Enhancing the Validity of Traffic Flow Models with Emerging Data

  • Rita Excell
  • Jiaqi Ma
  • Steven Shladover
  • Daniel Work
  • Michael Levin
  • Samer H. HamdarEmail author
  • Meng Wang
  • Stephen P. Mattingly
  • Alireza Talebpour
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)


Modeling the impact of connected and automated vehicles (CAVs) on the environmental sustainability, mobility and safety of roadway traffic at the local link level or the regional network level requires a significant amount of currently non-available data. Multiple CAV test-beds and data collection efforts utilizing the latest sensing and communication technologies have been however publicized over the past few years. Such efforts have been led by the industry and public agencies in the US and abroad. Accordingly, (1) researchers and practitioners should be aware of the type and quantity of data needed to calibrate and validate traffic models while taking into account the impact of CAV technological specifications, the driver behavioral characteristics and the surrounding driving environments. (2) Moreover, the gap between such emerging data needs and the data made available to researchers or practitioners should be identified. This chapter summarizes the presentations of speakers that are investigating such gap during the Automated Vehicles Symposium 2017 (AVS17) held in San Francisco, California on July 11–13, 2017. These speakers participated in the break-out session titled “Enhancing the Validity of Traffic Flow Models with Emerging Data”. The corresponding discussion and recommendations are presented in terms of the lessons learned and the future research direction to be adopted. This session was organized by the AHB45(3) Subcommittee on Traffic Flow Modeling for Connected and Automated Vehicles.


Traffic flow modeling CAV/AV Deployment CACC Data Test-beds DSRCs Platooning Calibration/Validation 



The authors would like to acknowledge the breakout session organizers (the AHB45(3) committee members along with Robert Bertini from University of South Florida and Soyoung Ahn from University of Wisconsin, Madison) who made this book chapter possible. Special thanks to Xiaopen Li from the University of South Florida, Danjue Chen from the University of Massachusetts Lowell, Steven Skabardonis from the University of California at Berkeley, Haizhong Wang from Oregon State University and Mark Brackstone from TSS-AIMSUM for their outreach efforts while coordinating the event details with the AVS2017 organizing committee.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Rita Excell
    • 1
  • Jiaqi Ma
    • 2
  • Steven Shladover
    • 3
  • Daniel Work
    • 4
  • Michael Levin
    • 5
  • Samer H. Hamdar
    • 6
    Email author
  • Meng Wang
    • 7
  • Stephen P. Mattingly
    • 8
  • Alireza Talebpour
    • 9
  1. 1.Australia and New Zealand Driverless Vehicle InitiativeAdelaideAustralia
  2. 2.University of CincinnatiCincinnatiUSA
  3. 3.PATH, Institute of Transport StudiesUniversity of CaliforniaBerkeleyUSA
  4. 4.Vanderbilt UniversityNashvilleUSA
  5. 5.University of MinnesotaMinneapolisUSA
  6. 6.George Washington UniversityWashington, D.C.USA
  7. 7.Delft University of TechnologyDelftThe Netherlands
  8. 8.University of Texas at ArlingtonArlingtonUSA
  9. 9.Texas A&M UniversityCollege StationUSA

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