presentation atm

Multi-Resolution Modeling of Active Traffic

Management on Urban Streets

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By: Aidin MassahiMajor Advisor: Dr. Mohammed Hadi

Committee Members: Dr. Albert Gan

Dr. Xia Jin

Dr. Hesham Ali

Dr. Yan Xiao

Dr. Zhenmin Chen

Dissertation Proposal Defense

March 28, 2016

AGENDA

INTRODUCTION

LITERATURE REVIEW

METHODOLOGY

RESEARCH TASKS

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INTRODUCTION

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Background ATM providing significant benefits in terms of travel time, travel time reliability,

emission, fuel consumption and safety

What ATM strategies are the most advantageous

Multi-resolution model (MRM) is an integrated approach that combines different

modeling levels in the assessment of ATM strategies

Dissertation develops and uses methods for the use of MRM to support agency decisions

related to ATM strategies deployment on urban streets and comparing the benefits of

these strategies compared to capacity improvements

INTRODUCTION

Recent interests in assessing the benefits of ATM strategies on urban streets

MRM is simulation-based Dynamic Traffic Assignment (DTA) DTA is capable of realistic modeling of traffic flow and driver responses

DTA model the time-dependent network states

DTA vehicle trajectories output can be processed to produce more detailed statistics

The main benefit of ATM is to improve reliability Reliability concept requires the assessment of the impacts of variations in demand weather, congestion,

incident, and other events on system performance

Scenario-based analysis that has been used for reliability assessment will be extended to evaluating other

performance measure including mobility, safety, and environmental impacts

Estimating performance is to base the performance on analyzing vehicle

trajectories

Reduce the dimensionality of generated scenarios Scenario Generations requires a large number of simulations analyzing by clustering and grouping analysis

patterns into representative patterns4

Motivation of the Study

INTRODUCTION

Goal: Develop and research methods for assessing the impacts of ATM

strategies on urban streets

Objective1- Develop methods that utilize combinations of advanced simulation and DTA models to allow the effective assessments of ATM strategies in terms of their impacts on mobility, reliability, safety, and environmental performance measures

Objective2- Compare the methods developed according to Objective 1 above with the results obtained from the assessment of ATM strategies using simple sketch planning procedures to justify the need for the more detailed assessment of simulation models

Obective3- Demonstrate the use of the developed methods for assessing the benefits of implementing ATM strategies in a real-world implementation

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Research Goal and Objectives

LITERATURE REVIEW

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Adaptive Ramp Metering Adaptive Traffic Signal Control Dynamic Junction Control Dynamic Lane Reversal / Contraflow Lane Reversal Dynamic Lane Use Control Dynamic Merge Control Dynamic Shoulder Lane Variable speed limits(VSL) Queue Warning Transit Signal Priority

Active Traffic Management (ATM) Strategies

LITERATURE REVIEW

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Multi-Resolution Analyses of Advanced Traffic Management Strategies

Sketch Planning

Macroscopic Simulation

Mesoscopic Simulation

Microscopic Simulation

LITERATURE REVIEW

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Sketch Planning Florida ITS Evaluation Tool (FITSEVAL)

Ramp Metering, Incident Management Systems, Highway Advisory Radio (HAR) and Dynamic

Message Signs (DMS), Advanced Travel Information Systems (ATIS), Managed Lane, Signal

Control, Emergency Vehicle Signal Preemption, Smart Work Zone, Road Weather Information

Systems, Transit Vehicle Signal Preemption, Transit Security Systems, Transit Information Systems

and Transit Electronic Payment Systems

The evaluation methodology implemented in FITSEVAL:

Postprocessor of demand model

Running assignment steps

TOPS-BC

Highway advisory radio (HAR), dynamic message signs (DMS), pre-trip travel information, ramp

metering systems, incident management systems, signal control, emergency vehicle signal

preemption , ATDM speed harmonization employer based traveler demand management ATDM

hard shoulder running, ATDM high occupancy lanes, road weather management, work zone

LITERATURE REVIEW

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Macroscopic Models Macroscopic models can be used with and without traffic assignment

Regional Demand Forecasting Models

Highway Capacity Manual (HCM)- Based Tools

STREETVAL

FREEVAL

HCS

VISUM

VISSUM has static assignment and DTA modules

VISUM traffic model considers spillback

VISUM has an (ODME) tool based on initial O-D matrices and count data

LITERATURE REVIEW

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Multi-Resolution Analyses

Mesoscopic Models DYNASMART

Demand Inputting Methods:• Time-variant O-D matrices among origin-destination • Vehicle loading method, requires inputting the origin and destination of each vehicle zones

DynusT Model shows more realistic representation of traffic flow compared to the original

Dynasmart model DTALite

Working in combination with the Network Explorer for Traffic Analysis (NEXTA) graphical user interface

DTALite’s Output data can be visualized using the NEXTA user interface Dynameq

Dynameq is its more detailed simulation models Capable to model lane-by-lane traffic Simulation model is considered as event-based simulation

Cube Avenue Simulation-based DTA extension of the Cube Voyager demand forecasting environment Vehicles are clustered into homogenous “packets” and simulated as they move through the

network

LITERATURE REVIEW

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Multi-Resolution AnalysesMicroscopic Models CORSIM

CORSIM does not have DTA model and requires the users to input turning movement counts

CORSIM and TRANSYT-7F, signal optimization program offered as one combined product

CORSIM is able to model incidents directly

Paramics Used to model ITS alternatives including variable speed limits (VSL), high occupancy tolling

(HOT), vehicle actuated signals, incident response, HOV lanes, dynamic lane control, route

choice updates, roadside message signs, and car parking signs

SimTraffic Utilized with the Synchro signal optimization tool to optimize signal timings of signalized

facilities

SimTraffic incorporates a more user-friendly interface that greatly eases network coding

requirements

LITERATURE REVIEW

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Multi-Resolution AnalysesHybrid Mesoscopic-Microscopic Model

AIMSUN AIMSUN recommended for modeling ITS applications

Microscopic Simulator Software Development Kit (microSDK), allowing users to override

default behavioral models

AIMSUN Platform Software Development Kit (platformSDK) can develop new interface for

ITS applications

TransModeler TransModeler is capable to model parts of the network at the microscopic level and parts of the

network at the mesoscopic and/or macroscopic simulation level in the same run

VISSIM VISSIM has a powerful programing extension, allowing modelers to program advanced

managements and pricing strategies

Utilize link-connector structure allowing for increasing accuracy & flexibility of modeling

LITERATURE REVIEW

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Multi-Resolution AnalysesHybrid Mesoscopic-Microscopic Model

AIMSUN AIMSUN recommended for modeling ITS applications

Microscopic Simulator Software Development Kit (microSDK), allowing users to override

default behavioral models

AIMSUN Platform Software Development Kit (platformSDK) can develop new interface for

ITS applications

TransModeler TransModeler is capable to model parts of the network at the microscopic level and parts of the

network at the mesoscopic and/or macroscopic simulation level in the same run

VISSIM VISSIM has a powerful programing extension, allowing modelers to program advanced

managements and pricing strategies

Utilize link-connector structure allowing for increasing accuracy & flexibility of modeling

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INCIDENT MANAGEMENT

The main elements of incident management include the incident detection, incident verification, response selection, incident removal, traffic management, and the provision of traveler information

Program Improvement Impacts

CHART program, MD

Detection, verification, and service patrols

• Incident reduction from 77 minutes to 33 minutes• Reduced the blockage duration from incidents by 36%. This translates to a reduction in highway user delay time of about 42,000

hours per incident• 15% to 38% reduction in all secondary crashes; 4% to 30% reduction in rear-end crashes; and 21% to 43% reduction in severe

secondary crashes

Atlanta, GANAVIGATOR system

Detection, verification, and service patrols

• Reduced 5.775 kg of hydrocarbons (HC), 75.58 kg of carbon monoxide (CO) and 8.059 kg of nitrous oxides NOx per incident• Reduced incident clearance time by an average of 23 minutes and the incident response time by 30% • Average time between first report and incident verification was reduced by 74%• Average time between verification and response initiation reduced by 50%• Average time between incident verification and clearance of traffic lanes reduced by 38%.• Maximum time between incident verification and clearance of traffic lanes was reduced by 60%

San Antonio, TXTech Program

Incident detection and verification using CCTV

• Improved the response time by 20 % (21% reduction for major incidents and 19% for minor incidents)

Brooklyn, NY Detection, verification, and service patrols

• Reduced the incident clearance average time by 66%• Reduced the average incident clearance time from 1.5 hours to 31 minutes

Minneapolis, MN Automatic tow truck dispatch program • Decreased the incident response and removal times by 20 minutes (85% improvement)

San Francisco, CA Service patrol implementation

• Reduced average response time from 28.9 minutes to 18.4 minutes (36 percent) • Reduced clearance time from 9.6 minutes to 8.1 minutes (16 percent) • Total delay saving per assisted breakdown was 42.4 vehicle-hours • Total delay savings per assisted accident was 20.3 vehicle-hours per incident

Houston, TX TrsnsGUide Service patrol implementation

• Reduced total duration of incident by 16.5 minutes• Dropped the average incident duration by 30%

Denver, CO Service patrol implementation • Reduced total duration of incident by 10.5 minutes

Pittsburgh, PA Service patrol implementation • Reduced response time to incidents from 17 to 8.7 minutes

Gresham, OR Service patrol implementation • Shortened the delay-causing incidents by approximately 30% on two lane Highway and 17% on Interstate

Northern, VA • Cell phone in response vehicles• CAD screens in response vehicles• GPS location in response vehicles

• Reduced the duration for all incidents by 2 to 5 • Reduced the duration for all incidents 2 to 5 minutes due • Reduced the duration for all incidents 4 to 7 minutes

The Florida DOT, District IV, FL

Detection, verification, and service patrols • The incident duration is reduced by 18 %

ITS Deployment Analysis System (IDAS)

• incident detection & verification• incident response & management • Combination detection &

management

• Incident duration reduction of 9% • Incident duration reduction of 39% • Incident duration reduction of 51%• 21 percent of fatalities are shifted to injuries

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Incident and Incident Management Modeling in The Tools CORSIM

Specific frame to model incident on freeways Drop the capacity in the vicinity of a freeway incident (using a rubberneck factor and the

warning sign location)

AIMSUN and VISSIM Specifying stopped bus with bus dwell time Set up a red signal at the incident lane Used the “Add vehicle” function, within the VISSIM’s COM interface

TOPS-BC Spreadsheet-Based Tool1. Travel time reliability improvement 2. Fatality crash reductionImprovement in travel time reliability is calculated as the reduction in incident-related delays

FITSEVAL ToolDiversion rate is set as a function of the estimated saved delays21% of fatalities are shifted to injuries Additional reduction factor of 2.8 % is used to account for IM on accident Reduction in incident delay is calculated based on queuing analysis Incident delays on the arterials are 1.25 higher than freeway

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Adaptive Signal Control The adaptive control software adjusts traffic signal splits, offsets, phase lengths, and

in some cases phase sequences to minimize delay and reduce the number of stops

Improvement Location Impacts

Los Angeles, CA• Decreased travel time by 12.7 percent• Reduced average stops by 31.0 • lowered average delay by 21.4 percent

Gresham, OR • Reduced the average travel times by 10 percent • Saved over 74,000 gallons of fuel every year

Lee's Summit, Mo

• Average travel times decreased on the mainline up 39 percent • Number of vehicle stops decreased by 17 percent to 95 percent per trip• Average vehicle speeds improved 5 to 10 mile per hour• Fuel consumption ranged between 4.5 percent increase and a 21.4

percent decrease• Changes to pollutants (HC, CO, and NOx) emission varied from a 9

percent increase to a decrease of 50 percent

Two corridors in CO• Improved weekday travel times 6 to 9 percent• Increased weekday average speed 7 to 11 percent • Decreased weekday stopped delay 13 to 15 percent

Oakland County, MI

• Reduced travel time by 7 percent in the morning peak and 8.6 percent during evening peak periods

• Off peak and non-peak direction travel times were improved by 6.6 to 31.8 percent

New York City, NY •A 10 percent reduction in travel times

Detroit, MI •Total crashes per mile per year decreased by 28.8%

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Time of Day Signal Control Retiming Signal timing strategies try to minimize stops, delays, fuel consumption and air

pollution emissions and maximize the traffic progression through the systemImprovement Location Impacts

Syracuse, NY

• Reduced the number of stops by 15.7 percent, travel time by 16.7 percent, and delay by 18.8 percent• 13.8 percent decline in fuel consumption• A 13 percent reduction in vehicle emissions and noise pollution• Decreased vehicular delay by 14 to 19 percent• Reduced total stops by 11 to 16 percent• Improved average speed by 7 to 17 percent

Oakland County, MI •Reductions between 1.7 and 2.5 percent in Carbon monoxide •1.9 to 3.5 percent in Nitrogen oxide•2.7 to 4.2 percent reduction in hydrocarbon

Texas Traffic Light Synchronization program•Reduced delays by 23 percent•lowered travel time by 14 percent•Reduced fuel consumption by 9.1 percent

U.S. Route 1, St. Augustine, FL•Reduced delay by 36 percent•Lowered travel time by 10 percent•Annual fuel savings of 26,000 gallons

State Route 26, Gainesville, FL •Reduced the average delay by 94 percent •Saved 3,300 gallons in fuel consumption annually

Burlington, Canada •Travel time was shortened by 7 percent•Fuel consumption was decreased by 6 percent

Montgomery County, MD •lowered delay by 13 percent •Reduced fuel consumption by 2 percent

FETSIM Program, California •Deceased delay by 15 percent •Fuel consumption by 8.6 percent

Lee County, FL •A 23 percent annual reduction in travel delays, causing $15,300,000 in travel time savings•$2,000,000 per year in fuel savings•Reduced vehicle emissions by 19 percent, resulting in an equivalent to $124,000 environmental benefits

Tysons Corner, VA •A 9 percent reduction in fuel consumption

Southwestern Pennsylvania Commission's (SPC) Regional Traffic Signal System

•Average travel times were shortened by 6 percent•Average stops lowered by 6 percent•Average signal delay decreased by 16 percent

US-31, Kokomo, IN •Saved 16,322 hours of travel time •Reduced 982 tons of CO2

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Impact of Signal Timing Strategies During Incident

New signal planning can increase the roadway capacity during arterial incidents and diversion due to freeway incidents

Give priority to specific movements in order to minimize the overall delay

Increase or decrease the throughput of traffic at certain intersections by increasing or decreasing the green times for those movements

Modifying signal timing can be combined with traveler information that guide motorists to alternative routes


CHART program, MD • Total delay time reduction of 30 million vehicle-hours• A total fuel consumption reduction of 5 million gallons

Fargo, ND •Improve travel times by 18 percent •Increase speeds by 21 percent

Detroit, MI •Reduced delay by 60 to 70 percent for the affected paths

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Weather-Response Signal Control Atmospheric events can decrease the efficiency of traffic signals

Adverse weather can reduce visibility and pavement friction

Readjusting signal timing plans is expected to mitigate delays due to severe weather effects

Signal adjustment would consider the increasing headways between vehicles in inclement weather


Minneapolis, MN • A 8 % reduction of signal delay for each vehicle • A 6 % reduction in average stops

Ogden, UT

•Reduced the cumulative travel time by 4.3 percent•11.2 percent reduction in the cumulative stop time•Travel times of cross-street improved by 3 percent•Overall cross-street stopped times decreased by 14.5 percent

Charlotte, NC

•Reduction in rear-end conflicts of approximately 22 percent for moderate volume levels •Reduction in rear-end conflicts of approximately43 percent for high volume levels

LITERATURE REVIEW

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Incorporation Travel Time Reliability Travel time reliability evaluation is critical to the assessment of ATDM strategies Travel time Reliability Indices Source of Travel Time Unreliability

I. Supply side Incidents Work Zones Weather Traffic ControlManagement Dynamic PricingVariation In Individual Driving Behaviors

II. Demand sideSpecial EventsDay-to-day Variation In Individual BehaviorsUnfamiliar Users

Reliability Performance

MetricDefinition Project Using Measure

Buffer IndexBuffer Index The difference between the 95th percentile travel time and the average travel time,

normalized by the average travel timeL03, L08

Failure/On-Time

Performance

Percentage of trips with travel times less than

1.1 x median travel time

1.25 x median travel time

Or percentage of trips with speed less than 50, 45, 40 or 35 mph

L03, L08

95th Percentile PTI95th percentile of the TTI distribution (95th percentile travel time divided by the free-flow

travel time)L03, L08

80th Percentile TTI80th percentile of the TTI distribution (80th percentile travel time divided by the free-flow

travel time)L03, L08

Skew StatisticsThe ratio of 90th percentile travel time minus the median travel time divided by the median

travel time minus the 10th travel time percentileL03

Misery Index The average of the highest 5% of travel times divided by the free-flow travel time L03

Standard Deviation Usual statistical definition L08

Kurtosis Usual statistical definition L08

LITERATURE REVIEW

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Incorporating Reliability into Operations Modeling Tools Scenario Manager : Capture exogenous unreliability sources I. Scenario Specification

Defining the spatial and temporal boundaries for which travel time variability is examined

Time-of-day selection for the scenario time horizon Determining the analysis approach

Selecting scenario components of interest II. scenario generation

Scenario generation aims to determine the occurrence of incidents Simulation Tools:

Model endogenous sources of demand unreliability Vehicle Trajectory Processor:

Extracts reliability information from the simulation output Presents both O–D-level and path-level travel time statistics such

as average and standard deviation

METHODOLOGY

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Utilized Framework

METHODOLOGY

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Data Sources and Tools

Speed, volume count, occupancy measurements, as well as associated derived measures such as queue length and travel time estimates

Partial origin-destination and travel time data

Travel time and origin-destination data

Incident data such as incident frequency, temporal, spatial and intensity

Weather data

Signal control data

ATM parameters

METHODOLOGY

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Network preparation for Multiresolution Analyses

Step 1- Subarea network and demand matrix extraction

Step 2-Importing the extracted network and the demand into NeXTA

Step 3-Network Modification

Step 4-Demand Estimation

METHODOLOGY

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Developing a Methodology to Assess the Impacts of ATM Strategies

Simulation Platform Scenario manager and trajectory procedure models will interface with DTAlite

to produce the varies types of performance measures for each ATM strategies Synchro/SimTraffic tool will be used to optimized the signal controls and to

allow the emulation of different signal timing strategies SimTraffic microscopic simulation will be used as need it to simulate more

detail specific facilities in the network

METHODOLOGY

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Development and Implementation Scenario Manager

scenario specificationDefine scenario components

I. Travel demand variation between daysII. External event (Incident, Weather)III. Implemented ATM strategies

Determine analysis approach I. Day-to-day variation (clustering analysis)II. Weather (grouping analysis based on HCM2010 approach)III. Incident (clustering analysis based on frequency, duration, lane blockage)

Defining the spatial and temporal boundaries I. Determine incident locations on weekdays

Time-of-day selection for the scenario time horizonI. Morning peak period

METHODOLOGY

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Development and Implementation Scenario Manager Scenario Generation A k-mean clustering analysis will be used to group the real-world

demands between days into different traffic patterns Rain intensity classes

I. No Rain and Light Rain (precipitation rate<0.1 inch/hr) II. Medium Rain (0.1 inch/hr <precipitation rate<0.25 inch/hr) III. Heavy Rain (precipitation rate>0.25 inch/hr)

Incident will consider the location, attributes, and duration of the incidents

METHODOLOGY

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Trajectory Processor Allow analyzing the DTAlite simulation results ATM strategies and bundles under different demand/incident and weather

condition will be assessed Network-level O–D level Path level

Outputs analysis simulation results Mobility Reliability Safety Sustainability

RESEARCH TASKS

Review Additional Literature

Data Collection Processing, Network Preparation and Calibration

Scenarios’ Implementations and Simulation

Performance Measures Estimation

Draft Dissertation Preparation and Submission

Final Dissertation Defense, Revision, and Submission

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Schedule for Research Tasks

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THANK YOU QUESTIONS

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