Time-Dependent Queuing Model for Traffic Congestion Using Mt/D/1/K: Simulation and Policy Insights

TL;DR

This paper models traffic congestion using a Mt/D/1/K queue, calibrates it with real data, and demonstrates how staggered start times can reduce congestion on a specific route.

Contribution

It introduces a generalized macroscopic traffic simulation based on queueing theory and provides policy insights through simulation of time-based interventions.

Findings

Staggered start times significantly reduce queue lengths.

Model accurately explains peak commute times using real data.

Simulation offers practical policy recommendations for traffic management.

Abstract

This study proposes a generalised macroscopic traffic simulation using a Mt/D/1/K queue to model congestion, using the Enniskillen to Belfast route as a case study. Empirical traffic data from Google's Directions API is used to calibrate the model, thus explaining peak commute times which we model using queue length. Simulations of staggered institutional start times showed significant reductions in queue lengths, suggesting time based interventions to improve rural to urban traffic flow.