Distribution of Gaps in Multi-lane Orderly and Disorderly Traffic Streams

TL;DR

This paper develops an analytical framework based on Renewal Process Theory to determine the distribution of gaps in multi-lane traffic streams, aiding traffic simulation and analysis.

Contribution

It introduces a novel analytical method for gap distribution in multi-lane traffic streams and provides a maximum likelihood estimation process for real-world data.

Findings

The derived distribution accurately fits real-world gap data.

The framework distinguishes between orderly and disorderly traffic streams.

The method enhances traffic simulation realism.

Abstract

To study gap acceptance behaviour one needs the distribution (or probability density function) of gaps in the opposing stream. Further, in these times of widespread availability of large computing powers, traffic simulation has emerged as a popular analysis and design tool. Such simulations rely on randomly generating the arriving vehicles in a way that statistically resembles real-world streams. The generation process for disorderly streams requires information on gap distributions. A study of past literature reveals that very little work has been done to determine the distribution of gaps on multi-lane orderly and disorderly streams. This study aims to develop an analytical framework to specify the distribution of gaps for such streams. This analytical framework is built using the Renewal Process Theory. A maximum likelihood based process for the estimation of the parameters of the analytically derived distribution is also described. Later, real-world gap data from three different sites covering orderly and disorderly streams are used to show how the derived distribution function (using the proposed method) ably describes the observed gap distributions.