 673 566 4.3 405 378 1.4 661 553 4.4

1067-1432 Sheaf Street 1362 1378 0.4 1155 1280 3.6 1693 1823 3.1

1072-1082 Sheaf Street 1276 1329 1.5 1180 1289 3.1 1645 1849 4.9




Detector AM IP PM


1082-1072 Sheaf Street 1418 1246 4.7 1133 1127 0.2 1694 1504 4.7

1121-1067 Sheaf Street 1347 1351 0.1 879 1156 8.7 1631 1655 0.6

1431-1487 Sheaf Street 1417 1232 5.1 1134 1098 1.1 1610 1479 3.3

1487-1122 Sheaf Street 1545 1403 3.7 1293 1246 1.3 1833 1571 6.3

1124-1505 Sheffield Parkway 1519 1445 1.9 1156 1271 3.3 2109 2106 0.1






1362-5116 Shoreham Street 119 128 0.8 194 234 2.7 301 354 2.9

1362-8914 Shoreham Street 851 960 3.6 650 672 0.8 1002 946 1.8

5116-1362 Shoreham Street 594 617 1.0 152 175 1.8 133 133 0.0

8963-1082 Shoreham Street 1318 1390 2.0 1137 1197 1.8 1795 1643 3.7

1090-8402 Suffolk Road 1322 1510 5.0 1273 1437 4.5 1849 2054 4.6

1091-1101 Suffolk Road 1992 2031 0.9 1750 1967 5.0 2584 2870 5.5

7359-7370 Sutherland Road 54 64 1.4 71 66 0.5 120 136 1.4

7370-7359 Sutherland Road 132 119 1.2 69 81 1.4 113 123 1.0

1209-2093 Upper Hanover Street 1136 1457 8.9 1095 1270 5.1 994 996 0.1





1152-9832 Wicker 1727 1793 1.6 960 1048 2.8 1468 1406 1.6

Total GEH < 5.0 85% 87% 88%




Table B3: PM Peak Validation Results

Detector street name observed modelled GEH

1067-1432 Sheaf Street 1693 1904 4.96

1072-1082 Sheaf Street 1645 1907 6.22

1082-1072 Sheaf Street 1694 1558 3.37

1090-8402 Shuffolk Road 1849 2140 6.52

1091-1101 Shuffolk Road 2584 3012 8.09

1097-1094 Saint Mary's Road 1949 2357 8.80

1121-1067 Sheaf Street 1631 1760 3.14

1124-1505 park sq rdbt 2109 2349 5.09

1127-1131 Culters Gate 2556 2445 2.21

1129-1128 Culters Gate 1559 1550 0.24

1129-1131 Sheffield Parkway 1778 1654 2.98


1131-1132 Culters Gate 1101 1219 3.47

1132-1129 Culters Gate 1066 857 6.73


1135-1206 Netherthorpe Road 1460 1271 5.10

1138-1127 Culters Gate 2343 2451 2.21

1152-9832 Wicker 1468 1448 0.52

1184-9717 Corporation Street 1865 1809 1.31


1190-1197 Penniston Rd Rdbt 628 601 1.08

1196-1190 Penniston Road 837 654 6.70

1197-1191 Penniston Road 1365 1215 4.17



1209-2093 Upper Hanover Street 994 969 0.81







1322-2074 Hanover Way 1416 1453 0.98

1324-2070 Saint Mary's Gate 1551 1723 4.26

1347-2091 Saint Mary's Gate 2323 2256 1.40

1352-2085 Eyre Street 652 551 4.11

1362-1343 Saint Mary's Road 1362 1503 3.73

1362-8914 Shoreham Street 1002 1026 0.76

1431-1487 Sheaf Street 1610 1553 1.43

1487-1122 Sheaf Street 1833 1669 3.91

2005-9711 Gibraltar Street 692 598 3.70


2028-1137 Culters Gate 1649 1595 1.33


2069-1569 Culters Gate 2242 2307 1.37

2071-1335 Saint Mary's Gate 1942 1871 1.62

2075-1599 Hanover Way 1088 1102 0.41

2086-9769 Saint Mary's Road 1774 1865 2.14

2090-1331 Saint Mary's Road 1590 1769 4.37

2091-2092 Saint Mary's Road rdbt 2814 2834 0.38



4008-1188 Hoyle Street 1140 953 5.77

8402-1090 Shuffolk Road 128 63 6.65

8402-1091 Shuffolk Road 2099 2314 4.58

8963-1082 Shoreham Street 1795 1730 1.54

9711-2005 Moorfields 1185 1238 1.53

9711-9719 735 627 4.15


9719-9711 Gibraltar Street 945 1060 3.65

9724-1168 Mowbray Street 1026 1006 0.64

9726-9832 Mowbray Street 1720 1597 3.03






9769-1362 Saint Mary's Road 1799 1861 1.44

1108-1098 654 698 1.71

1183-1175 Burton Road 386 397 0.56

1201-1134 Meadow Street 145 163 1.45

1207-2098 Bolsover Street 346 355 0.48

1211-2099 Brook Hill 819 715 3.74

1320-2076 Moore Street 916 876 1.33

1374-1107 Queens Road 687 610 3.02

1377-1138 Furnival Road 312 325 0.71

1379-1456 Leveson Street 233 228 0.30

1449-1195 Peniston Road 1426 1311 3.10

1483-1346 Bramall Lane 685 735 1.88

1489-1117 Broad Street 366 338 1.49

1584-1095 Edmund Road 67 68 0.12

2035-2036 Broomspring Lane 41 58 2.42

2036-2035 Broomspring Lane 33 15 3.67

2037-1147 36 35 0.17


5116-1362 Shoreham Street 133 109 2.16

7370-7359 Sutherland Road 113 118 0.50

7390-7528 Burngreave Road 217 200 1.18

7529-7528 Rutland Road 222 190 2.25

8851-1456 Attercliffe Road 384 394 0.51

8870-1385 Savile Street 661 553 4.37

8872-7359 Carlise Street East 427 388 1.91

9732-1222 Glossop Road 271 305 2.01

1091-9210 Fornham Street 264 229 2.23

1095-1584 Edmund Road 26 48 3.62

1098-1108 284 306 1.28




1099-1375 550 551 0.06

1107-1374 935 882 1.75

1116-1117 150 152 0.20

1117-1489 160 152 0.63

1131-5012 3195 2878 5.75

1134-1201 346 324 1.20

1138-1377 Furnival Road 178 221 3.05

1147-2037 Castlegate 8 6 0.76

1172-7529 Pitsmoor Road 344 309 1.94

1175-1183 Burton Road 254 252 0.10

1212-8058 Brook Hill 1127 1108 0.56

1222-9732 Glossop Road 333 300 1.85

1362-5116 Shoreham Street 301 341 2.23

1384-7592 Savile Street 665 591 2.95

1456-1379 Leveson Street 354 338 0.84

1456-8851 Attercliffe Road 220 228 0.55

1501-1123 Park sq rdbt 538 460 3.49

2077-1592 Ecclesall Road 1044 969 2.36

2088-1484 Bramall Lane 1115 1068 1.42

2098-1207 Bolsover Street 587 512 3.18

7359-7370 Sutherland Road 120 133 1.18

7528-7390 Burngreave Road 296 255 2.48

7528-7529 Rutland Road 457 387 3.42

GEH < 5 87.61%

GEH < 10 99.12%

Sheffield City Council SyITS Microsimulation Framework Lower Don Valley Model Update/ BRT North Black

Sheffield City Council SyITS Microsimulation Framework Lower Don Valley Model Update/ BRT North

October 2010

This report takes into account the

particular instructions and requirements

of our client.

It is not intended for and should not be

relied upon by any third party and no

responsibility is undertaken to any third

party Ove Arup & Partners Ltd

Admiral House, Rose Wharf, 78 East Street, Leeds LS9 8EE

Tel +44 (0)113 2428498 Fax +44 (0)113 2428573 www.arup.com

Job number 207585-09

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Sheffield City Council SyITS Microsimulation FrameworkLower Don Valley Model Update/ BRT North


Ove Arup & Partners Ltd Issue 13 October 2010

Contents

Page

1 Introduction 1

1.1 Background 1

1.2 Objectives 1

1.3 Report Structure 1

2 Model Network Update 2

2.1 Network 2

2.2 Network Modifications 3

2.3 Modifications to Centroids 3

2.4 Traffic Demand 3

2.5 Modelled Base Year and Time Periods 3

2.6 Traffic Signals 4

3 Model Calibration 5

3.1 Introduction 5

3.2 Software Version 5

3.3 Calibration of Route Choice Models 5

3.4 Section Characteristics 10

3.5 Vehicle Characteristics 11

3.6 Simulation Step and Reaction Time 11

3.7 Behavioural Models 11

3.8 Demand Flow Calibration 12

4 Validation 19

4.1 General 19

4.2 Journey Time Validation 19

4.3 Bus Journey Time Data 22

4.4 Queue Length Validation 22

5 Summary 23

Appendices

Appendix A

Matrix Calibration

Appendix B

Calibration Results



Page 1 Ove Arup & Partners LtdIssue 13 October 2010

1 Introduction

1.1 Background

Ove Arup & Partners Limited (Arup) has been commissioned by Sheffield City Council

(SCC) under the syITS Microsimulation framework to update and calibrate the existing Base

2008 microsimulation traffic model for the Lower Don Valley.

In 2008, Arup was commissioned by Sheffield City Council (SCC) to develop a

microsimulation traffic model to assist the City Council in identifying the traffic impact of a

proposed development in the Lower Don Valley.

The matrices used in the 2008 base year Aimsun model (developed in 2008) were created

by cordoning the 2007 Sheffield and Rotherham Transport Model (SRTM), and then uplifted

using Tempro.

The SRTM has now been updated (now known as SRTM3) and Arup has therefore been

commissioned to update, calibrate and validate the Lower Don Valley Aimsun model in line

with the current highway layout, signal timings and traffic management measures and with

demand that is consistent with SRTM3.

The model will then be used to inform the design of, and obtain journey times for, the

proposed Northern Bus Rapid Transit (BRT) proposals.

1.2 Objectives

The key objective of this study was to update and validate the base year 2008

microsimulation traffic model of the Lower Don Valley for the AM peak, PM peak and an

average inter-peak period.

1.3 Report Structure

The report sets out the stages of updating the Aimsun microsimulation model, and explains

the process undertaken in the calibration and validation of the model.

Section 2 describes the model extent and the data sources used. Section 3 explains the

calibration of the microsimulation model and the validation is detailed in Section 4. The

conclusions of the report are summarised in Section 5.




2 Model Network Update

2.1 Network

The extent of the updated Base Model remains the same, running from Spital Hill to M1

Junction 34 at Tinsley and including the following three radial routes:

• Carlisle Street/ Holywell Road;

• Savile Street/ Brightside Lane;

• Attercliffe Road/ Sheffield Road.

Figure 1 shows the study area.

Figure 1: Extent of the Lower Don Valley Aimsun Model




2.2 Network Modifications

A number of modifications have been made to the network, in order that the base model has

sufficient coverage for future testing of the BRT proposals. Changes have also been made

to be consistent with the SRTM3 output matrices. These changes include:

• Linking Sheffield Road and Bawtry Road with St. Lawrence Road;

• Adding in a new tram route from Meadowhall running parallel to Attercliffe Common;

• Adding centroid 479 which was not in the previous Aimsun model, but was included with

the latest Saturn matrices; and

• Network modifications around centroids 627 and 629.

2.3 Modifications to Centroids

Due to the cordoning process in Saturn, the centroid numbers have changed from the

previous model and these have therefore been updated in the Aimsun model.

2.4 Traffic Demand

The updated base year origin-destination (O-D) data was obtained from SRTM3 and input to

the Aimsun model. The STRM3 cordon was created by MVA, who provided 15 output

matrices (five vehicle types/ user classes, for the three time periods).

The Aimsun traffic demand is split into three vehicle types – private cars, light goods

vehicles (LGVs) and other goods vehicles (OGVs). The SRTM3 output matrices were

therefore factored from passenger car units (pcus) to vehicles per hour (vph) for each of the

three vehicle types as follows:

• Private cars – these are equivalent to the sum of user classes UC1, UC2 and UC3 in

SRTM3, assuming that one private car is equivalent to one pcu;

• LGVs – these are the equivalent of user class UC4 in the SRTM3, assuming that one

LGV is equivalent to one pcu;

• OGVs - these are the equivalent of user class UC5 in the SRTM3, assuming that one

OGV is equivalent to 1.7 pcu;

2.5 Modelled Base Year and Time Periods

The Aimsun base model year (2008) and time periods are consistent with the SRTM3.

These time periods are as follows:

• AM peak (0800 to 0900 hours);

• Inter peak (an average typical off-peak hour from the time period 1000 to 1200 hours

and 1400 to 1600 hours);

• PM peak (1700 to 1800 hours).

To represent the build-up of traffic during the modelled hours, the traffic demand has been

profiled based on the profile set out in Table 1 below.

.




Table 1: Demand Profile

Peak Period Proportion of Demand in 15-minute Segment

xx:00 xx:15 xx:30 xx:45

AM Peak 24% 26% 26% 24%

Inter Peak 25% 25% 25% 25%

PM Peak 24% 26% 26% 24%

2.6 Traffic Signals

The existing Aimsun model was assumed to have up-to-date traffic signal timings. However,

during the calibration process, traffic signal timings that appeared incorrect were reviewed

and updated, as necessary. In particular, traffic signal timings were updated at the following

junctions:

• A6109 Fred Mulley Road / B6080 Sutherland Street;

• A6178 Attercliffe Road / Sutherland Street / Leeveson Street;

• A6109 Fred Mulley Road / A6178 Attercliffe Road;

• A6178 Attercliffe Road / Staniforth Road.

At all of the above junctions, the traffic signal timings within the original model were not

sufficient to accommodate the traffic flows that had been surveyed on-street. These traffic

signal timings were therefore checked with Sheffield Urban Traffic Control, who provided

corrected signal staging and timings for inclusion in the model.




3 Model Calibration

3.1 Introduction

3.1.1 Introduction

Model calibration is the process of adjusting the parameters of the model to ensure that

simulated traffic flows, routes and travel behaviour correspond with observed behaviour. A

number of features within the Aimsun models were calibrated to ensure the best

representation of the network and traffic demand. The calibration parameters in the model

include:

• Mesoscopic route choice model;

• Microscopic route choice model;

• Link characteristics;

• Vehicle characteristics;

• Simulation step and reaction time;

• Behavioural Models.

The calibration of the mesoscopic and microscopic models is discussed in detail in the

following sections.

3.2 Software Version

The updated 2009 Lower Don Valley Aimsun Base Model has been calibrated and validated

in the Aimsun 6.1.2 environment, at a dynamic Mesoscopic and Microscopic simulation

level.

3.3 Calibration of Route Choice Models

3.3.1 Cost Function

Vehicles are assigned through the network based on the relative costs of each of the

possible routes between each origin and destination. These costs are determined using the

cost function, which in this model is the default function. The default cost function uses link

capacity, travel times and user defined costs to determine the costs on each link in the

network and is as follows:

IniCost�,� � TravelTFF�,� � TravelTFF�,� � φ�1 � CL� CL��⁄ � � τ � UserDefCost$

Where IniCostj,vt is the initial cost of link j per vehicle type vt;

TravelTFFj,vt is the estimated travel time of vehicle type vt in link j in free flow

conditions;

φ is the user-defined capacity weighting;

CLmax is the estimated maximum link capacity in the network;

τ is the user-defined cost parameter weighting;

UserDefCosts is the user-defined cost of section s which can be applied

independently per section.

The weighting applied to the link capacity and user defined costs can be altered, which in

turn will affect link costs and hence the path assignment. It is the process of adjusting these

parameters that is required to calibrate the cost function.




3.3.2 Road Hierarchy

Section capacities and user defined costs need to be correctly defined, as these are used

within the cost function and ultimately within the route choice model. To achieve this, a road

hierarchy was used within the model which was based on link capacities set out in the

Design Manual for Roads and Bridges (DMRB)1. The link capacities (in vehicles per hour)

for different widths of the road used in the model are shown in Table 2.

Table 2: Link Capacities – One way hourly flows in each direction

Two-way Single Carriageway Dual Carriageway

Total number of lanes Number of lanes in each

direction

2 2-3 3 2 3 4

Road

Speed

Limit

Carriageway

width 6.1m 6.75m 7.3m 9.0m 10.0m 12.3m 6.75m 7.3m 11.0m 14.6m

40 mph 1,020 1,260 1,470 1,550 1,650 1,700 2,950 3,200 4,800 *

30 mph 750 900 1,140 1,320 1,410 * * * * *

Source: Table 2, TA 79/99, DMRB

3.3.3 Mesoscopic Dynamic User Equilibrium

The mesoscopic simulator has been used to determine a Dynamic User Equilibrium (DUE)

in terms of route choice. In static user equilibrium, the journey times on all the routes

actually used are equal, and less than those which would be experienced by a single vehicle

on any unused route. In a DUE, the journey paths are able to change over time to react to

changes in levels of congestion, but, at any point in time, a user equilibrium is established.

In Aimsun, DUE is achieved through an iterative process by the application of a modified

version of the Method of Successive Averages (MSA). In each iteration, the solution should

converge towards an equilibrium solution. The measure used to define how close the

modelled traffic flows are to a user equilibrium is the Relative Gap, which is the ratio of the

total excess cost with respect to the total minimum cost if all travellers had used shortest

paths. The stopping criteria for the updated Lower Don Valley Aimsun model is a relative

gap of 0.5%.

3.3.4 Final DUE Parameter Settings

Table 3.1 illustrates the final values for the user defined settings in the mesoscopic DUE model for the updated Lower Don Valley Aimsun model and

Figure 2 to

1 ‘Determination of Urban Road Capacity’, TA 79/99, Design Manual for Roads and Bridges,




Figure 4 show the DUE convergence for each of the three time periods.

Table 3: Mesoscopic DUE settings

AM IP PM

Meso interval 1 hour 1 hour 1 hour

Meso output statistics 1 hour 1 hour 1 hour

Meso cycle time 15 mins 15 mins 15 mins



Number of iterations at stopping 19 13 25

Figure 2: AM Peak DUE Convergence

Figure 3: Inter-peak DUE Convergence




Figure 4: PM Peak DUE Convergence

The above graphs show that each DUE assignment during each time period has converged

to an acceptable level.

The path assignments for each of the three time periods have been output to a file for use in

the microscopic simulation.

3.3.5 Microscopic Model Route Choice Model

The microscopic model uses stochastic dynamic traffic assignment to determine the path a

vehicle will take between a given origin and destination from a set of alternative routes. In a

stochastic model, the probability of a vehicle taking a particular route depends on the cost of

that route relative to the costs of the alternative route(s). The costs are determined by the

cost function and the probabilities are determined by the route choice model. The route

choice model defines the drivers’ decision of which path to take from a set of alternatives,

connecting one origin to one destination, depending on the cost calculation by the cost

function. The ‘standard’ route choice models within Aimsun include:

• Fixed (time);

• Binomial;

• Proportional;

• Logit;

• C-Logit.

This study uses the C-Logit model, as the network includes many alternative and

overlapping paths between origins and destination. There are global parameters within the

C-Logit model, which require calibration as follows:

• Cycle time: this is the length of the period after which the route choice paths and

probabilities are recalculated;

• Number of intervals: this is the number of preceding cycles that are used to calculate

the route choice paths in the next route choice cycle

• Initial K-SPs: the number of route choice paths used at the beginning of the simulation;




• Maximum number of routes: the maximum number of routes for each O-D pair to

which vehicles are assigned;

• Scale factor, θ: this influences the standard error of the distribution of expected travel

times and effectively determines the weight given to differences in costs between

routes. For a small value of the scale factor (θ < 1), there is a large variability about the

true route costs and hence a trend towards using many routes whereas for large values

of the scale factor (θ > 1) there is a small variability about the true route costs and route

choice is concentrated in very few routes;

• Beta factor, β: this affects the likelihood of drivers choosing non-overlapped longer

paths over heavy overlapped shorter paths. The higher the beta factor is, the more likely

the drivers choose non-overlapped longer paths;

• Gamma factor, γ: this is similar to β but has the opposite effect and exerts less

influence on the route choice;

• Attractiveness weight: this is the weighting afforded to the capacity when the route

costs are calculated by the cost function;

• User defined cost weight: this is the weighting afforded to the user defined costs when

the route costs are calculated by the cost function.

These parameters were calibrated as shown in Table 4 below.

Table 4: C-Logit Calibrated Parameter Values

C-Logit Model Parameter Final Calibrated Value

Cycle time 5 minutes

Initial K-SPs 1

Maximum number of paths 3

Scale factor, θ 17

Beta factor, β 1

Gamma factor, γ 1

3.3.6 Microscopic Cost Function

The calibrated parameter values used in the cost function for the microscopic route choice

model are as shown in Table 5. These values were updated such that they are consistent

with the updated Sheffield City Centre Aimsun model, which operates with a reduced cycle

time and a higher proportion of vehicles following the path assignment results than previous

versions of the model.

Table 5: Cost Calibrated Parameter Values

AM IP PM

Cycle 5 mins 5 mins 5 mins


User Defined Cost Weight 0.5 1 1




3.3.7 Use of Mesoscopic DUE Paths

In order to produce realistic route choice within the microsimulation model, 85% of the

vehicles are assigned to follow DUE paths from the mesoscopic model. These represent

the majority of drivers who habitually follow the same paths regardless of varying congestion

on the network. The remaining 15% of vehicles are assigned to follow the DTA paths,

representing those drivers to react to congestion or incidents as they occur on the network.

3.4 Section Characteristics

There are a number of section characteristics that can be calibrated in the Aimsin model as

follows:

• Link Speed: This gives the maximum speed that vehicles travel on the section;

• Visibility distance: This is distance from the end of the link where vehicles begin to

apply the gap acceptance model and is used to control calibrate capacity of priority

junctions.

• Maximum Give-Way Time Variation: The give-way time is the maximum amount of

time a driver is prepared to wait before reducing their safety margin from two to one

simulation steps in the gap acceptance model. The maximum give-way time variation

parameter locally varies this time on the section and was used in the model to calibrate

the aggressiveness of vehicles at some priority junctions;

• Yellow Box Speed: The yellow box speed prohibits a vehicle from entering the junction

area (which is designated as a yellow box) should the preceding vehicle leaving be

travelling at a speed lower than the specified value set. This facility can be used to

model yellow boxes that are marked on-street. However, it is also used to simulate the

effect of slow moving traffic on the main road allowing traffic to emerge from a minor

side road access, to avoid gridlock which often occurs in many microsimulation models,

and to adjust the relative capacity of approaches. The yellow box speed can also be set

by turning movement;

• Distance Zones 1 and 2: Sections are split into three zones labelled Zone 1, 2 and 3.

In Zone 1, lane-changing decisions are mainly governed by the traffic conditions of the

lanes involved and the next desired turning movement is not taken into account. In Zone

2, it is the desired turning lane that affects the lane-changing decision. Vehicles not

driving in the correct lane for the next turn tend to move towards the correct lane.

Vehicles looking for a gap may try to adapt to it, but do not affect the behaviour of

vehicles in the adjacent lanes. In Zone 3, vehicles are forced to reach the correct lane,

reducing speed if necessary, and even coming to a complete stop in order to make the

lane change possible. Also, vehicles in the adjacent lane can modify their behaviour in

order to provide a gap big enough for the vehicle to change lanes.

The “Distance Zone 1” and “Distance Zone 2” parameters determine the locations of

Zones 1, 2 and 3 and therefore affect how the lane changing model is applied in

different sections of the network. The distance is specified as a time, which is converted

into length using a function of the section speed limit and the desired speed of each

vehicle using the section. Using time makes the variable distance dependent on the

vehicle characteristics.

These parameters were carefully set on the approach to junctions to obtain realistic

lane-changing and diverging behaviour in the model.




3.5 Vehicle Characteristics

There are several vehicle characteristics specified in the model. The mean, standard

deviation, maximum and minimum values, as well as types and limits of distribution are

carefully defined. The characteristics can be broadly split into two categories, vehicle

properties, and driver characteristics. Vehicle properties include size, maximum speed and

maximum acceleration and driver characteristics include speed acceptance, minimum

distance between vehicles and maximum give way time. Most of the values used in the

model were the default Aimsun values. However, the maximum give-way time parameters

for HGVs and buses were reduced to make these vehicles exhibit more aggressive

behaviour in give-way situations.

3.6 Simulation Step and Reaction Time

The simulation step is a global parameter which defines the time it takes a driver to react to

speed changes in the preceding vehicle, and this will influence the capacity at signal

controlled junctions. The parameter can be either fixed (for all vehicle types) or variable (a

discrete probability function is defined for each vehicle type). The parameter was sensitivity

tested in the calibration process. In the model, the parameter was fixed at final values of 0.7

seconds for all vehicle types. Equal values were also used for the reaction time at stop

(which determines how quickly a vehicle reacts from a complete stop) and reaction time at

traffic light (which determines how quickly the vehicle at the head of the queue at a traffic

signal reacts to the changing signals) parameters.

3.7 Behavioural Models

3.7.1.1 Car Following and Lane Change Models

Both car following and lane changing models have global parameters for which it is possible

to alter the default settings. The Deceleration estimation (sensitivity factor) car following

model was used in North Wakefield Gateway model. The lane changing model is a decision

process and the factors of the model include percent overtake and percent recover. In the

model, none of the values were changed from these default settings, which are shown in

Table 6.

Table 6: Car Following and Lane Changing Model Parameters

Percentage Overtake Percentage Recover

90% 95%

3.7.1.2 Look Ahead Model

The look ahead model determines how many turnings ahead of a given turning vehicles

have knowledge of that turning and consequently affects lane usage and lane changing

behaviour. The look head model can be used to ensure that most vehicles move into the

correct lane for a particular manoeuvre several junctions ahead of that manoeuvre and can

be useful in modelling the uneven lane usage that occurs in reality. The model relies on the

correct specification of Zones 1, 2 and 3 in relation to lane changing behaviour. Careful

specification of these parameters, together with the look ahead model were used to obtain

the correct queuing behaviour on the approach to junctions in the model. The look ahead

model was set to consider 10 turnings.

3.7.1.3 Trip Generation

When loading a traffic demand into the simulation model a number of different models can

be used to determine the headway between two consecutive vehicle arrivals. Five types of

traffic generation are available in Aimsun: exponential uniform, normal, constant and ASAP.

Figure 5 illustrates the trip generation profile for each type of distribution. Clearly, the ASAP

distribution is not appropriate for this model and was therefore discounted. Sensitivity




testing of the other distributions was undertaken to determine which best reflected reality. It

was found that the exponential distribution gave the most realistic results, as it allows for

more variation in arrivals and also accounts for some platooning of arrival traffic. This

distribution has therefore been used in the model.

Figure 5: Trip Generation Comparison

3.8 Demand Flow Calibration

Calibration of the demand data (matrices) formed a substantial part of the overall calibration

process. The modelled flow data were collected by the detectors and compared with the

observed data. The majority of the observed data covered the year 2008 and were from the

same dataset that was used to calibrate and validate the Sheffield and Rotherham

Transport Model version 3 (SRTM3).

In addition, average weekday peak hour traffic data has also been obtained for 12

automated traffic counts in the vicinity of M1 Junction 34 from the Highways Agency’s Traffic

Flow Data System (TRADS). A full year’s worth of data for 2008 were available for some

locations. In those areas where the 2008 data are not available, 2009 or 2010 data has

been used as a proxy.

The GEH statistic has been used to compare observed and modelled traffic flows. It is a

self-scaling formula, mathematically similar to the chi-squared test, which reduces the

issues that occur when using a simple percentage comparison. The criteria recommended

in Design Manual for Roads and Bridges (DMRB) Volume 12 for a model to be calibrated /

validated is that 85% of link flows should have a GEH statistic of less than 5.

The locations of the detectors used to calibrate the model are shown in Figure 6.




Figure 6: Location of Calibration Data

3.8.1 Before Calibration

Table 7 below shows the percentage of links with a GEH statistic of less than 5.0 for each of

the peak periods modelled before link flow calibration. The table shows that the initial

modelled results varied greatly from the observed data and were significantly below the DfT

criteria. Thus, demand data calibration was necessary.

Table 7: Initial Results Before Calibration

Criteria / Target

Weekday Modelled Hour

AM IP PM

GEH<5 71% 72% 71%

3.8.2 Internal Demand Calibration

Occasionally, individual cell values (or full row or column totals) for the internal centroids

were amended to match the observed flows for these links. Details of these adjustments,

along with the justification, are presented at Appendix A.

3.8.3 Total Demand Before and After Calibration

The total demand in the matrices before and after the model calibration is shown in Table 8.

The tables show that the demand data calibration process has slightly changed the overall

demand, which is primarily associated with reducing cordon entry flows to match both

observed traffic counts and queuing behaviour.




Table 8: Total Demand in Model


AM Peak

Before Calibration 16,347 2,859 1,978

After Calibration 17,153 2,859 2,070

Inter Peak

Before Calibration 14,330 3,263 2,448


PM Peak

Before Calibration 21,534 2,397 975


The full results of the calibration can be found in Tables B1, B2 and B3 in Appendix B. In

summary, the three time periods were shown to calibrate to a high degree of accuracy,

meeting the selected DMRB criteria and targets as mentioned previously. A summary of

these results is provided in Table 9 and Figure 7 to Figure 9.

Table 9: Validation Summary Results

Criteria / Target Weekday Modelled Hour

AM IP PM

GEH<5 89% 86% 85%

Figure 7: Model Validation – AM Peak Hour




Figure 8: Model Validation – IP Peak Hour




Figure 9: Model Validation – PM Peak Hour

3.8.4 Regression Analysis

As well as considering the GEH statistic, TA 79/99 recommends the use of regression

analysis to compare how well the observed and modelled data are correlated. The

regression analysis calculates the correlation coefficient (R), which can be used to measure

the goodness of model fit. A correlation coefficient of 1.0 would denote a perfect fit and

DMRB advises that the correlation co-efficient should be greater than 0.95.

Figure 10 to




Figure 12 illustrate the regression lines in AM peak, inter peak and PM peak respectively

and Table 10 summarises the values of the correlation coefficient, R. The table shows that

the calibration links in all three periods have a correlation co-efficient that exceeds the

DMRB guidance for validation.

Table 10: Correlation Coefficient

AM IP PM

Correlation

coefficient, R 0.984 0.963 0.981




Figure 10: AM Peak Regression Line

Figure 11: Inter Peak Regression Line




Figure 12: PM Peak Regression Line




4 Validation

4.1 General

The validation process determines whether the simulated model is an accurate

representation of the observed situation by comparing modelled output data with observed

data. The validation results are an average of ten model runs for each modelled period, as

each model replication is unique.

The following data was used to validate the model:

• Journey time data;

• Bus Journey time data;

• Queue lengths observations.

4.2 Journey Time Validation

Modelled journey times have been based on the DMRB criteria, indicating that modelled

journey times should be within 15% or 1 minute (if higher) of observed journey times.

ANPR journey time data collected in September 2008 was compared to the modelled

journey time data. The range and the mean of the observed and modelled data are plotted

on graphs corresponding to each time period. The results are shown in the following

sections.

4.2.1 AM Peak Journey Time Validation

Figure 13 provides a comparison of the modelled and observed results for the AM peak

hour. The observed journeys times demonstrate a wide range of journey times for each

section, ranging from around 7 minutes (section 2) to in excess of 13 minutes (for section 4).

Although the model does not fully reflect the relatively wide range of observed journey

times, the range of modelled journey times are within the observed data.

Figure 13: AM Journey Time Comparison




4.2.2 Inter Peak Journey Time Validation

Figure 14 provides a comparison of the modelled and observed results for the inter peak

period. The modelled inter peak hour represents an average demand, and as such, does

not correspond to a particular hour. In comparison, the observed data demonstrates a

significant degree of variation, particularly for routes 1, 3 and 4. In each case, the mean

modelled journey times fall within the range of the observed data, although with significantly

less variation.

Whilst there is considerable overlap between the observed and modelled journey times for

route 2, the mean modelled journey time is higher than the maximum observed journey

time. This would suggest that there is more congestion in this section of the model than

observed, although the animated model runs do not show any particular congestion issues

on this route. An alternative explanation could be that vehicles exceed the speed limit on

this route during the interpeak period to a greater extent than predicted by the model.

Figure 14: IP Journey Time Comparison




4.2.3 PM Peak Journey Time Validation

Figure 15 provides a comparison of the modelled and observed results for the PM peak

period. All of the mean modelled journey times fall within the range and are similar to the

means of the observed data. In some cases, the model does not fully reflect the relatively

wide range of observed journey times, however, the range of modelled journey times are

generally within the observed data.

Figure 15: PM Journey Time Comparison




4.3 Bus Journey Time Data

The updated model will be used to test proposals for the Northern BRT system and it is

therefore important that the model accurately predicts public transport journey times.

Modelled bus journey times for service number 69 were therefore compared with the

observed bus journey times, from Sheffield City Council’s Annual Journey Time Survey.

The observed bus journey times were recorded timing point to timing point travel times and

dwell time at stops in both peak periods across three days (11 to 13 March 2008). The

results are shown in Figure 16.

Figure 16: Bus Journey Time Comparison

The analysis indicates that the modelled bus journey times compare well with the observed

journey times in both the AM and PM peaks. In each case, the modelled mean journey time

is within 15% of the observed value, meeting the DMRB validation criteria.

4.4 Queue Length Validation

No surveyed queue length data is available, but the queue length validation was undertaken

by eye by the members of the SCC team using their extensive knowledge of the observed

situation. This formed a valuable part of the model validation process. Generally, the

location and length of queues are considered reasonable.

0

200

400

600

800

1,000

1,200

To Shef'd Modelled

To Shef'd Observed

To Rot'ham Modelled

To Rot'ham Observed

To Shef'd Modelled

To Shef'd Observed

To Rot'ham Modelled

To Rot'ham Observed

AM PM

Mean




5 Summary

This report outlines the data update, calibration and validation of the microsimulation model

of the Lower Don Valley, Sheffield. The microsimulation model matrices were extracted from

SRTM3 by applying the cordoning technique.

The model has been built and validated in three time periods, AM peak (0800 to 0900

hours), inter peak (averaged from the time period 1000 to 1200 hours and 1400 to 1600

hours), and PM peak (1700 to 1800 hours).

The model was calibrated by adjusting the global and local parameters to ensure the model

represents the network and the traffic demand. The cost function and the route choice

models were also carefully calibrated. The model was also calibrated by comparing the

modelled and observed link flows at number of locations using the GEH statistic. In all time

periods, more than 85% of validation links have a GEH statistic of less than 5. Regression

analysis has also been undertaken to demonstrate that correlation coefficients for the

calibration links are above the value of 0.95 required by DMRB for each time period.

The model has been validated on the basis of comparing modelled and observed journey

times. In addition, the assignment and queue lengths were also validated by both members

of the Arup and SCC teams who have local knowledge of the Lower Don Valley area and

the final base model is considered an acceptable representation of the observed traffic

conditions during the three modelled time periods.

Validation of journey time data for general traffic shows that the model produces reasonable

estimates of vehicle journey times, but does not always produce the full journey time

variability observed in practice, particularly during the inter peak period.

Appendix A

Matrix Calibration



Page A1 Ove Arup & Partners LtdIssue 13 October 2010

Table A1: AM Peak Internal Matrices Demand Data Calibration

Centroid Adjustment Reason

From To Original Adjustment Adjusted

219 240 0 100 100 Match demand to the observed data





603 604 226 -100 126 Match demand to the observed data





















Page A2 Ove Arup & Partners Ltd Issue 13 October 2010

Table A2: Inter Peak Internal Matrices Demand Data Calibration
























Page A3 Ove Arup & Partners LtdIssue 13 October 2010

Table A3: PM Peak Internal Matrices Demand Data Calibration
























Appendix B

Calibration Results



Page B1 Ove Arup & Partners LtdIssue 13 October 2010

Table B1: AM Peak Link Flow Calibration Results

Detector Observed

Flow Modelled

Flow Absolute Difference

GEH

7804-7536-0 26 7 19 4.73

7536-7804-0 29 10 19 4.22

7593-7222-0 67 157 -90 8.49

9355-7118-0 65 131 -66 6.70

7382-7223-0 268 433 -164 8.78

8851-1456-0 272 385 -112 6.20

7134-7593-0 434 597 -163 7.17

9354-7222-0 280 206 74 4.76

1384-7592-0 857 638 220 8.03

8828-7382-0 390 293 97 5.26

1167-1168-0 75 57 18 2.27

7729-7428-0 364 275 89 4.98

1384-1385-0 522 690 -168 6.81

1384-7359-0 229 289 -61 3.76

7222-9354-0 120 151 -31 2.68

7235-8036-0 438 552 -114 5.11

7593-7134-0 376 471 -95 4.61

7119-7118-0 341 427 -86 4.37

7340-7348-0 282 226 56 3.49

9445-7359-0 181 145 35 2.74

7428-7729-0 412 500 -88 4.10

7359-7370-0 65 54 11 1.43

7370-7359-0 157 132 25 2.10

7118-7579-0 403 478 -75 3.58

1168-1170-0 382 323 59 3.15

7118-7119-0 435 514 -79 3.60

8870-1385-0 792 673 119 4.40

7224-7222-0 1,198 1,019 179 5.38

7579-7118-0 250 294 -43 2.63

7359-8068-0 224 263 -39 2.48

32: 8034-71 664 778 -114 4.24

7382-8828-0 394 460 -66 3.19

7222-7223-0 940 806 134 4.54

7121-7120-0 635 739 -104 3.96

7134-8034-0 429 488 -60 2.80

7306-7649-0 541 477 64 2.85

1456-1382-0 559 628 -69 2.82

8864-7133-0 693 778 -85 3.12

7382-7383-0 406 364 42 2.16

7222-7221-0 1,167 1,047 121 3.63

7549-7293-0 577 638 -61 2.49

1379-1456-0 286 260 27 1.62

7209-7103-0 386 426 -39 1.96

7803-7133-0 349 316 32 1.77

7223-7382-0 362 398 -36 1.87

1168-1153-0 477 525 -48 2.13



Page B2 Ove Arup & Partners Ltd Issue 13 October 2010

7221-7222-0 683 621 62 2.43

8068-7359-0 281 308 -27 1.54

7118-9355-0 259 282 -23 1.41

7269-9245-0 2,095 1,924 171 3.82

TRADS (7131) 3,196 2,937 259 4.68

7359-8872-0 256 279 -23 1.38

RADS (30022 2,674 2,904 -230 4.35

7708-7178-0 1,991 1,853 138 3.14

7103-7210-0 594 635 -42 1.68

7649-7306-0 708 662 45 1.74

7215-7590-0 869 928 -59 1.98

87: 7118-71 114 107 7 0.67

7117-7118-0 563 530 34 1.45

7589-7214-0 559 526 33 1.42

7302-7294-0 1,888 2,004 -117 2.64

7175-7179-0 1,298 1,223 75 2.11

8977-1168-0 666 707 -40 1.54

TRADS (30022903) 3,197 3,021 176 3.15

1170-1168-0 467 442 25 1.16

1385-1382-0 637 604 33 1.31

1456-1379-0 364 347 17 0.93

7133-7803-0 292 280 12 0.73

TRADS (30022633) 2,675 2,787 -112 2.15

TRADS (30022255) 3,204 3,076 128 2.28

1168-9724-0 624 650 -26 1.02

-1168-0 (V) 1,112 1,157 -46 1.35

TRADS (30021294) 3,203 3,078 125 2.23

7349-7341-0 1,514 1,457 57 1.47

-7382-0 (V) 493 511 -19 0.83

TRADS (30022246) 2,674 2,772 -98 1.87

1383-1384-0 1,136 1,176 -40 1.18

7120-7121-0 364 352 11 0.60

8036-7235-0 1,026 1,054 -28 0.88

7134-7133-0 967 993 -26 0.83

-7359-0 (V) 426 436 -10 0.48

TRADS (30021204) 1,887 1,848 39 0.90

7293-7549-0 450 441 9 0.43

7292-7301-0 728 714 15 0.55

84: 1456-88 313 319 -6 0.35

TRADS (30021279) 728 715 13 0.49

7359-1384-0 439 434 4 0.21

7267-7268-0 773 780 -7 0.25

70: TRADS (7130) 2,677 2,698 -22 0.41

7133-7134-0 939 931 7 0.24

7359-9445-0 288 289 -1 0.08




31: 7133-88 774 775 -1 0.04

1168-8977-0 824 824 0 0.00

Mean GEH 2.68

Number %

Total GEH < 5 83 89%

Total GEH < 10 93 100%

Total 93




Table B2: Inter Peak Link Flow Calibration Results

Detector Observed

Flow Modelled


GEH

9355-7118-0 25 127 -102 11.64

7804-7536-0 10 21 -11 2.88

7222-9354-0 57 102 -44 4.97

7382-7223-0 186 322 -135 8.50

7593-7222-0 64 104 -41 4.43

TRADS (30021293) 589 936 -347 12.55

7383-7382-0 266 410 -144 7.83

1384-7592-0 709 464 245 10.10

7359-8872-0 166 248 -82 5.72

8828-7382-0 248 346 -98 5.71

1384-7359-0 185 254 -69 4.66

TRADS (30021279) 790 1,063 -273 8.96

7593-7134-0 332 442 -111 5.63

8851-1456-0 244 319 -75 4.50

7536-7804-0 18 14 4 1.07

7221-7222-0 718 555 163 6.47

TRADS (30021205) 863 1,114 -251 7.98

7359-8068-0 177 225 -48 3.39

7222-7223-0 900 709 191 6.74

7359-9445-0 141 176 -35 2.80

9354-7222-0 212 173 40 2.86

7370-7359-0 84 69 15 1.77

7382-7383-0 294 356 -63 3.47

8870-1385-0 491 405 86 4.04

7428-7729-0 303 364 -61 3.37

8068-7359-0 210 252 -41 2.72

1168-8977-0 668 559 108 4.37

32: 8034-71 523 622 -99 4.13

1167-1168-0 75 89 -14 1.52

8036-7235-0 559 657 -99 4.01

1168-1170-0 281 329 -49 2.80

7223-7382-0 252 296 -43 2.61

7118-7579-0 314 367 -54 2.91

7359-7370-0 82 71 12 1.32

TRADS (30021204) 1,208 1,039 169 5.03

-7133-0 (V) 306 264 42 2.49

1385-1382-0 423 366 57 2.85

7121-7120-0 369 426 -57 2.86

7134-8034-0 496 570 -74 3.19

7589-7214-0 527 605 -78 3.26

7118-7119-0 460 527 -67 3.00

7103-7210-0 244 279 -34 2.12

7120-7121-0 511 449 62 2.84

1456-1382-0 431 483 -52 2.43




7267-7268-0 1,399 1,250 148 4.08

7359-1384-0 350 314 36 1.99

7235-8036-0 655 728 -73 2.79

7649-7306-0 561 506 55 2.39

7215-7590-0 568 514 54 2.31

7579-7118-0 324 358 -34 1.81

87: 7118-71 99 90 9 0.91

9445-7359-0 136 149 -13 1.10

7729-7428-0 331 363 -32 1.72

7209-7103-0 251 274 -23 1.44

7134-7593-0 357 389 -32 1.64

7117-7118-0 367 337 30 1.58

8864-7133-0 628 682 -55 2.14

7222-7221-0 520 480 40 1.77

1384-1385-0 469 505 -36 1.65

7549-7293-0 413 383 30 1.48

7224-7222-0 489 457 32 1.48

7269-9245-0 1,362 1,279 82 2.27

7134-7133-0 785 834 -50 1.74

7175-7179-0 1,299 1,379 -81 2.20

1170-1168-0 380 358 22 1.15

1168-1153-0 457 432 25 1.18

7118-9355-0 197 186 11 0.77

-1384-0 (V) 961 910 51 1.66

8977-1168-0 614 583 31 1.26

7293-7549-0 396 381 15 0.76

TRADS (30022899) 2,547 2,452 95 1.90

7133-7803-0 264 257 8 0.48

TRADS (7130) 2,546 2,472 74 1.49

TRADS (30022903) 2,581 2,656 -75 1.47

TRADS (30022246) 2,545 2,480 65 1.30

1456-1379-0 270 264 6 0.38

-7178-0 (V) 1,364 1,336 29 0.78

8872-7359-0 301 306 -6 0.33

TRADS (30021294) 2,584 2,632 -49 0.95

84: 1456-88 268 272 -4 0.27

-7649-0 (V) 497 503 -7 0.29

31: 7133-88 688 696 -8 0.31

1168-9724-0 528 534 -6 0.26

7133-7134-0 768 777 -8 0.30

TRADS (7131) 2,581 2,599 -18 0.35

TRADS (30022633) 2,546 2,529 17 0.34

9724-1168-0 868 874 -5 0.18

7382-8828-0 342 339 2 0.11

-1456-0 (V) 188 189 -1 0.07

TRADS (30022255) 2,583 2,587 -4 0.08




7119-7118-0 348 348 0 0.01

Mean GEH 2.81

Number %


Total GEH < 10 88 97%

Total 91




Table B3: PM Peak Link Flow Calibration Results

Detector Observed

Flow Modelled


GEH

7222-9354-0 169 82 87 7.78

8851-1456-0 237 384 -147 8.36

9354-7222-0 329 203 126 7.73

7359-9445-0 179 289 -110 7.17

7119-7118-0 335 223 112 6.70

-7118-0 (V) 121 177 -56 4.57

84: 1456-88 321 220 101 6.14

7118-9355-0 267 187 80 5.32

7134-8034-0 446 624 -177 7.66

1456-1382-0 432 596 -164 7.25

TRADS (30021205) 794 1,078 -284 9.29

7804-7536-0 97 72 25 2.68

7383-7382-0 420 315 105 5.48

7359-7370-0 157 120 37 3.17

7593-7222-0 71 93 -22 2.43

1167-1168-0 75 97 -22 2.34

7359-8872-0 234 301 -67 4.11

7134-7593-0 515 405 110 5.12

7133-7803-0 407 510 -102 4.78

7359-1384-0 407 325 82 4.27

1170-1168-0 654 525 128 5.27

1168-1170-0 499 402 97 4.56

7428-7729-0 284 351 -67 3.74

7382-8828-0 379 309 70 3.76

TRADS (30021279) 1,595 1,952 -357 8.48

7729-7428-0 651 548 103 4.22

8864-7133-0 715 823 -108 3.89

7222-7221-0 537 612 -75 3.14

1168-1153-0 715 632 83 3.22

7370-7359-0 127 113 14 1.32

7708-7178-0 1,680 1,492 188 4.73

1384-1385-0 537 478 59 2.61

1383-1384-0 1,100 1,234 -133 3.90

7536-7804-0 45 40 5 0.72

7221-7222-0 870 970 -100 3.28

7649-7306-0 645 578 66 2.67

7223-7382-0 369 411 -42 2.13

7235-8036-0 1,009 913 96 3.11

32: 8034-71 759 693 65 2.43

7359-8068-0 378 414 -36 1.78

7269-9245-0 1,446 1,323 123 3.30

8068-7359-0 228 249 -21 1.36

TRADS (30021204) 1,180 1,085 95 2.83

(30021293) 1,459 1,579 -121 3.09




1384-7592-0 614 665 -51 2.01

1384-7359-0 411 445 -34 1.62

TRADS (7131) 3,045 2,836 209 3.86

7267-7268-0 2,108 1,978 130 2.87

7293-7549-0 645 686 -41 1.59

TRADS (30022899) 3,100 3,290 -190 3.37

7133-8864-0 839 791 48 1.69

7593-7134-0 493 523 -29 1.31

7103-7210-0 466 441 26 1.20

TRADS (30022633) 3,098 3,259 -161 2.86

7306-7649-0 757 796 -39 1.39

1168-8977-0 627 657 -31 1.21

9445-7359-0 187 179 9 0.63

8828-7382-0 453 475 -21 1.00

7549-7293-0 541 566 -25 1.07

7224-7222-0 624 597 27 1.10

TRADS (30022255) 3,040 3,174 -134 2.41

7118-7119-0 593 619 -25 1.03

1379-1456-0 223 233 -9 0.62

1168-9724-0 673 646 27 1.05

TRADS (30022246) 3,105 3,230 -125 2.23

7215-7590-0 487 505 -19 0.85

7118-7579-0 251 242 9 0.59

7579-7118-0 405 392 14 0.68

8872-7359-0 412 427 -14 0.70

8977-1168-0 746 722 24 0.90

7117-7118-0 436 450 -14 0.66

7382-7223-0 401 411 -10 0.52

7133-7134-0 906 928 -22 0.73

8870-1385-0 675 661 14 0.56

7209-7103-0 454 464 -10 0.46

9724-1168-0 1,047 1,026 21 0.64

7803-7133-0 366 373 -7 0.37

8036-7235-0 767 780 -13 0.47

1456-1379-0 350 354 -4 0.22

7222-7223-0 1,183 1,170 14 0.40

7175-7179-0 2,024 2,003 21 0.47

7121-7120-0 347 350 -3 0.17

87: 7118-71 196 195 2 0.13

TRADS (30022903) 3,045 3,025 20 0.36

7134-7133-0 1,079 1,074 6 0.17

TRADS (30021294) 3,042 3,057 -16 0.28

7382-7383-0 380 381 -1 0.06

7120-7121-0 733 731 2 0.07

1385-1382-0 566 565 1 0.05

TRADS (7130) 3,103 3,109 -6 0.11




7589-7214-0 878 878 1 0.02

Mean GEH 2.62

Number %


Total GEH < 10 91 100%

Total 91

Subject BRT North Additional Work

Date 21 July 2011

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© Arup | F0.13 | 14 February 2011

BRT North Run-Time Modelling

1 Introduction

The note outlines the methodology and the results in July 2011.

2 Methodology

2.1 Models Used

2.1.1 Sheffield District

The Lower Don Valley and Sheffield City Centre to obtain the run-times of BRT vehicles within on the “Scenario 4” model from the BRT runfuture year traffic demand and bus stop dwell times are based on outputs from SRTM3 and SRPTM3, respectively

2.1.2 Rotherham District

In order to predict run times on sections of the route in Rotherham Dbeen developed. This model takesSRTM3 model. The BRT route is modelled on a metre by metre basis using information about speeds, delays and stop times. Results are representative of, and consistent with, the AM peak and PM peak periods modelled with SRTM3 and Aimsun.

2.1.3 Aimsun Model Refinement

A number of refinements have been

• Incorporation of the Hartswell Tesco proposals including provision of new access junctions on Spital Hill and Savile Street;

• Provision of a separately signalled right

BRT North Additional Work

Job No/Ref

11 TRANSPORTATION\0-11-8 REPORTS\2011.07.29.BRT RUN TIME MODELLING - JULY 2011.DOCX

Time Modelling

the methodology and the results from the run-time modelling exercise undertaken

and Sheffield City Centre Aimsun microsimulation modeltimes of BRT vehicles within Sheffield district. The future year model

” model from the BRT run-time modelling study undertaken in March 2011future year traffic demand and bus stop dwell times are based on outputs from

respectively.

Rotherham District

In order to predict run times on sections of the route in Rotherham District a spreadsheet model has information on delays at junctions and free flow speeds from the

SRTM3 model. The BRT route is modelled on a metre by metre basis using information about Results are representative of, and consistent with, the AM peak and

PM peak periods modelled with SRTM3 and Aimsun.

Model Refinements

refinements have been made to the Aimsun models including:

Incorporation of the Hartswell Tesco proposals including the closure of Carlisle Street and provision of new access junctions on Spital Hill and Savile Street;

Provision of a separately signalled right-turn at the Meadowhall Way / Tinsley Link junction.

207585-19/LWC

Page 1 of 3

time modelling exercise undertaken

Aimsun microsimulation models have been used The future year models are based

ng study undertaken in March 2011. The future year traffic demand and bus stop dwell times are based on outputs from the latest runs of

spreadsheet model has information on delays at junctions and free flow speeds from the

SRTM3 model. The BRT route is modelled on a metre by metre basis using information about Results are representative of, and consistent with, the AM peak and

the closure of Carlisle Street and

turn at the Meadowhall Way / Tinsley Link junction.

J:\200000\207585-00\207585-19 BRT NORTH ADDITIONAL WORK\0 ARUP\0-11 TRANSPORTATION\0-11-8 REPORTS\2011.07.29.BRT RUN TIME MODELLING - JULY 2011.DOCX

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3 Results

3.1 Run Time results

A summary of the run-times for the refinement is provided in the table below, with a comparison with the results from the committed transport scheme carried out in March 2011. A detailed breakdown on the results is presented in Appendix A, including the standard deviation associated with the mean run-times outputted from the Aimsun model.

Table 1 Run-time Summary Table

Scenario

Peak

2015 2030

Run Time Dwell Time Total Run Time Dwell Time Total

March 2011

Scenario 4

AM 55:26 10:14 65:40 59:33 10:14 69:47

IP 52:26 5:08 57:34 52:12 5:08 57:20

PM 63:12 8:56 72:08 63:22 8:56 72:18

July 2011

Iteration 1

AM 50:56 8:49 59:45 52:16 8:41 60:57

IP 50:25 8:01 58:25 50:27 8:00 58:27

PM 60:03 8:57 69:00 63:35 10:30 74:05

Note: All times are shown in a mm:ss format

4 Analysis of Results

4.1 Comparison with December 2010 Run Times

The results from this iteration show improvements over the total travel time for most scenarios. In those scenarios where there are increases in journey time, the increase is mainly due to the increase in dwell time. The key changes relative to the previous run times are as follows:

• 2015 AM – Total run times have reduced by approximately six minutes. These is due to reduced congestion as a result of less traffic in the model coupled with lower dwell times;

• 2015 IP – Total run times have increased by just under a minute. This increase is due to increased dwell times, although this is offset somewhat by less congestion as a result of reduced traffic in the model;

• 2015 PM – Total run times have reduced by approximately three minutes. These is due to reduced congestion as a result of less traffic in the model;

• 2030 AM - Total run times have reduced by almost nine minutes. These is due to less congestion as a result of reduced traffic in the model coupled with lower dwell times;

• 2030 IP - Total run times have increased by just over a minute. This increase is due to increased dwell times, although this is offset somewhat by lower congestion as a result of reduced traffic in the model;

• 2030 PM - Total run times have increased by almost two minutes, driven by increases in dwell time.

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4.2 Run Time Variability

Five runs (known as replications) of the Aimsun model have been undertaken for each time period (AM, IP and PM). Each of the replications is unique, based on a randomly generated seed. For each replication, Aimsun outputs the mean and standard deviation for the journey times for each replication. Table 2 below presents the minimum, maximum and mean of the mean journey times for each replication in each time period. The table therefore shows the variation between different replications which, in effect, represent different days (ie day-to-day variation).

The table confirms that run-times will be between approximately 57 and 72 minutes in 2015, increasing to between approximately 58 and 80 minutes in 2030.

Table 2: Minimum, Mean and Maximum Run Times

2015 2030

Min Mean Max No

Runs Min Mean Max

No

Runs

AM 58.45 59.45 60.50 5 59.47 60.57 63.03 5

IP 57.34 58.35 59.12 5 57.52 58.28 58.52 5

PM 65.20 69.00 71.47 5 70.18 74.05 80.14 5

5 Summary

Additional work has been undertaken to refine the Aimsun modelling of BRT run-times in response to further SRTM3 iteration. This note has outlines the methodology and the results obtained.

A comparison has been made between Scenario 4 with committed transport schemes from March 2011 output. The results from this iteration show improvements over the total travel time for most scenarios. The reduction is BRT journey time is mainly due to reduction in congestion in the modelled time periods.

Appendix A

Stop-to-stop run timestop run time

Jonathan.burton

Rectangle

Run Time Dwell Time Total Run Time Dwell Time TotalStandard

deviationRun Time Dwell Time Total Run Time Dwell Time Total

Standard


Standard

deviation

Rotherham

InterchangeWestgate 1.42 0.46 2.27 1.41 1.16 2.57

Rotherham


Rotherham


Westgate Grange Lane 3.05 0.15 3.20 3.26 0.06 3.33 Westgate Grange Lane 3.07 0.07 3.15 3.07 0.03 3.10 Westgate Grange Lane 3.49 0.05 3.55 3.22 0.16 3.38

Grange Lane Lock Lane 1.41 0.17 1.58 1.39 0.28 2.07 Grange Lane Lock Lane 1.38 0.07 1.45 1.39 0.15 1.54 Grange Lane Lock Lane 1.34 0.07 1.41 1.36 0.09 1.45

Lock LaneMeadowhall

South2.05 0.40 2.45 2.18 0.07 2.26 0.09 Lock Lane

Meadowhall

South2.01 0.15 2.16 2.12 0.17 2.29 0.15 Lock Lane

Meadowhall

South2.02 0.14 2.16 2.01 0.19 2.20 0.07

Meadowhall

SouthCarbrook 3.18 0.40 3.58 2.50 0.19 3.09 0.13

Meadowhall

SouthCarbrook 5.36 0.15 5.51 3.48 0.10 3.58 0.51

Meadowhall

SouthCarbrook 6.00 0.14 6.14 5.16 0.16 5.31 1.12

Carbrook Attercliffe North 4.27 0.44 5.11 3.30 0.46 4.17 0.22 Carbrook Attercliffe North 3.43 0.14 3.57 3.21 0.35 3.56 0.21 Carbrook Attercliffe North 4.20 0.21 4.41 3.49 0.52 4.40 0.24

Attercliffe North Attercliffe South 2.04 0.44 2.48 2.11 0.09 2.20 0.22 Attercliffe North Attercliffe South 1.54 0.12 2.06 1.57 0.10 2.08 0.11 Attercliffe North Attercliffe South 3.02 0.20 3.22 3.06 0.12 3.17 0.56

Attercliffe South Wicker 3.60 0.45 4.45 3.56 0.18 4.15 0.38 Attercliffe South Wicker 3.55 0.15 4.10 3.43 0.13 3.55 0.36 Attercliffe South Wicker 4.29 0.26 4.55 4.39 0.14 4.54 0.52

Wicker Castle Square 2.46 0.54 3.40 2.37 0.18 2.56 0.12 Wicker Castle Square 2.18 0.33 2.51 2.12 0.29 2.41 0.17 Wicker Castle Square 2.47 1.24 4.11 2.31 0.11 2.42 0.30

Castle SquareHallam

University0.60 0.20 1.20 0.51 0.49 1.40 0.03 Castle Square

Hallam


Hallam

University1.10 0.31 1.41 2.11 0.13 2.24 2.13

Hallam

UniversityFurnival Street 0.22 0.32 0.54 0.32 0.24 0.56 0.05

Hallam


Hallam


Furnival Street Sheaf Square 0.57 0.21 1.18 0.56 0.15 1.11 0.01 Furnival Street Sheaf Square 0.57 0.13 1.10 0.56 0.06 1.01 0.01 Furnival Street Sheaf Square 0.56 0.35 1.31 0.57 0.04 1.01 0.03

Sheaf SquareSheffield

Interchange0.54 0.29 1.23 0.53 0.44 1.37 0.02 Sheaf Square

Sheffield


Sheffield

Interchange0.56 0.50 1.46 0.56 0.20 1.16 0.05

Sheffield

InterchangeWicker 2.56 0.28 3.24 2.43 0.55 3.38 0.13

Sheffield


Sheffield


Wicker Attercliffe South 3.50 0.17 4.07 4.04 0.14 4.18 0.39 Wicker Attercliffe South 3.21 0.08 3.29 3.31 0.22 3.52 0.26 Wicker Attercliffe South 3.29 0.14 3.43 4.04 0.22 4.26 0.30

Attercliffe South Attercliffe North 2.42 0.17 2.59 2.10 0.21 2.30 0.15 Attercliffe South Attercliffe North 2.08 0.08 2.16 2.22 0.27 2.48 0.15 Attercliffe South Attercliffe North 2.32 0.14 2.46 2.25 0.29 2.54 0.19

Attercliffe North Carbrook 4.43 0.14 4.57 3.26 0.12 3.38 0.15 Attercliffe North Carbrook 3.20 0.02 3.22 3.31 0.19 3.51 0.32 Attercliffe North Carbrook 5.18 0.05 5.23 3.33 0.36 4.09 0.27

CarbrookMeadowhall

South3.01 0.14 3.15 3.08 0.17 3.24 0.18 Carbrook

Meadowhall

South3.17 0.15 3.32 3.16 0.31 3.46 0.24 Carbrook

Meadowhall

South5.27 0.05 5.32 3.49 0.45 4.35 0.35

Meadowhall

SouthLock Lane 2.22 0.14 2.36 2.26 0.13 2.39 0.16

Meadowhall

SouthLock Lane 2.11 0.11 2.23 2.47 0.14 3.01 0.23

Meadowhall

SouthLock Lane 2.06 0.12 2.17 3.06 0.21 3.27 0.29

Lock Lane Grange Lane 1.24 0.14 1.38 1.18 0.06 1.24 Lock Lane Grange Lane 1.18 0.12 1.29 1.18 0.04 1.22 Lock Lane Grange Lane 1.18 0.15 1.33 1.18 0.13 1.31

Grange Lane Westgate 3.23 0.17 3.41 2.54 0.14 3.08 Grange Lane Westgate 2.46 0.10 2.55 2.51 0.12 3.03 Grange Lane Westgate 3.25 0.14 3.39 3.21 0.21 3.42

WestgateRotherham

Interchange2.46 0.32 3.18 1.26 0.17 1.43 Westgate

Rotherham


Rotherham

Interchange2.13 0.03 2.16 1.27 0.46 2.13

55.26 10.14 65.40 50.56 8.49 59.45 52.26 5.08 57.34 50.25 8.01 58.25 63.12 8.56 72.08 60.03 8.57 69.00

2015 AM 2015 IP

2015 Run Times

July 2011 Iteration 1 July 2011 Iteration 1

2015 PM

Ru

n-t

ime

Mo

del

Ru

n-t

ime

Mo

del

From To

Total (mm.ss) Total (mm.ss) Total (mm.ss)

Lo

wer

Do

n V

all

ey A

imsu

n

Mo

del

Scenario 4

From To

Lo

wer

Do

n V

all

ey A

imsu

n

Mo

del

Lo

wer

Do

n V

all

ey A

imsu

n

Mo

del

Sh

effi

eld

Cit

n C

entr

e A

imsu

n M

od

el

Scenario 4 July 2011 Iteration 1

Ru

n-t

ime

Mo

del

Ru

n-t

ime

Mo

del

Ru

n-t

ime

Mo

del

Lo

wer

Don

Vall

ey A

imsu

n

Mod

elS

hef

fiel

d C

itn

Cen

tre

Aim

sun

Mo

del

Lo

wer

Do

n V

all

ey A

imsu

n

Mo

del

Lo

wer

Do

n V

all

ey A

imsu

n

Mo

del

Sh

effi

eld

Cit

n C

entr

e A

imsu

n M

od

el

Scenario 4

From To

Ru

n-t

ime

Mo

del

55.26 10.14 65.40 50.56 8.49 59.45 52.26 5.08 57.34 50.25 8.01 58.25 63.12 8.56 72.08 60.03 8.57 69.00Total (mm.ss) Total (mm.ss) Total (mm.ss)

Run Time Dwell Time Total Run Time Dwell Time TotalStandard


Standard


Standard

deviation

Rotherham


Rotherham


Rotherham


Westgate Grange Lane 4.22 0.15 4.36 4.29 0.08 4.37 WestgateGrange

Lane3.09 0.07 3.16 3.10 0.03 3.13 Westgate Grange Lane 3.32 0.05 3.38 4.06 0.18 4.25

Grange Lane Lock Lane 1.39 0.17 1.56 1.39 0.20 1.59Grange

LaneLock Lane 1.38 0.07 1.45 1.39 0.19 1.57 Grange Lane Lock Lane 1.34 0.07 1.41 1.36 0.10 1.46

Lock LaneMeadowhall

South2.11 0.40 2.51 2.09 0.12 2.21 0.06 Lock Lane

Meadowhal

l South2.08 0.15 2.23 2.16 0.18 2.35 0.15 Lock Lane

Meadowhall

South2.08 0.14 2.22 2.09 0.19 2.27 0.15

Meadowhall

SouthCarbrook 4.02 0.40 4.42 2.50 0.30 3.20 0.12

Meadowhal

l SouthCarbrook 3.46 0.15 4.01 3.19 0.15 3.34 0.24

Meadowhall

SouthCarbrook 4.09 0.14 4.23 4.07 0.36 4.43 0.42

Carbrook Attercliffe North 5.56 0.44 6.40 3.54 0.39 4.33 0.45 CarbrookAttercliffe

North3.38 0.14 3.52 3.27 0.38 4.05 0.16 Carbrook Attercliffe North 4.07 0.21 4.28 4.27 0.51 5.18 0.54

Attercliffe North Attercliffe South 1.57 0.44 2.41 1.53 0.06 1.59 0.06Attercliffe

North

Attercliffe

South1.39 0.12 1.51 1.49 0.11 2.01 0.05 Attercliffe North Attercliffe South 1.58 0.20 2.18 2.42 0.12 2.55 0.36

Attercliffe South Wicker 3.56 0.45 4.41 3.38 0.12 3.50 0.30Attercliffe

SouthWicker 3.50 0.15 4.05 3.31 0.26 3.57 0.24 Attercliffe South Wicker 3.58 0.26 4.24 3.25 0.40 4.04 0.36

Wicker Castle Square 3.26 0.54 4.20 2.17 0.13 2.30 0.20 WickerCastle

Square2.34 0.33 3.07 2.38 0.19 2.57 0.13 Wicker Castle Square 4.27 1.24 5.51 4.18 0.11 4.29 1.22

Castle SquareHallam

University0.60 0.20 1.20 0.53 0.32 1.24 0.09

Castle

Square

Hallam


Hallam

University1.37 0.31 2.08 2.32 0.08 2.41 3.12

Hallam


Hallam

University

Furnival

Street0.27 0.21 0.48 0.25 0.13 0.39 0.05

Hallam


Furnival Street Sheaf Square 1.03 0.21 1.24 0.60 0.14 1.14 0.08Furnival

Street

Sheaf

Square1.01 0.13 1.14 0.58 0.05 1.03 0.14 Furnival Street Sheaf Square 1.31 0.35 2.06 1.04 0.04 1.08 0.10

Sheaf SquareSheffield

Interchange1.03 0.29 1.32 0.53 0.34 1.27 0.02

Sheaf

Square

Sheffield


Sheffield

Interchange1.16 0.50 2.06 0.55 0.21 1.16 0.02

Sheffield


Sheffield


Sheffield


Wicker Attercliffe South 5.44 0.17 6.01 4.02 0.17 4.19 0.33 WickerAttercliffe

South4.04 0.08 4.12 3.59 0.15 4.14 0.36 Wicker Attercliffe South 3.43 0.14 3.57 4.47 0.22 5.09 1.19

Attercliffe South Attercliffe North 2.13 0.17 2.30 2.13 0.18 2.31 0.10Attercliffe

South

Attercliffe

North2.13 0.08 2.21 2.13 0.23 2.37 0.15 Attercliffe South Attercliffe North 2.24 0.14 2.38 2.37 0.29 3.06 0.28

Attercliffe North Carbrook 4.11 0.14 4.25 3.40 0.25 4.06 0.28Attercliffe

NorthCarbrook 4.30 0.02 4.32 3.29 0.24 3.53 0.17 Attercliffe North Carbrook 4.48 0.05 4.53 3.46 0.51 4.37 0.42

CarbrookMeadowhall

South3.01 0.14 3.15 3.19 0.24 3.43 0.27 Carbrook

Meadowhal

l South3.20 0.15 3.35 3.30 0.34 4.04 0.24 Carbrook

Meadowhall

South5.15 0.05 5.20 4.23 0.47 5.10 0.43

Meadowhall

SouthLock Lane 2.22 0.14 2.36 3.03 0.12 3.15 0.23

Meadowhal

l SouthLock Lane 2.23 0.11 2.35 2.47 0.12 2.59 0.18

Meadowhall

SouthLock Lane 3.07 0.12 3.19 2.58 0.21 3.19 0.32

Lock Lane Grange Lane 1.18 0.14 1.32 1.18 0.07 1.25 Lock LaneGrange

Lane1.18 0.12 1.29 1.18 0.05 1.22 Lock Lane Grange Lane 1.18 0.15 1.33 1.18 0.17 1.35

Grange Lane Westgate 2.49 0.17 3.06 2.55 0.13 3.08Grange

LaneWestgate 2.47 0.10 2.56 2.52 0.12 3.04 Grange Lane Westgate 3.25 0.14 3.39 3.22 0.25 3.47

WestgateRotherham

1.43 0.32 2.14 1.17 0.25 1.42 WestgateRotherham

1.35 0.15 1.49 1.03 0.31 1.34 WestgateRotherham

2.13 0.03 2.16 1.21 0.52 2.12

2030 Run Times

2030 AM 2030 IP 2030 PM

From To

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Scenario 4

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July 2011 Iteration 1

To

July 2011 Iteration 1Scenario 4

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July 2011 Iteration 1Scenario 4

From To

WestgateRotherham


Rotherham


Rotherham

Interchange2.13 0.03 2.16 1.21 0.52 2.12

59.33 10.14 69.47 52.16 8.41 60.57 52.12 5.08 57.20 50.27 8.00 58.27 63.22 8.56 72.18 63.35 10.30 74.05Total (mm.ss)Total (mm.ss) Total (mm.ss)

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Admiral House, Rose Wharf

78 East Street, Leeds LS9 8EE

www.arup.com

Project title Rotherham - Sheffield Bus Rapid Transit

Cc Project Team

Prepared by Jonathan Burton (Leeds)

Subject i

Northern Route Base Run Time Model Validation Report

1. INTRODUCTION

Assessing the time savings to be generated from process as any time savings are likely to form a significant scheme. The DfT does not provide any specific guidance on the methodology for forecasting run times and does not, currently, permit the appraisal of benefits by microsimulation modelling alone.A number of AIMSUN microsimulation models exist for the sections of the network in Sheffield. It is thought that these models offer the most robust method for fohowever, only the sections of BRT route

In order to predict run times on sections of the route in Rotherham has been developed. This from the SRTM3 model. The BRT route is modelled on a metre by metre basis using information about speeds, delays and stop times. peak and PM peak periods

The purpose of this analysis is to provide information to support the refinement of the scheme design and to supplement the strategic modelling underpinning the major scheme appraisal. While it strengthens the robust approach taken by the project partners to understanding the main impacts of the scheme it is not intended the DfT. As such, a formal validation complying with DfT guidance Instead a local comparison to demonstrate consistency with other models and available observed data has been undertaken.

In order to assess the accuracy of thhas been validated against a number of sources. These include:

• The AIMSUN models for sections of the route in Sheffield

• The bus journey times contained within SRPTM3

• Journey time surveys

These three sources share some common background data sources but exhibit differences at the detailed level. For example, the AIMSUN model in general predicts longer journey times than the SRPTM3 model. It is noted that there will be some iteration betweand these differences should reduce in scale. reference for the run time model and instead, all three comparative data sets.

Sheffield Bus Rapid Transit Job number

123006-00

File reference

0-11-8-13

(Leeds) Date

5 September 2011

Northern Route Base Run Time Model Validation Report

Assessing the time savings to be generated from BRT is an important part of the appraisal process as any time savings are likely to form a significant element of the economic case

. The DfT does not provide any specific guidance on the methodology for forecasting run and does not, currently, permit the appraisal of benefits by microsimulation modelling alone.

A number of AIMSUN microsimulation models exist for the sections of the network in Sheffield. It is thought that these models offer the most robust method for forecasting bus and BRT run times;

sections of BRT route within Sheffield District are within scope of these models.

In order to predict run times on sections of the route in Rotherham District a s been developed. This model takes information on delays at junctions and free flow speeds

from the SRTM3 model. The BRT route is modelled on a metre by metre basis using information about speeds, delays and stop times. Results are representative of and consistent with eak and PM peak periods modelled with SRTM3 and AIMSUN.

he purpose of this analysis is to provide information to support the refinement of the scheme design and to supplement the strategic modelling underpinning the major scheme appraisal.

gthens the robust approach taken by the project partners to understanding the main impacts of the scheme it is not intended to contribute to the modelling data evaluated in detail by

formal validation complying with DfT guidance is not relevant to this model. Instead a local comparison to demonstrate consistency with other models and available observed

has been undertaken.

In order to assess the accuracy of this model and thus its fitness for purpose the ted against a number of sources. These include:

AIMSUN models for sections of the route in Sheffield

times contained within SRPTM3

time surveys of existing routes.

These three sources share some common background data sources but exhibit differences at the For example, the AIMSUN model in general predicts longer journey times than the

SRPTM3 model. It is noted that there will be some iteration between the models in due course and these differences should reduce in scale. Consequently there is no single point validation reference for the run time model and instead, the target is to show consistency within the range of all three comparative data sets.

Technical Note

Page 1 of 8

Tel +44 (0)113 2428498

Fax +44 (0)113 2428573

00

5 September 2011

part of the appraisal of the economic case for the

. The DfT does not provide any specific guidance on the methodology for forecasting run and does not, currently, permit the appraisal of benefits by microsimulation modelling alone.

A number of AIMSUN microsimulation models exist for the sections of the network in Sheffield. It recasting bus and BRT run times;

istrict are within scope of these models.

a spreadsheet model information on delays at junctions and free flow speeds

from the SRTM3 model. The BRT route is modelled on a metre by metre basis using information and consistent with the AM

he purpose of this analysis is to provide information to support the refinement of the scheme design and to supplement the strategic modelling underpinning the major scheme appraisal.

gthens the robust approach taken by the project partners to understanding the main to the modelling data evaluated in detail by

relevant to this model. Instead a local comparison to demonstrate consistency with other models and available observed

fitness for purpose the Run Time model

These three sources share some common background data sources but exhibit differences at the For example, the AIMSUN model in general predicts longer journey times than the

en the models in due course Consequently there is no single point validation

consistency within the range of

123006-00

5 September 2011

2. SUMMARY OF RUN TIME

The Arup run time model was developed in order to model run times that could be expected from bus and BRT services and to examine the impact of the priority measures proposed as part of the scheme on these run times.

The run time model is an Excel based programme and provides a mathematical representation of a vehicle’s journey along its route. For every metre of the route the speed and run time of the vehicle is modelled. A number of inputs feed into the model, incl

• Vehicle characteristics (acceleration, deceleration, maximum speed);

• Route distance;

• Speed limit information;

• The location of junctions (and length of these along the route);

• Speeds and delays from the SRTM3 highway model;

• Stop time (this is base

• Location and length of priority measures (bus lanes and signal priority)

For areas with priority the model assumes that vehicles will travel at the speed limit wherever possible, accelerating and decelerating at stopping points in accordance with the values set in the vehicle characteristics input. experience the same levels of traffic. Thus speeds and delays from the SRTM3 highway model are used to inform speeds and delays for buses in the run time model.AIMSUN with buses assuming to slow in accordance with the deceleration profile, before waiting for the required length of period (as output from SRTM3), and accelerating again.

The base models examined in this scenario service is modelled in the model (to allow for validation against survey data). With this in mind no additional bus priority measures are modelled in the base model. The base model does not include the section of the route that diverts via St. Lawrence road and Bawtry Road as this are is not included in the BRT proposals.

Information from three sources (AIMSUN models, SRPTM3, and observed counts) has been used to validate the run time model. Figure 1sources.

3. VALIDATION AGAINST A

The Run Time model has been validated against run times provided by the Lower Don Valley AIMSUN model. Analysis of the base run times were significantly quicker than those from AIMSUN, with this in mind the speeds that were input in to the Run Time Model from SRTM3 were factored to reduce the overall run time. This was done on a section by section basis, with some AIMSUN, the speeds on these sections were increased. Run Time Model post calibration are shown in run times for the section of model between Vulcan Road and the Wicker (this is the common area covered by both models)

SUMMARY OF RUN TIME MODEL CONSTRUCTION

The Arup run time model was developed in order to model run times that could be expected from bus and BRT services and to examine the impact of the priority measures proposed as part of the scheme on these run times.

The run time model is an Excel based programme and provides a mathematical representation of a vehicle’s journey along its route. For every metre of the route the speed and run time of the vehicle is modelled. A number of inputs feed into the model, including:

Vehicle characteristics (acceleration, deceleration, maximum speed);

Speed limit information;

The location of junctions (and length of these along the route);

Speeds and delays from the SRTM3 highway model;

Stop time (this is based on surveyed information from the SCC surveys);

Location and length of priority measures (bus lanes and signal priority).

he model assumes that vehicles will travel at the speed limit wherever possible, accelerating and decelerating at stopping points in accordance with the values set in the vehicle characteristics input. If no priority is available then the model assumes tha

the same levels of delay at junctions and on the route in generaltraffic. Thus speeds and delays from the SRTM3 highway model are used to inform speeds and delays for buses in the run time model. Junctions are not modelled in detail as they are in AIMSUN with buses assuming to slow in accordance with the deceleration profile, before waiting for the required length of period (as output from SRTM3), and accelerating again.

The base models examined in this scenario follow the route of the existing service 69 and this service is modelled in the model (to allow for validation against survey data). With this in mind no additional bus priority measures are modelled in the base model. The base model does not

ection of the route that diverts via St. Lawrence road and Bawtry Road as this are is not included in the BRT proposals.

Information from three sources (AIMSUN models, SRPTM3, and observed counts) has been used to validate the run time model. Figure 1 appended to this report shows the coverage of these data

VALIDATION AGAINST AIMSUN DATA

odel has been validated against run times provided by the Lower Don Valley Analysis of the base run times showed that the times form the Run Time

were significantly quicker than those from AIMSUN, with this in mind the speeds that were input in odel from SRTM3 were factored to reduce the overall run time. This was done

on a section by section basis, with some sections demonstrating a slower run time than that from AIMSUN, the speeds on these sections were increased. The run times from AIMSUN and the Run Time Model post calibration are shown in Tables 3.1, 3.2 and 3.3. These run times for the section of model between Vulcan Road and the Wicker (this is the common area covered by both models)

Technical Note

Page 2 of 8

The Arup run time model was developed in order to model run times that could be expected from bus and BRT services and to examine the impact of the priority measures proposed as part of the

The run time model is an Excel based programme and provides a mathematical representation of a vehicle’s journey along its route. For every metre of the route the speed and run time of the

d on surveyed information from the SCC surveys);

.

he model assumes that vehicles will travel at the speed limit wherever possible, accelerating and decelerating at stopping points in accordance with the values set in the

If no priority is available then the model assumes that vehicles will delay at junctions and on the route in general as does general

traffic. Thus speeds and delays from the SRTM3 highway model are used to inform speeds and t modelled in detail as they are in

AIMSUN with buses assuming to slow in accordance with the deceleration profile, before waiting for the required length of period (as output from SRTM3), and accelerating again.

follow the route of the existing service 69 and this service is modelled in the model (to allow for validation against survey data). With this in mind no additional bus priority measures are modelled in the base model. The base model does not

ection of the route that diverts via St. Lawrence road and Bawtry Road as this are is

Information from three sources (AIMSUN models, SRPTM3, and observed counts) has been used appended to this report shows the coverage of these data

odel has been validated against run times provided by the Lower Don Valley orm the Run Time Model

were significantly quicker than those from AIMSUN, with this in mind the speeds that were input in odel from SRTM3 were factored to reduce the overall run time. This was done

sections demonstrating a slower run time than that from run times from AIMSUN and the

ese tables show overall run times for the section of model between Vulcan Road and the Wicker (this is the common area

123006-00

5 September 2011

Table 3.1: Run Time Model comparison with

Spital Hill - Staniforth Road

Staniforth Road - Worksop Road

Worksop Road - Janson Street

Janson Street - Weedon Street

Weedon Street – Vulcan Road

Total

M1 Junction 34 - Weedon Steet

Weedon Street - Janson Street

Janson Street - Worksop Road

Worksop Road - Staniforth Road

Staniforth Road - Spital Hill

Total

Table 3.2: Run Time Model comparison with AIMSUN bus journey times IP (mm.ss)





Weedon Street – M1 Junction 34

Total

M1 Junction 34 - Weedon Steet





Total

Table 3.3: Run Time Model comparison with AIMSUN bus journey times





Weedon Street - M1 Junction 34

Total

M1 Junction 34 - Weedon





Total

The results of the analysis show that the however, post calibration these run times are significantly closer to the AIMSUN run times than prior to calibration. All times are

Run Time Model comparison with AIMSUN bus journey times AIMSUN Run Time Model Percentage

Staniforth Road 6.26 5.26

Worksop Road 1.26 1.46

Janson Street 2.54 1.48

Weedon Street 2.47 2.41

Vulcan Road 1.42 1.59

15.15 13.40

Weedon Steet 2.23 2.23




Spital Hill 5.00 4.17

13.52 11.46

3.2: Run Time Model comparison with AIMSUN bus journey times IP (mm.ss)AIMSUN Run Time Model Percentage





M1 Junction 34 1.39 1.21

16.08 12.54






13.56 12.12

Table 3.3: Run Time Model comparison with AIMSUN bus journey timesAIMSUN Run Time Model Percentage

Road 8.23 8.56




M1 Junction 34 1.42 2.09

17.35 20.58






15.15 11.04

The results of the analysis show that the Run Time Model generally produces shorter however, post calibration these run times are significantly closer to the AIMSUN run times than prior to calibration. All times are within 15% of each other in all periods and directions

Technical Note

Page 3 of 8

times AM (mm.ss) Percentage

-16%

23%

-38%

-4%

17%

-10%

0%

-15%

-13%

-45%

-14%

-15%

3.2: Run Time Model comparison with AIMSUN bus journey times IP (mm.ss) Percentage

-16%

41%

-47%

-28%

-18%

-20%

57%

-20%

-24%

-42%

-13%

-12%

Table 3.3: Run Time Model comparison with AIMSUN bus journey times PM (mm.ss) Percentage

7%

99%

0%

37%

26%

19%

-45%

31%

-11%

-67%

-31%

-27%

produces shorter run times; however, post calibration these run times are significantly closer to the AIMSUN run times than

ll periods and directions.

123006-00

5 September 2011

4. VALIDATION AGAINST S

Bus journey times have been extracted from the base year used to validate the Run is presented in Tables 4respectively. This shows the run times for the two models between Rotherham Interchange and Lock Lane. The data from the run time model timetabled information included in SRPTM3 is given to the nearest minute, thus there is additional variation inherent in the rounding of SRPTM3 values.

Table 4.1: Run Time Model comparison with

AM

Rotherham Interchange

Magna – Rotherham Interchange

IP



PM



Table 4.2: Run Time Model comparison with SRPTM3

AM



IP



PM



The results of the comparison with the SRPTM3 assessments in this note. Ttime periods and directionsvalidation.

This combination of both overconsistency in the AIMSUNSRPTM3 model, reflecting have been collected and analysed

5. VALIDATION AGAINST J

5.1 Comparison with observed data

Journey time surveys for the route of the number 69 between Rotherham and Sheffield are routinely undertaken by Sheffield City Council. surveys and these have been analysed in order to route segments. The timings have Time Model validation process (Taamber (15% - 20% difference) or green (1

These surveys we’re used to calibrate the model with the times from the model being uplifted by a percentage to match the surveyed timthe speed limit at any point, the times for those sections were capped to the speed limit and other sections were uplifted so that the total time for the journey matched the surveys.

VALIDATION AGAINST SRPTM3

Bus journey times have been extracted from the base year SRPTM3 modelun Time Model. Information for common sections between 4.1 and 4.2, which show journey times for the A1 and 69 services

This shows the run times for the two models between Rotherham Interchange and The data from the run time model is provided to the nearest second whereas the

etabled information included in SRPTM3 is given to the nearest minute, thus there is additional variation inherent in the rounding of SRPTM3 values.

Run Time Model comparison with SRPTM3 for service A1

SRPTM3 Run Time Model

Rotherham Interchange - Magna 6.00 7.42

Rotherham Interchange 8.00 6.53





Run Time Model comparison with SRPTM3 for service 69 SRPTM3 Run Time Model







The results of the comparison with the SRPTM3 model are the most mixed of the validation assessments in this note. The Run Time Model generally underestimates journey times for many time periods and directions compared to SRPTM3 matching with the results of the AIMSUN

both over-estimation and under-estimation in contrast to the relative IMSUN comparison indicates variation in some of the individual

, reflecting relative differences with the way in which the data sethave been collected and analysed.

VALIDATION AGAINST JOURNEY TIME SURVEYS

Comparison with observed data

Journey time surveys for the route of the number 69 between Rotherham and Sheffield are routinely undertaken by Sheffield City Council. Bus stop timings are collected

have been analysed in order to generate stop to stop run times ftimings have then been compared to the run times and are used in the

odel validation process (Table 5.1). Differences are categorised as red (>2% difference) or green (15-0% difference).

These surveys we’re used to calibrate the model with the times from the model being uplifted by a percentage to match the surveyed time. If this process would lead to some services exceeding the speed limit at any point, the times for those sections were capped to the speed limit and other sections were uplifted so that the total time for the journey matched the surveys.

Technical Note

Page 4 of 8

TM3 model and these have been odel. Information for common sections between the two models .2, which show journey times for the A1 and 69 services

This shows the run times for the two models between Rotherham Interchange and is provided to the nearest second whereas the

etabled information included in SRPTM3 is given to the nearest minute, thus there is some

SRPTM3 for service A1 (mm.ss)

Percentage

24%

-18%

23%

-11%

53%

39%

for service 69 (mm.ss) Percentage

-47%

-60%

23%

-21%

53%

11%

model are the most mixed of the validation journey times for many

with the results of the AIMSUN

estimation in contrast to the relative individual times in the

data sets in each model

Journey time surveys for the route of the number 69 between Rotherham and Sheffield are ed as part of the

generate stop to stop run times for certain been compared to the run times and are used in the Run

Differences are categorised as red (>20% difference),

These surveys we’re used to calibrate the model with the times from the model being uplifted by a e. If this process would lead to some services exceeding

the speed limit at any point, the times for those sections were capped to the speed limit and other sections were uplifted so that the total time for the journey matched the surveys.

123006-00

5 September 2011

Table 5.1 shows that thetimes.

Table: 5.1: Run Time Model comparison with

Origin Stop


Centenary Way

Vulcan Road

Meadowhall Retail Park

Coleridge Road

Staniforth Road

Total

Wicker Arches

Staniforth Road

Coleridge Road


Bessemer Way

Centenary Way

Total

* Observed data for AM peak period (7.00 * Section between Ickles Way and Vulcan Road is not modelled

There are some individual differences in of defining timing points in the model order to output times. In most cases the differences in absolute journey times are quite small (less than 30 seconds). This may reflect levels of variable delay difficult for services to operate to fixed timetablessegments additional time may be built into timetables to ensure that buses meet timing points, whereas on other sections timetables may not have enough time built in. The variable nature of congestion and delay, particularly around Meadowhall problems, including sections with a high crossings.

Table 5.2 outlines the comparison for the PM peak, showing that the run times from the modelvalidate well against existing journey times

that the calibrated run times from the model validate well against existing journey

Run Time Model comparison with AM observed journey time

Destination Stop Journey

Time Data Run Time

Model

Centenary Way 3.46

Ickles Way 1.52

Meadowhall Retail Park 2.11

Coleridge Road 3.00

Staniforth Road 3.15

Wicker Arches 3.51

17.55 17.55


Coleridge Road 2.38


Vulcan Road 1.42

Centenary Way 1.54

Rotherham Interchange 4.01 17.44 17.44

* Observed data for AM peak period (7.00 – 10.00) * Section between Ickles Way and Vulcan Road is not modelled as this is not included in the do

individual differences in certain sections, with this largely relatpoints in the model consistently with the timing points used in the surveys in

In most cases the differences in absolute journey times are quite small (less This may reflect levels of variable delay within observed data,

difficult for services to operate to fixed timetables in all stop-to-stop segmentssegments additional time may be built into timetables to ensure that buses meet timing points, whereas on other sections timetables may not have enough time built in. The variable nature of congestion and delay, particularly around Meadowhall and junction 34 will cause additional

sections with a high incidence of controlled or uncontrolled

.2 outlines the comparison for the PM peak, showing that the run times from the modelexisting journey times.

Technical Note

Page 5 of 8

run times from the model validate well against existing journey

journey times (mm.ss) Run Time

Model Difference

4.06 9%

1.50 -1%

2.15 3%

2.56 -2%

3.09 -3%

3.39 -5%

17.55 0%

4.42 -2%

2.37 -1%

2.58 12%

1.47 5%

1.46 -7%

3.54 -3%

17.44 0%

do-something options

relating to the difficulty with the timing points used in the surveys in

In most cases the differences in absolute journey times are quite small (less within observed data, which make it

stop segments. On certain segments additional time may be built into timetables to ensure that buses meet timing points, whereas on other sections timetables may not have enough time built in. The variable nature of

and junction 34 will cause additional incidence of controlled or uncontrolled pedestrian

.2 outlines the comparison for the PM peak, showing that the run times from the model

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Table: 5.2: Run Time Model comparison with

Origin Stop


Centenary Way

Vulcan Road


Coleridge Road

Staniforth Road

Total

Wicker Arches

Staniforth Road

Coleridge Road


Bessemer Way

Centenary Way

Total

* Observed data for PM peak period (16.00 * Section between Ickles Way and Vulcan Road is not modelled something scenarios.

5.2 Journey time variability

The observed data used to support the analysis naturally variable. The analysis presented above shows average observed journey times along the route. The individual run times variability that were experienced on this routeand minimum journey times for each timing point to timing point segment.

Figure 5.1: Observed data

* surveyed data from route 69 (13 observations over 3 days March 2008)

00:00:00

00:01:26

00:02:53

00:04:19

00:05:46

00:07:12

00:08:38

00:10:05

00:11:31

Run Time Model comparison with PM observed journey time

Destination Stop Journey

Time Data Run Time

Model

Centenary Way 4.23

Ickles Way 2.01


Coleridge Road 2.58


Wicker Arches 3.33

18.46 18.46


Coleridge Road 2.47


Vulcan Road 2.13

Centenary Way 2.18

Rotherham Interchange 4.25 19.35 19.35

Observed data for PM peak period (16.00 – 19.00) * Section between Ickles Way and Vulcan Road is not modelled as there is no route match between the base and do

ourney time variability within the observed data

used to support the analysis show that journey times along the route are naturally variable. The analysis presented above shows average observed journey times along

individual run times underlying this average show the level of journey timeexperienced on this route. Figures 5.1 and 5.2 show the average

and minimum journey times for each timing point to timing point segment.

Observed data variation – AM peak period (7.00 – 10.00)


Technical Note

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journey times (mm.ss) Run Time

Model Difference

5.10 20%

1.57 -22%

1.55 -30%

2.58 0%

3.15 -5%

3.32 0%

18.46 0%

5.02 14%

2.41 -2%

3.12 1%

2.18 2%

2.11 -3%

4.11 -3%

19.35 0%

between the base and do

show that journey times along the route are naturally variable. The analysis presented above shows average observed journey times along

e level of journey time .2 show the average, maximum

10.00)

Min

Average

Max

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Figure 5.2: Observed data


The figures above show that there is significant variation in the run times gobserved surveys; the range of this variable delay along the route include:

• Pedestrian crossings which operate o

• The point in the cycle that the bus arrives at key signalised junctions;

• Driver behaviour (if the driver waits until passengers are seated to accelerate);

• Passenger behaviour at boarding (variation in the time to process different ticket typ

• Number of passengers boarding per service;

• Variable levels of traffic congestion across the peak periods.

This variable delay must be considered when comparing the results of the run time models to the averages from the observed data. between model formats that have used different observed data for model building and validation and where variation has been “smoothed out”. time, based upon delaysmatch the observed data. The fact that the overall route run time is within 7% of the observed for the whole route across

6. SUMMARY

This note has presented the results of the validation for the BRT northThe model has been validated using data from the Lower Don Valley AIMSUN model, the SRPTM3 base year model and journey time survey information for existing bus services.

Figure 6.1 presents the run times for the Run Time Model, the AIMSUN Model and the observed data. This demonstrates the representing the inherent variation between these data sets

0:00:00

0:01:26

0:02:53

0:04:19

0:05:46

0:07:12

0:08:38

0:10:05

0:11:31

0:12:58

Observed data variation – PM peak period (16.00-19.00)


above show that there is significant variation in the run times gained from the observed surveys; the range of this variation is up to 3 minutes for a single segmentvariable delay along the route include:

Pedestrian crossings which operate on user demand;

The point in the cycle that the bus arrives at key signalised junctions;

Driver behaviour (if the driver waits until passengers are seated to accelerate);

Passenger behaviour at boarding (variation in the time to process different ticket typ

Number of passengers boarding per service;

Variable levels of traffic congestion across the peak periods.

This variable delay must be considered when comparing the results of the run time models to the averages from the observed data. This reinforces the potential difficulties in reading across between model formats that have used different observed data for model building and validation and where variation has been “smoothed out”. The run time models provide a ‘snap shot’ run time, based upon delays and speeds from SRTM3 and thus may not give run times which exactly match the observed data. The fact that the overall route run time is within 7% of the observed for the whole route across all time periods offers a reasonable level of confidence in the model.

This note has presented the results of the validation for the BRT northern route model has been validated using data from the Lower Don Valley AIMSUN model, the

year model and journey time survey information for existing bus services.

.1 presents the run times for the Run Time Model, the AIMSUN Model and the observed data. This demonstrates the overall goodness of fit between the different data sourcesrepresenting the inherent variation between these data sets.

Technical Note

Page 7 of 8

19.00)

ained from the for a single segment. Reasons for

Driver behaviour (if the driver waits until passengers are seated to accelerate);

Passenger behaviour at boarding (variation in the time to process different ticket types);

This variable delay must be considered when comparing the results of the run time models to the the potential difficulties in reading across

between model formats that have used different observed data for model building and validation The run time models provide a ‘snap shot’ run

and speeds from SRTM3 and thus may not give run times which exactly match the observed data. The fact that the overall route run time is within 7% of the observed for

offers a reasonable level of confidence in the model.

route Run Time Model. model has been validated using data from the Lower Don Valley AIMSUN model, the

year model and journey time survey information for existing bus services.

.1 presents the run times for the Run Time Model, the AIMSUN Model and the observed goodness of fit between the different data sources whilst still

Min

Average

Max

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Figure 6.1: Summary comparison of run time model

*SRPTM3 data not included in

Overall, the results show good validation of the Run Time Model, especially againstdata, with run times from the model for all directions and time periods observed data, following validationshown in this summary indicates a good level of validation in the longer journey times for the

.1: Summary comparison of run time models and observed data

*SRPTM3 data not included in Figure 6.1 as there are no directly comparable points for Vulcan Road and

show good validation of the Run Time Model, especially againstwith run times from the model for all directions and time periods exactly matching the

observed data, following validation. It is noted that the comparison with the SRTM3 model not shown in this summary indicates a good level of validation in the Sheffield direction but generally longer journey times for the Rotherham direction.

Technical Note

Page 8 of 8

and observed data

points for Vulcan Road and the Wicker.

show good validation of the Run Time Model, especially against the observed exactly matching the

the comparison with the SRTM3 model not direction but generally

appendix b26 - the run-time model€¦ · appendix b26 – the run time model this appendix...

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