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Freeway Performance Measurement System, PeMS v3, Phase 1: Final Report

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  • Varaiya, Pravin

Abstract

PeMS is a freeway performance measurement system for all of California. It processes 2 GB/day of 30-second loop detector data in real time to produce useful information. Managers at any time can have a uniform, and comprehensive assessment of freeway performance. Traffic engineers can base their operational decisions on knowledge of the current state of the freeway network. Planners can determine whether congestion bottlenecks can be alleviated by improving operations or by minor capital improvements. Travelers can obtain the current shortest route and travel time estimates. Researchers can validate their theory and calibrate simulation models. PeMS is a low-cost system. It uses the Caltrans network for data acquisition. It is easy to deploy and maintain. It takes under six weeks to bring a Caltrans district online. As of August 11, 2001, Districts 3,4,7,8,12 were connected to PeMS; District 11 will be connected by the end of August.

Suggested Citation

  • Varaiya, Pravin, 2001. "Freeway Performance Measurement System, PeMS v3, Phase 1: Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt20p1j2w7, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt20p1j2w7
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    Citations

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    Cited by:

    1. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    2. Zhu, Zheng & Li, Xinwei & Liu, Wei & Yang, Hai, 2019. "Day-to-day evolution of departure time choice in stochastic capacity bottleneck models with bounded rationality and various information perceptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 168-192.
    3. Divya Jayakumar Nair & Flavien Gilles & Sai Chand & Neeraj Saxena & Vinayak Dixit, 2019. "Characterizing multicity urban traffic conditions using crowdsourced data," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    4. Golob, Thomas F. & Recker, Wilfred W., 2003. "A Method for Relating Type of Crash to Traffic Flow Characteristics on Urban Freeways," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7n64466d, Institute of Transportation Studies, UC Berkeley.
    5. Yajun Ge & Jiannan Wang & Bo Zhang & Fan Peng & Jing Ma & Chenyu Yang & Yue Zhao & Ming Liu, 2024. "Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting," Mathematics, MDPI, vol. 12(19), pages 1-18, October.
    6. Varaiya, Pravin, 2001. "Freeway Performance Measurement System: Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2cx2x2s6, Institute of Transportation Studies, UC Berkeley.
    7. Golob, Thomas F. & Recker, Wilfred W. & Alvarez, Veronica M., 2003. "A Tool to Evaluate the Safety Effects of Changes in Freeway Traffic Flow," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1kn30323, Institute of Transportation Studies, UC Berkeley.
    8. Xiaoyuan Feng & Yue Chen & Hongbo Li & Tian Ma & Yilong Ren, 2023. "Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction," Sustainability, MDPI, vol. 15(9), pages 1-13, May.
    9. Zhong, Zijia & Lee, Joyoung, 2019. "The effectiveness of managed lane strategies for the near-term deployment of cooperative adaptive cruise control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 257-270.

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