Research on work zone vehicle queuing behavior based on cellular automata

TL;DR

This paper develops a cellular automata model to estimate work zone vehicle queue lengths, aiding in work zone management by optimizing warning zone length and lane-changing strategies based on empirical data.

Contribution

It introduces a novel cellular automata model validated with empirical data to improve work zone capacity and safety management strategies.

Findings

Optimal warning zone length depends on design flow.

Lane-changing strategies vary with different flow conditions.

Model accurately estimates queue lengths in work zones.

Abstract

A model is proposed to estimate the work zone queue length, and the cellular automata based on empirical data is used for model validation. This estimation model can be applied to work zone organization and management to improve work zone capacity and security. Relationship between the average queue length and the warning zone length can be found, and the appropriate warning zone length can be determined according to design flow. Moreover, the appropriate work zone lane-changing strategies under different design flows are found through the estimation model.