Review of real-time vehicle schedule recovery methods in transportation services

Abstract

This paper presents a comprehensive review on methods for real-time schedule recovery in transportation services. The survey concentrates on published research on recovery of planned schedules in the occurrence of one or several severe disruptions such as vehicle breakdowns, accidents, and delays. Only vehicle assignment and rescheduling are reviewed; crew scheduling and passenger logistics problems during disruptions are not. Real-time vehicle schedule recovery problems (RTVSRP) are classified into three classes: vehicle rescheduling, for road-based services, trainbased rescheduling, and airline schedule recovery problems. For each class, a classification of the models is presented based on problem formulations and solution strategies. The paper concludes that RTVSRP is a challenging problem that requires quick and good quality solutions to very difficult and complex situations, involving several different contexts, restrictions, and objectives. The paper also identifies research gaps to be investigated in the future, stimulating research in this area.

Managing disruptions in the multi-depot vehicle scheduling problem

“…In this section, we present the related studies in the literature on the MDVSP and disruption management. We refer the interested reader to Visentini et al (2014) for an extensive review on various recovery methods. Although most of the solution methodologies are heuristics and handle the disruptions at the operational level, there exist few studies proposing exact approaches that incorporate robustness in their model in the planning stage.…”

Evaluation of a transportation system employing autonomous vehicles

“…Optimization algorithms for planning transportation services according to [18,19] generally fall into several broad categories: (i) vehicle assignment and rescheduling; (ii) crew scheduling; and (iii) passenger logistics. Many of these processes rely on the generation of a schedule prior to use, but with sufficient vehicle and station instrumentation, efficient real-time scheduling and dispatch for vehicles in an optimal manner are achievable.…”

“…The vast majority of infrastructure-related work is focused on urban intersection handling where wireless vehicle-to-infrastructure (V2I) communication is in vehicle right-of-way coordination [12,13]. Lastly, high-level strategies for improving traffic flow generally focus on headway regulation [14,15] and optimal routing [16,17].Optimization algorithms for planning transportation services according to [18,19] generally fall into several broad categories: (i) vehicle assignment and rescheduling; (ii) crew scheduling; and (iii) passenger logistics. Many of these processes rely on the generation of a schedule prior to use, but with sufficient vehicle and station instrumentation, efficient real-time scheduling and dispatch for vehicles in an optimal manner are achievable.…”

Vehicle scheduling based on variable trip times with expected on‐time performance

“…Since the 1990s, most research has focused on the VSP with multiple depots (MDVSP) (Kliewer et al, 2006), which is proven to be NP-hard by Bertossi et al (1987). Meanwhile, more realistic characteristics have been included, such as fuel consumption (Haghani and Banihashemi, 2002;Li, 2014), time windows (Desaulniers et al, 1998;Kliewer et al, 2011), multiple vehicle types (Kliewer et al, 2006;Ceder, 2011;Hassold and Ceder, 2014), integration with timetabling (Ibarra-Rojas et al, 2014), integration with crew scheduling (Huisman et al, 2005;Shen and Ni, 2006;Shen and Xia, 2009;Shen and Zhao, 2009;Steinzen et al, 2010), and integration with crew scheduling and rostering (Mesquita et al, 2013).…”

“…Huisman et al (2004) proposed a dynamic vehicle scheduling approach to solve a sequence of rescheduling problems in real time, in which the disturbances affecting the trip times are considered. Moreover, a review on more schedule recovery methods can be found in Visentini et al (2014).…”