Abstract
In this paper we present a method to construct a periodic timetable from a tactical planning perspective. We aim at constructing a timetable that is feasible with respect to infrastructure constraints and minimizes total perceived passenger travel time. In addition to in-train and transfer times, our notion of perceived passenger time includes the adaption time (waiting time at the origin station). Adaption time minimization allows us to avoid strict frequency regularity constraints and, at the same time, to ensure regular connections between passengers’ origins and destinations. The combination of adaption time minimization and infrastructure constraints satisfaction makes the problem very challenging.The described periodic timetabling problem can be modelled as an extension of a Periodic Event Scheduling Problem (PESP) formulation, but requires huge computing times if it is directly solved by a general-purpose solver for instances of realistic size. In this paper, we propose a heuristic approach consisting of two phases that are executed iteratively. First, we solve a mixed-integer linear program to determine an ideal timetable that minimizes the total perceived passenger travel time but neglects infrastructure constraints. Then, a Lagrangian-based heuristic makes the timetable feasible with respect to infrastructure constraints by modifying train departure and arrival times as little as possible. The obtained feasible timetable is then evaluated to compute the resulting total perceived passenger travel time, and a feedback is sent to the Lagrangian-based heuristic so as to possibly improve the obtained timetable from the passenger perspective, while still respecting infrastructure constraints. We illustrate the proposed iterative heuristic approach on real-life instances of Netherlands Railways and compare it to a benchmark approach, showing that it finds a feasible timetable very close to the ideal one.
Highlights
For a passenger, a public transportation system is attractive if it offers a route from their origin to their destination with low travel time, no or few transfers, and at the ‘right’ time, i.e., with the desired departure time
In this paper we present an approach to solve the Passenger-Oriented Timetabling (POT) problem that aims at finding a timetable which is feasible with respect to infrastructure, and minimizes the passengers’ total perceived travel time, which is defined as the travel time on the ‘best’ route for each passenger including adaption time
The problem is defined as follows: Passenger-Oriented Timetabling (POT): Given an infrastructure network with stations and tracks connecting them, and a line plan, specifying line routes, stopping patterns and frequencies: find a periodic timetable including all or a subset of the trains that satisfies the headway constraints induced by the infrastructure network and that minimizes average perceived travel time, where we assume that passengers will travel on shortest routes according to perceived travel time
Summary
A public transportation system is attractive if it offers a route from their origin to their destination with low travel time, no or few transfers, and at the ‘right’ time, i.e., with the desired departure (or arrival) time. In this paper we present an approach to solve the Passenger-Oriented Timetabling (POT) problem that aims at finding a timetable which is feasible with respect to infrastructure, and minimizes the passengers’ total perceived travel time, which is defined as the travel time on the ‘best’ route for each passenger including adaption time. The problem is defined as follows: Passenger-Oriented Timetabling (POT): Given an infrastructure network with stations and tracks connecting them, and a line plan, specifying line routes, stopping patterns and frequencies: find a periodic timetable including all or a subset of the trains that satisfies the headway constraints induced by the infrastructure network and that minimizes average perceived travel time, where we assume that passengers will travel on shortest routes according to perceived travel time. Our contribution in this paper is twofold: First, we propose an iterative heuristic approach to construct a periodic timetable that satisfies infrastructure constraints and minimizes the average perceived travel time (that includes the adaption time).
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