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SWUTC Research Project Description

Novel Transit Signal Priority under the Connected Vehicle Framework

University:  Texas A&M University

Principal Investigator:
Kevin Balke
Texas Transportation Institute
(979) 845-9899

Project Monitor:
Mr. Abed Abukar
Dallas Area Rapid Transit

Funding Source:  USDOT and State of Texas General Revenue Funds

Total Project Cost: $56,625

Project Number:  600451-00014

Date Started: 4/1/12

Estimated Completion Date:  3/31/13

Project Summary

Project Abstract:
By providing signal priorities to the transit buses in need (i.e., the transit signal priority [TSP]) is an essential strategy to improve transit efficiency and reliability. This would require the knowledge of bus arrival time, current passenger load and adherence to schedule. Many current TSP systems utilized fixed sensors to detect or estimate the arrivals of buses. This conventional approach leaves it almost impossible to realize the full benefits of transit priorities due to the inability to account for variable dwell time, passenger load and schedules. Furthermore, many existing TSP models and algorithms cannot consider non-transit vehicles, thus excessive delays on private vehicles are common findings in literatures. The connected vehicle technology has emerged as a standard telecommunication framework used in mobile environments to facilitate information exchange. With the help of this communications tool, detail data from both the transit and passenger vehicles are available. In this project, we will formulate a framework that integrates the connected vehicle technology into traditional transit signal priority strategies. We will conduct a proof-of-concept testing of the prioritization framework using microscopic simulations. Lastly, we will determine the feasibility of prototyping local deployments.

Project Objectives:
There are two main objectives of the proposed study. First, we would like to develop a framework for integrating the connected vehicle technology into traditional transit signal priority strategies. Once the system is concluded feasible in lab testing, we will develop concept papers for prototyping the system and seek opportunities for local deployment.

Task Descriptions:
Task 1. Conduct Literature Review
In this task, we will perform a comprehensive review of literatures related to transit signal priorities. The review will focus on a number of specific topics, including but not limited to traditional strategies of transit signal priority, latest development of signal control methods with and without priorities, signal optimization methodologies and the standard communications messages available in the connected vehicle framework. The purpose of the review is to become familiar with the current practice of bus signal priority and will determine how improved strategies can be developed by using the emerging systems and technologies.

Task 2. Contact Signal Operators and Transit Service Providers
One important element of the transit signal priority is to understand under what conditions it is important for different types of transit vehicles to receive. We will contact transit providers and signal managers and discuss with them the general principles of signal operations with transit priorities. Through our conversations, we want to gain insight into the decision process operators used in determining when and where transit signal priority should be provided. Specifically, we hope to gain knowledge regarding the following:

  • what vehicle class types and levels are considered
  • what the current strategies to handle single and multiple priority requests are
  • whether the signal prioritization is done coordinately for multiple intersections along a bus route

These investigations will enhance the understanding of the important factors involved in a real-world transit priority problem. In addition, properly designed questions can help identify gaps between research and practice.

Task 3. Develop Control Framework
This task will need to develop an overall framework which specifies a practical TSP algorithm that manipulates signal controllers to grant priority to buses. In developing this framework, the scope of this project will be further refined. Based upon the reviews of current research and practice in task 1 and task 2, general assumptions and considerations will be laid out. A signal control strategy with transit prioritization will be developed in flow charts and the components of the control strategy will be modulated. This framework will identify the decisions to perform a number of priority actions (e.g., green extension, insertion, rotation, etc.) given a variety of intersection scenarios.

Task 4.Formulate Signal Optimization Model
Previous relevant research suggests mathematical programming models can serve as a good tool for a signal optimization problem. This task will formulate a mathematical program that represents the behaviors of the signal system subjected to a variety of constraints including the handling of transit priority. Specific priority considerations might include passenger loads, schedule/headway adherence, and peak direction of travel. Our plan is to take advantages of these transit data as well as those from the general traffic into considerations, and develop prioritizing strategies that can utilize the optimal results from the mathematical model.

Task 5. Develop Solution Algorithm
Given a good problem formulation developed in task 4, this task will identify existing methods or develop new algorithms that can solve the formulated mathematical program. The purpose of this task is to obtain an optimal timing for the study intersection(s) once all the inputs (e.g., traffic volume, priority requests) are given. To obtain the optimal results, this task will test several standard solution algorithms, such as Fletcher-Reevse algorithms, conjugate gradients methods or others appropriate to the problem. The most effective one will be incorporated into the control framework.

Task 6. Conduct Proof-of-Concept Testing
We are proposing to conduct proof-of-concept testing of the prioritization framework using microscopic simulations along a corridor, either a specific corridor in Bryan/College Station or in the Safety Pilot study corridor in Ann Arbor, MI. Using data from the corridor,  including signal timing, bus routes and schedules, and traffic demands, we will evaluate the prioritization algorithm. The proposed algorithm will be compared to existing means of providing transit priority with the goal of maximizing passenger throughput through a series of connected-vehicle equipped intersections. The proof-of-concept testing will illustrate the advantages to be obtained of using a connected vehicle environment over today’s infrastructure. The connected vehicle framework will be mimicked and the control framework will be coded through VISSIM API or COM interface. Different scenarios will be developed and the effectiveness of the control framework against these scenarios will be evaluated.

Task 7. Determine Feasibility of Prototype Deployment
We will develop a short concept paper for the TSP system developed in previous tasks. The concept paper briefly explains the system architecture in a plain language. The concept paper will identify communications messages and propose efficient message flows that support the operations of the TSP system. We will also conduct brief interviews with officials at TAMU and the city of College Station or in the US DOT designated Safety Pilot corridor to explore the feasibility of deploying a prototype locally for further testing and refinement. The feasibility study aims to answer at least some of the following questions:

  • What is the minimum requirement for the equipment (both signal and on transit vehicles)
  • Coordinated efforts from what local agencies are necessary to deploy and maintain the TSP system
  • What market penetrations of connected vehicles are required to ensure the effectiveness of the system

Task 8. Prepare Final Report
A final technical report will be prepared for this research project. Final research reports shall give a complete description of the problem, approach, methodology, findings, conclusions, recommendations, etc., developed in the project and shall completely document all data gathered, analyses performed, and results achieved.

Implementation of Research Outcomes:
This project explored a fundamentally new way to operate traffic signal systems to provide transit signal priority at signalized intersections in urban environments. With current techniques, transit vehicles are serviced on a first-come, first-serve basis, and each intersection is operated independently of the other intersection in the corridor. What frequently occurs in practice is that priority treatment can be given to a transit vehicle to has few riders and/or is less behind schedule than a later transit vehicle which is more in need of priority (i.e., has more full or is running more behind schedule) than the first vehicle. This approach attempts to optimize when, where, and how transit vehicles are treated in a corridor based upon estimates of their arrival time and level of need.

Products developed by this research include:

Model Developed – Researchers developed an model which used a stochastic mixed-integer nonlinear model (SMINP) as the core component of a real-time transit signal priority control system. The model adopts a novel approach to capture the impacts of the priority operation to other traffic by using the deviations of the phase split times from the optimal background split times. In addition, the model explicitly accounts for the randomness of a bus arrival time to the stop bar, by considering the bus stop dwell time and the delay caused by standing vehicle queues. The SMINP is implemented in a simulation evaluation platform developed using a combination of a microscopic traffic simulator and a commercial optimization solver. Comparison analyses were performed to compare the proposed control model with the state-of-the-practice TSP system (namely RBC-TSP). The results showed the SMINP has yielded as much as 30% improvement of bus delay comparing to RBC-TSP in single bus case. In multiple bus case, SMINP handles the bus priority request much more effectively under congested traffic conditions.

PresentationA Model for Transit Signal Priority Considering Stochastic Bus Arrival Time, Kevin Balke, presented at the 93rd TRB Annual Meeting, Washington, DC, January 2014.

PublicationA Model for Transit Signal Priority Considering Stochastic Bus Arrival Time, Kevin Balke, published in the Transportation Research Record:  the Journal of the Transportation Research Board.

Journal Article Submitted for ReviewNovel Transit Signal Priority under the Connected Vehicle Framework, Kevin Balke, accepted for publication in the IEEE Transactions on Intelligent Transportation Systems.

Impacts/Benefits of Implementation:
This research does have the potential to impact transit system planning and operations.  The research also has the potential to impact future development of traffic signal operating systems.

The results of this research are most likely to change the public’s attitude about the effectiveness of transit as an alternative mode of transportation for relieving congestion.

Web Links:

Final Report