Modelling Travel Time Distribution and Its Influence Over Stochastic Vehicle Scheduling

Abstract

Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement.

The Stochastic and Dynamic Vehicle Routing Problem: A Literature Review

“…There are many scholars studied the problem from other perspectives. For example, [17] proposed a modelling approach for travel time using distribution fitting method, [18] used the satisficing measure approach to solve the VRP with uncertain demands and uncertain travel time respectively, and [19] constructed a fuzzy optimization model based on credibility theory for the route planning of electric vehicles. In addition, robust optimization is also a kind of common methods used to solve the uncertain VRP [20][21][22].…”

A simulation-based optimisation for the stochastic green capacitated p-median problem

“…Those parameters are assumed to follow a normal distribution and the realisations of that assumption have a good outcome. In the routing problem, normal distribution is widely used to estimate the travel time or travel cost(Li, Tian & Leung, 2010;Shen, Xu, Wu & Ni, 2019;Bakach, Campbell, Ehmke & Urban, 2021…”