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Car-Following Headways on Freeways Interpreted by the Semi-Poisson Headway Distribution Model

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  • Paul Wasielewski

    (General Motors Research Laboratories, Warren, Michigan)

Abstract

The goal of this work is to investigate driver car-following patterns on freeways, particularly as a function of traffic flow level, using a headway distribution model. A number of authors have developed “two-component” vehicular headway distribution models that assume vehicles on a road can be divided into two groups according to whether or not they are interacting with the vehicle ahead. A model of this type, the “semi-Poisson” model proposed by Buckley, is applied to a data base consisting of 42,000 observed headways from a single lane of an urban freeway over a range of flow from 900 to 2,000 vehicles per lane per hour. A previously developed computational method allows the distribution of followers headways to be calculated directly from the observed total headway distribution by numerically solving an integral equation without introducing a parametric form for the followers distribution. The resulting followers headway distribution is found to be independent of the flow with a mean of 1.32 s and a standard deviation of 0.52 s. No statistically significant discrepancies are found between the model results and the observed data. The theoretical basis for the semi-Poisson model is discussed and compared with those of other models in order to assess the plausibility of the interpretation with respect to car following.

Suggested Citation

  • Paul Wasielewski, 1979. "Car-Following Headways on Freeways Interpreted by the Semi-Poisson Headway Distribution Model," Transportation Science, INFORMS, vol. 13(1), pages 36-55, February.
    • Handle: RePEc:inm:ortrsc:v:13:y:1979:i:1:p:36-55
    DOI: 10.1287/trsc.13.1.36
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    Cited by:

    1. Serge P. Hoogendoorn & W. Daamen, 2005. "Pedestrian Behavior at Bottlenecks," Transportation Science, INFORMS, vol. 39(2), pages 147-159, May.
    2. Xiao, Jianli & Wang, Zhonghao, 2018. "Traffic speed cloud maps: A new method for analyzing macroscopic traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 367-375.
    3. Fang, Zhenyuan & Zhu, Shichao & Fu, Xin & Liu, Fang & Huang, Helai & Tang, Jinjun, 2022. "Multivariate analysis of traffic flow using copula-based model at an isolated road intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    4. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2019. "Fast or forced to follow: A speed heterogeneous approach to congested multi-lane bicycle traffic simulation," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 72-98.
    5. Guohui Zhang & Yinhai Wang, 2014. "A Gaussian Kernel-Based Approach for Modeling Vehicle Headway Distributions," Transportation Science, INFORMS, vol. 48(2), pages 206-216, May.

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