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

Paul Wasielewski

Open publisher page 83 citations

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.

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What this paper is about

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.

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Available 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.

Key concepts: Headway, Poisson distribution, Traffic flow (computer networking), Range (aeronautics), Parametric statistics, Flow (mathematics), Distribution (mathematics), Mathematics

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