Abstract
A well designed train timetable should fully utilize the limited infrastructure and
rolling stock resources to maximize operators’ profits and passenger travel demand
satisfaction. Thus, an internally coherent scheduling process should consider the three
main aspects: (1) dynamic choice behaviors of passengers so as to evaluate and calculate
the impact of variable passenger demand to (2) underlying train service patterns and
detailed timetables, which in turn are constrained by (3) infrastructure and rolling
stock capacity. This paper aims to develop an integrated demand/service/resource
optimization model for managing the above-mentioned three key decision elements with a
special focus on passengers’ responses to time-dependent service interval times or
frequencies. The model particularly takes into account service-sensitive passenger
demand as internal variables so that one can accurately map passengers to train services
through a representation of passenger carrying states throughout a team of trains. The
added state dimension leads to a linear integer multi-commodity flow formulation in
which three closely interrelated decision elements, namely passengers’ response to
service interval times, train stopping pattern planning and timetabling for conflict
detecting and resolving are jointly considered internally. By using a Lagrangian
relaxation solution framework to recognize the dual costs of both passenger travel
demand and limited resources of track and rolling stock, we transfer and decompose the
formulation into a novel team-based train service search sub-problem for maximizing the
profit of operators. The sub-problem is solvable efficiently by a forward dynamic
programming algorithm across multiple trains of a team. Numerical experiments are
conducted to examine the efficiency and effectiveness of the dual and primal solution
search algorithms.
Keywords
Train timetabling, Service-sensitive demand, State-space-time
network, Lagrangian relaxation, Dynamic programming
Highlights
• Optimize train service plans in an integrated framework for proactively managing
passenger demand.
• Introduce a team-based solution approach to synchronize demand assignment, routing,
timetabling tasks.
• Propose a Lagrangian relaxation solution framework to utilize dual cost information at
different network layers.
• The proposed methods can better satisfy passenger travel demand and increase operators’
profit, compared to reactive and iterative approaches.
原文传递： https://doi.org/10.1016/j.trb.2019.02.017
