On the Role of Multi-Objective Optimization to the Transit Network Design Problem

TL;DR

This paper demonstrates how combining single and multi-objective optimization approaches can effectively redesign urban transit networks, using Lisbon as a case study, to adapt to changing traffic patterns and improve service quality.

Contribution

It introduces a novel method that integrates multi-objective Pareto front approximation with single-objective inference for transit network design, applied to real-world data from Lisbon.

Findings

Up to 28.3% reduction in objective function values.

Significant improvements in passenger travel time and transfer metrics.

Effective incremental redesign of bus networks based on real-time data.

Abstract

Ongoing traffic changes, including those triggered by the COVID-19 pandemic, reveal the necessity to adapt our public transport systems to the ever-changing users' needs. This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP). Single objective formulations are dynamically inferred from the rating of networks in the approximated (multi-objective) Pareto Front, where a regression approach is used to infer the optimal weights of transfer needs, times, distances, coverage, and costs. As a guiding case study, the solution is applied to the multimodal public transport network in the city of Lisbon, Portugal. The system takes individual trip data given by smartcard validations at CARRIS buses and METRO subway stations and uses them to estimate the origin-destination demand in the city. Then, Genetic Algorithms are used, considering both single and multi objective approaches, to redesign the bus network that better fits the observed traffic demand. The proposed TNDP optimization proved to improve results, with reductions in objective functions of up to 28.3%. The system managed to extensively reduce the number of routes, and all passenger related objectives, including travel time and transfers per trip, significantly improve. Grounded on automated fare collection data, the system can incrementally redesign the bus network to dynamically handle ongoing changes to the city traffic.