Data Collection for Measuring Performance of Integrated Transportation Systems

  • Wei-Bin ZhangEmail author
  • Alex Skabardonis
  • Meng Li
  • Jingquan Li
  • Kun Zhou
  • Liping Zhang
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 144)


More than ever, traffic congestion is plaguing our heavily populated metropolitan areas. Transportation professionals have recognized that we cannot build our way out of this ever-increasing congestion. The challenge over the next decade is to get more out of the existing transportation system by improving its productivity. To address this challenge, we must evolve into “system managers”: agencies and individuals who manage the system through operational strategies, complemented by targeted expansion investments. The concept of system management has been embraced by many agencies at both state and federal levels. For example, the California Department of Transportation (Caltrans) and most of its stakeholders adopted the concept of the System Management pyramid, as depicted in Fig. 3.1. The foundation of system management is “System Monitoring and Evaluation”. This foundation provides support for a variety of informed investment decisions.


Traffic Signal Transit Service Travel Time Estimation Transit Vehicle Transit Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Kai Leung of Caltrans’ Division of Traffic, Paul Chiu, James Lau, and Lindy Cabugao with Caltrans District 4, Sonja Sun, Don Dean, and Greg Larson of Caltrans’ Division of Research and Innovation, Frank Burton of Samtrans, Fanping Bu, Yue Li, and Scott Johnston of California PATH, Guoyuan Wu, Offer Grembek, Amy Wang, Peter Lau, Joe Liu, and Yao Ma of University of California at Berkeley for their contributions and support toward the Parsons T2 Lab.


  1. Choe T, Skabardonis A, Varaiya P (2002) Freeway performance measurement system (PeMS): an operational analysis tool, transportation research record #1811, Washington, DCGoogle Scholar
  2. Geroliminis N, Skabardonis A (2006) Real time vehicle re-identification and performance measures on signalized arterials. IEEE intelligent transportation systems conference (ITSC) proceedings, Toronto, CA, pp 188–193Google Scholar
  3. Li M, Zou ZJ, Bu FP, Zhang WB (2008) Application of vehicle-infrastructure integration (VII) data on real-time arterial performance measurements, under review by Transportation Research, Part CGoogle Scholar
  4. Li M, Song MK, Gu GY (2009) An online performance measurement method based on arterial infrastructure data. Paper presented at the 88th annual meeting of the transportation research board, Washington, DCGoogle Scholar
  5. Skabardonis A, Geroliminis N (2005) Real time estimation of travel times along signalized arterials. Proceedings of 16th international symposium on transportation and traffic theory, ElsevierGoogle Scholar
  6. Smith HR, Hemily B (2005) Transit signal priority, a planning and evaluation handbook. ITS America, Washington, DCGoogle Scholar
  7. Varaiya P (2006) Freeway performance measurement system (PeMS), PeMS 6. Final report for CCIT TO 15Google Scholar
  8. Zhang LP, Zhou K, Zhang WB, Misener J (2009) Prediction of red light running based on statistics of discrete point sensors. Paper presented at the 88th annual meeting of the transportation research board, Washington, DCGoogle Scholar
  9. Zhou K et al (2008) Field evaluation of San Pablo Corridor Transit Signal Priority (TSP) system, PATH research report. Institute of Transportation Studies, University of California, BerkeleyGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Wei-Bin Zhang
    • 1
    Email author
  • Alex Skabardonis
  • Meng Li
  • Jingquan Li
  • Kun Zhou
  • Liping Zhang
  1. 1.California PATHUniversity of California at BerkeleyRichmondUSA

Personalised recommendations