Locating optimal timetables and vehicle schedules in a transit line

Abstract

This paper deals with the Transit Network Timetabling and Scheduling Problem (TNTSP) in a public transit line. The TNTSP aims at determining optimal timetables for each line in a transit network by establishing departure and arrival times of each vehicle at each station. We assume that customers know departure times of line runs offered by the system. However, each user, traveling after or before than their desired travel time, will give rise to an inconvenience cost, or a penalty cost if that user cannot be served according to the scheduled timetable. The provided formulation allocates each user to the best possible timetable considering capacity constraints. The problem is formulated using a p-median based approach and solved using a clustering technique. Computational results that show useful applications of this methodology are also included.

Planning, operation, and control of bus transport systems: A literature review

“…Then, the authors develop an analytical model considering two steps: (i) a demand allocation problem is solved to assign individuals to line runs then, (ii) an optimization process is solved to set departure times in such a way as to minimize the passengers' total schedule delay costs. The minimization of the user's inconvenience is also studied by Mesa et al (2013). Constraints of the system are the limited number of trips in a given planning period, limits to the fleet size, and with respect to the vehicles' capacities.…”

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

“…For example, so far, up to 96 stations in the Beijing rail transit system have imposed routine passenger control strategies. ② Another practical method is demand-oriented train scheduling; this is a traditional approach used to deal with heavily congested passenger flow in peak hours, which has been studied deeply in the past years [2] , [3] , [4] . More precisely, based on detailed information on dynamic passenger demand, this method aims to shorten the train headway to enhance the capacity of the urban rail transit line/system and optimize train schedules so as to minimize the passenger waiting time at stations.…”

Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak

“…Sometimes, it is possible that the service cannot deliver all passengers, especially during peak periods, due to the imbalance in the high levels of travel demand and the limited train capacity ( Lu et al, 2021 ). Train scheduling has been recognized a basic, traditional approach to handle congested and unbalanced travel situations ( Niu and Zhou, 2013 ; Barrena et al, 2014 ; Mesa et al, 2014 ; Shi et al, 2018 ). Thus, competent schedule planning is needed to improve transport serviceability and to reduce unaccommodated passenger numbers in the system.…”