Deriving Operational Traffic Signal Performance Measures from Vehicle Trajectory Data

Author:

Saldivar-Carranza Enrique1,Li Howell1,Mathew Jijo1,Hunter Margaret1,Sturdevant James2,Bullock Darcy M.1

Affiliation:

1. Civil Engineering, Purdue University, West Lafayette, IN

2. Indiana Department of Transportation, Indianapolis, IN

Abstract

Operations-oriented traffic signal performance measures are important for identifying the need for retiming to improve traffic signal operations. Currently, most traffic signal performance measures are obtained from high-resolution traffic signal controller event data, which provides information on an intersection-by-intersection basis and requires significant initial capital investment. Over 400 billion vehicle trajectory points are generated each month in the United States. This paper proposes using high-fidelity vehicle trajectory data to produce traffic signal performance measures such as: split failure, downstream blockage, and quality of progression, as well as traditional Highway Capacity Manual level of service. Geo-fences are created at specific signalized intersections to filter vehicle waypoints that lie within the generated boundaries. These waypoints are then converted into trajectories that are relative to the intersection. A case study is presented that summarizes the performance of an eight-intersection corridor with four different timing plans using over 160,000 trajectories and 1.4 million GPS samples collected during weekdays in July 2019 between 5:00 a.m. and 10:00 p.m. The paper concludes by commenting on current probe data penetration rates, indicating that these techniques can be applied to corridors with annual average daily traffic of ~15,000 vehicles per day for the mainline approaches, and discussing cloud-based implementation opportunities.

Funder

Joint Transportation Research Program and Pooled Fund Study

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Reference28 articles.

1. Day C. M., Bullock D. M., Li H., Remias S. M., Hainen A. M., Freije R. S., Stevens A. L., Sturdevant J. R., Brennan T. M. Performance Measures for Traffic Signal Systems: An Outcome-Oriented Approach. Purdue University, West Lafayette, IN, 2014. https://dx.doi.org/10.5703/1288284315333.

2. Day C. M., Taylor M., Mackey J., Clayton R., Patel S. K., Xie G., Li H., Sturdevant J. R., Bullock D. Implementation of Automated Traffic Signal Performance Measures. ITE Journal, Vol. 86, No. 8, 2016, pp. 26–34. https://mydigitalpublication.com/publication/?m=19175&i=324591&view=articleBrowser&article_id=2544747&ver=html5.

3. Day C., Bullock D., Li H., Lavrenz S., Smith W. B. B., Sturdevant J. Integrating Traffic Signal Performance Measures into Agency Business Processes. Purdue University, West Lafayette, IN, 2015. http://dx.doi.org/10.5703/1288284316063.

4. National Academies of Sciences, Engineering, and Medicine. Performance-Based Management of Traffic Signals. Transportation Research Board, Washington, D.C. 2020. https://www.nap.edu/catalog/25875. Accessed July 28, 2020.

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