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Freeway Performance Measurement System (PeMS)

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  • Chen, Chao

Abstract

The freeway Performance Measurement System (PeMS) collects real time traffic data from sensors and generates performance measures of vehicle miles traveled, hours traveled, and travel time. This project is sponsored by the California Department of Transportation (Caltrans). PeMS provides tools and reports for traffic planners, operators, and engineers. It has a Web interface. Growing traffic demand in metropolitan areas has far outpaced increases in freeway lane-miles in the United States. The solution to congestion lies in increasing the efficiency of existing infrastructure. Performance measurement is the first step in effective management and operation of any system. Currently, the freeway system is not managed scientifically. Planning and operating decisions are made without accurate knowledge of the performance of each part of the system. PeMS collects data from automatic sensors that are already installed on most of California freeways. Its large database of real time and historical data 2 allows us to accurately measure the performance of freeways and its trends. Traffic planners need this information to allocate the available resources to improve mobility. PeMS computes performance measures and other traffic quantities from sensor data. Among them are speed, vehicle-hours of delay, vehicle-miles traveled, and travel time statistics. These values can be visualized in plots and summarized in reports, and they are available online through a Web interface. Policy makers can use PeMS to evaluate the effect of their decisions and set performance targets, planners monitor trends in congestion and respond with congestion-reduction measures, engineers view detailed data to improve conditions at specific locations, and travelers use the information to make more informed decisions. Researchers use PeMS's database to analyze traffic behavior on a large scale. We present some results from studies on freeway capacity, travel time variability, and the impact of incident on overall delay. In these cases, using observations from a large number of locations and times allows us to characterize traffic flow statistically. PeMS processes raw data into useful forms. It computes speed from single loop detectors, predict travel time from real time and historical data, and detect and fix data errors. We describe these data processing algorithms, which are based on empirical models and fitted to historical data.

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  • Chen, Chao, 2003. "Freeway Performance Measurement System (PeMS)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6j93p90t, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt6j93p90t
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    1. Dailey, D. J., 1999. "A statistical algorithm for estimating speed from single loop volume and occupancy measurements," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 313-322, June.
    2. Eugene L. Lawler, 1972. "A Procedure for Computing the K Best Solutions to Discrete Optimization Problems and Its Application to the Shortest Path Problem," Management Science, INFORMS, vol. 18(7), pages 401-405, March.
    3. Cassidy, Michael J. & Bertini, Robert L., 1999. "Some traffic features at freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 33(1), pages 25-42, February.
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    Cited by:

    1. Coifman, Benjamin, 2004. "Distributed Surveillance and Control on Freeways," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2wx1d9ck, Institute of Transportation Studies, UC Berkeley.
    2. Farnaz Khaghani & Farrokh Jazizadeh, 2020. "mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    3. Jinwon Kim & Jucheol Moon & Dongyun Yang, 2024. "Pigouvian Congestion Tolls and the Welfare Gain: Estimates for California Freeways," Working Papers 2402, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    4. Steven Sexton, 2012. "Paying for Pollution? How General Equilibrium Effects Undermine the “Spare the Air” Program," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 53(4), pages 553-575, December.
    5. Bencsik, Panka & Lusher, Lester & Taylor, Rebecca L.C., 2025. "Slow traffic, fast food: The effects of time lost on food store choice," Journal of Urban Economics, Elsevier, vol. 146(C).
    6. Ioannou, Petros & Giuliano, Genevieve & Dessouky, Maged & Chen, Pengfei & Dexter, Sue, 2020. "Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes," Institute of Transportation Studies, Working Paper Series qt3ns4b894, Institute of Transportation Studies, UC Davis.
    7. Rindt, Craig R. & McNally, Michael G., 2009. "Cartesius and CTNET Integration and Field Operational Test," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1qn7q6zf, Institute of Transportation Studies, UC Berkeley.
    8. Brownstone, David & Chu, Lianyu & Golob, Tom & Nesamani, K.S. & Recker, Will, 2008. "Evaluation of Incorporating Hybrid Vehicle Use of HOV Lanes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5c81b9vv, Institute of Transportation Studies, UC Berkeley.
    9. Dongqing Zhang & Zhaoxia Guo, 2019. "On the Necessity and Effects of Considering Correlated Stochastic Speeds in Shortest Path Problems Under Sustainable Environments," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    10. Sexton, Steven E., 2010. "Rationing Public Goods by Cooperation or Pecuniary Incentives: Evidence from the Spare-the-Air Program," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5xs9r6t8, Department of Agricultural & Resource Economics, UC Berkeley.
    11. Hongwen He & Jinquan Guo & Nana Zhou & Chao Sun & Jiankun Peng, 2017. "Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-19, November.
    12. Ioannou, Petros & Chen, Pengfei, 2023. "Centrally Coordinated Schedules and Routes of Airport Shuttles with LAX Terminals as Application Area," Institute of Transportation Studies, Working Paper Series qt6gg7r6c5, Institute of Transportation Studies, UC Davis.
    13. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
    14. Kim, Jinwon, 2022. "Does roadwork improve road speed? Evidence from urban freeways in California," Regional Science and Urban Economics, Elsevier, vol. 93(C).
    15. Sumalee, A. & Zhong, R.X. & Pan, T.L. & Szeto, W.Y., 2011. "Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 507-533, March.

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