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Models of Single Lane Time Headway Distributions

Author

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  • David Branston

    (Université de Montréal, Montreal, Québec, Canada)

Abstract

The movement of traffic past a point is compared to the output of a queuing system having random input. A generalization of the queue output model leads to a suitable headway model: this model is a mixture of two distributions, representing following and nonfollowing headways, in appropriate proportions. Thin model is compared with several others that have been suggested; when used with a lognormal distribution of following headways, it gives the best overall fit to data from the M4 motorway, England, and two-way roads in Indiana, USA. For each site, the parameters of the following headway distribution can be assumed constant. The mean following headways are 1.3 sec and 1.6 sec for the M4 fast and slow lanes respectively, and 2 sec for the Indiana sites. The standard deviation of logarithms of following headways is 0.4 for both M4 lanes and 0.45 for the Indiana sites. For most samples, the reciprocal of the mean interbunch gap (lambda) can be approximated by (lambda) = (lambda)* - 1/2 (lambda) *3/2 , where (lambda)* is the flow rate, and the proportion of the following vehicles (psi) can be approximated by (psi) = (rho) - 1/2((rho) - 1)(lambda) *3/2 , where (rho) is the traffic intensity.

Suggested Citation

  • David Branston, 1976. "Models of Single Lane Time Headway Distributions," Transportation Science, INFORMS, vol. 10(2), pages 125-148, May.
  • Handle: RePEc:inm:ortrsc:v:10:y:1976:i:2:p:125-148
    DOI: 10.1287/trsc.10.2.125
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    Cited by:

    1. Silvano, Ary P. & Koutsopoulos, Haris N. & Farah, Haneen, 2020. "Free flow speed estimation: A probabilistic, latent approach. Impact of speed limit changes and road characteristics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 283-298.
    2. Bari, Chintaman Santosh & Chandra, Satish & Dhamaniya, Ashish, 2022. "Service headway distribution analysis of FASTag lanes under mixed traffic conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Jabari, Saif Eddin & Zheng, Jianfeng & Liu, Henry X., 2014. "A probabilistic stationary speed–density relation based on Newell’s simplified car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 205-223.
    4. Sun, Lu & Jafaripournimchahi, Ammar & Kornhauser, Alain & Hu, Wushen, 2020. "A new higher-order viscous continuum traffic flow model considering driver memory in the era of autonomous and connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    5. Chiu, Yi-Chang & Zhou, Liang & Song, Houbing, 2010. "Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 152-174, January.
    6. Raffaele Mauro & Andrea Pompigna, 2022. "A Statistically Based Model for the Characterization of Vehicle Interactions and Vehicle Platoons Formation on Two-Lane Roads," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    7. Li, Baibing, 2017. "Stochastic modeling for vehicle platoons (II): Statistical characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 378-393.
    8. Qu, Xiaobo & Zhang, Jin & Wang, Shuaian, 2017. "On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 256-271.
    9. 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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