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Driver memory: Motorist selection and retention of individualized headways in highway traffic

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  • Cassidy, Michael J.
  • Windover, John R.

Abstract

The paper presents evidence that (1) drivers have different personalities in that they follow vehicles at different headways, and (2) drivers retain their personalities in that each driver tends to maintain his headway over space and, in some instances, drivers return to their headways after being forced by a traffic disturbance to alter them temporarily. This attribute, which we term driver memory, is revealed by visual inspection of modified curves of measured cumulative vehicle arrival number versus time.

Suggested Citation

  • Cassidy, Michael J. & Windover, John R., 1998. "Driver memory: Motorist selection and retention of individualized headways in highway traffic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(2), pages 129-137, February.
    • Handle: RePEc:eee:transa:v:32:y:1998:i:2:p:129-137
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    References listed on IDEAS

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    1. Cassidy, Michael J., 1998. "Bivariate relations in nearly stationary highway traffic," Transportation Research Part B: Methodological, Elsevier, vol. 32(1), pages 49-59, January.
    2. Daganzo, Carlos F., 1995. "Requiem for second-order fluid approximations of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 277-286, August.
    3. Newell, Gordon F., 1995. "Theory of highway traffic flow: 1945 to 1965," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt20s9h43s, Institute of Transportation Studies, UC Berkeley.
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    Citations

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    Cited by:

    1. Mauch, Michael & Cassidy, Michael J., 2004. "Freeway Traffic Oscillations: Observations and Predictions," University of California Transportation Center, Working Papers qt89c3h1vv, University of California Transportation Center.
    2. Smilowitz, Karen & Daganzo, Carlos & Cassidy, Michael & Bertini, Robert, 1998. "Some Observations Of Highway Traffic In Long Queues," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8rd637pq, Institute of Transportation Studies, UC Berkeley.
    3. Wang, Xiao & Jiang, Rui & Li, Li & Lin, Yi-Lun & Wang, Fei-Yue, 2019. "Long memory is important: A test study on deep-learning based car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 786-795.
    4. Kim, Kwangho & Cassidy, Michael J., 2012. "A capacity-increasing mechanism in freeway traffic," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1260-1272.
    5. Ahn, Soyoung & Cassidy, Michael J., 2002. "Identifying Density-Flow Relations on Arterial Surface Streets," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8685r9ks, Institute of Transportation Studies, UC Berkeley.
    6. Mauch, Michael, 2002. "Analyses of Start-Stop Waves in Congested Freeway Traffic," University of California Transportation Center, Working Papers qt9kb9x6n5, University of California Transportation Center.
    7. Ahn, Soyoung & Cassidy, Michael J. & Laval, Jorge, 2004. "Verification of a simplified car-following theory," Transportation Research Part B: Methodological, Elsevier, vol. 38(5), pages 431-440, June.
    8. Li, Li & Li, Xiaopeng, 2019. "Parsimonious trajectory design of connected automated traffic," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 1-21.
    9. Chiabaut, Nicolas & Leclercq, Ludovic & Buisson, Christine, 2010. "From heterogeneous drivers to macroscopic patterns in congestion," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 299-308, February.

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