Optimizing Travel Time and Regenerative Energy for Periodic Timetables

TL;DR

This paper develops a bicriteria optimization model for railway timetables that maximizes regenerative energy while minimizing passenger travel time, extending the PESP framework.

Contribution

It introduces a new bicriteria model for periodic railway timetables that balances energy regeneration and passenger travel time, with complexity analysis and practical case studies.

Findings

The problem is NP-hard even for a single objective.

Several polynomial-time solvable special cases are identified.

Case studies demonstrate the model's practical applicability.

Abstract

Regenerating braking energy is one major pathway to make rail traffic energy-efficient. It is therefore desirable to design timetables that exploit this feature. However, timetables that allow to regenerate energy are often bad for the passengers. We hence formulate and analyze a bicriteria optimization problem (PESP-Passenger-Energy) to find periodic railway timetables that maximize the regenerated energy in terms of the brake-traction overlap time and minimize the travel time of the passengers. Our model extends the Periodic Event Scheduling Problem (PESP) and offers a rich combinatorial theory. We investigate its computational complexity on one-station networks, building on matchings and Hamiltonian paths. Besides showing its NP-hardness even for a single objective, we identify several polynomial-time solvable special cases. Finally, we provide two case studies, underlining the practicability of our model, and analyzing the Pareto front.