Anna Matías Alemán

January 15, 2013

Simulation of Transit Signal Priority

Strategies for BRT Operations

Outline

Motivation

Background

Objectives

Methodology

Evaluation Approach

Boston Case Study

Next Steps

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Why TSP?

A technique to provide priority to public transport services

through traffic lights

A means to improve performance of public transportation

systems at the operational level

A strategy aiming at reducing travel times and improving bus

service reliability

When properly designed for BRTs it can complement their

other features such as exclusive bus lanes, off-vehicle fare

collection, all-door boarding, etc., and potentially contribute

to improved system performance

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Types of Priority

Unconditional – priority is always provided

Conditional – priority is provided only when certain

conditions are met (schedule adherence, vehicle load, etc.)

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TSP Actions

With traditional signal controllers, the actions that can be used

when a vehicle is detected are:

Extension of the current green interval

Early start of the green interval

Skipping a phase

Inserting an extra phase

Previous Studies in TSP The majority of studies in TSP are through simulation experiments

It has been found that it can accomplish travel time reductions of

0-42% (Chang et al, 1995)

But, as the buses benefit from this, cross-traffic and traffic light

synchronization can be negatively affected

Studies have shown that providing priority to all buses can

significantly affect the overall traffic, therefore, providing priority

to express routes -or select buses conditionally- could result in higher

benefits (Dion and Rakha, 2005)

Studies indicate that it may work well in arterial intersections in

urban areas if properly planned and designed

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Background Research

Work to date has focused mainly on conventional bus

systems; the study of TSP for BRT corridors is limited

A recent simulation study of schedule-based TSP for a BRT

line in Beijing

Another study of TSP using speed guidance and advanced

detection for a BRT line in Yingtan City

But BRT corridors present a number of challenges and

operating characteristics, which are different from

conventional corridors and are worth considering (high

frequencies of service, great levels of demand, etc.)

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Research Objectives

The objective of the proposed research activity is to study how TSP can benefit BRT systems and can best be incorporated into their operations both in the U.S. and in developing countries. The main tasks include the following:

review literature and experience with TSP with both conventional and BRT systems, and document the main conclusions and lessons learned;

determine how TSP can best be implemented in BRT corridors with different characteristics in terms of demand levels, frequency of service, etc.;

evaluate TSP strategies that consider different conditions (schedule, headways, loading, etc.) in a BRT context; and

develop guidelines for implementation of signal priority strategies in BRT corridors, based on their characteristics. 8

Data Sources

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Automated Passenger Counting Records (APC)

Automatic Vehicle Location Records (AVL)

Signal Timing Plans

Traffic count studies/O-D Matrix from Planning Model

Vehicle Specifications

Data Preparation

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Traffic Data

Road network – coded or from a geographic file

Traffic Flows – turning movements inputted from traffic counts

or O/D matrix generated with Cube Analyst

Signal Timing Plans

Data Preparation

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Transit Data

Transit Routes delineated with defined route physical stop

Average Departing headways and standard deviation calculated

from AVL Records

Average Arrival rates and Alighting Percentages per stop

calculated from APC Records

Vehicle type defined with capacity and dwell time parameters

Average Initial load calculated for the segment of the route to

be simulated, if necessary

Dwell Time Model

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Passengers are not really modeled in the simulation, but rather their effect on dwell times, which will be a function of the number of passenger boardings and alightings, using parameters that account for crowding and boarding and alighting times per passenger.

Dwell time model:

T=ɣ + αA + βB if there is no crowding

T=ɣ + αA + βB’’ + (β +CF) B’’’ if there is crowding

These parameters are defined by vehicle class to define the dead time component and the service time component of the dwell time. Therefore, the vehicle class will have a defined seating capacity, total capacity, dead time, alighting time, boarding time, and crowding factor.

Actions Evaluated

Green Extension

Red Truncation

Phase Skipping (only if it is not a major cross-street) 13

Conditional Priorities Evaluated

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Schedule deviation (a bus must be running late) on all intersections -Headway limitation: 2 cycles & Lateness Treshold: 1min

Combined schedule deviation and minimum passenger load on all intersections (a bus must be late and have the requisite number of passengers to be eligible for priority)

Schedule deviation on all intersections with the minimum passenger load constraint only on critical intersections (cross-streets with high volumes or high-frequency bus routes)

Schedule deviation on all intersections except on critical intersections (no priority will be provided in cross-streets with high volumes or high-frequency bus routes)

*Will be evaluated in current conditions and other projections

Evaluation Metrics

Impact on average and variability of bus travel times

Impact on average bus speeds

Impact on general traffic speed

Headway variability

Intersection delays

Crowding

Vehicle Emissions (in the long run)

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The cities

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Case Studies

Boston Silver Line 5 – Washington Street

Limited bus-only lanes, 6 min headways during peaks, +15,000 weekday boardings

Minneapolis Route 10 – Central Avenue

Limited bus-only lanes, 12 min headways during peaks, +7,300 weekday boardings

Santiago Routes 204 and 204 e – Carmen Avenue

Limited bus-only lanes, 4 min headways during peaks ,+15,000 weekday boardings

Many assumptions were made (fleet, frequencies, lanes, etc)

Boston

• Silver Line 5 - Washington Street

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Boston Case Study – SL5

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Applied Conditional TSP (schedule-based) at “major” signalized intersections:

Melnea Cass Boulevard

Mass Ave – outbound

East Berkeley St.

Herald St.

Operating Scheme:

Bus computer sends location to MBTA’s Bus Control Center

Bus Control Center checks if it is behind schedule and sends signal to hardware in kiosk on the side of the intersection

Hardware in kiosk sends contact closure signal to the intersection signal controller which passes the signal to the BTD computer system

BTD decides to grant priority (green extension or early green)

Boston Case Study – SL5

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OTP Without TSP

Boston Case Study – SL5

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OTP with TSP

Next Steps

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Finish gathering then inputs for the corridors

Evaluate the effects of the different strategies on each

corridor

Project the scenarios to higher levels of demand and

frequency (and visualize the effects of having more than one

request per cycle)

Generate conclusions depending on corridor characteristics

Thank you!

Questions? Suggestions?