The Bus Rapid Transit Investment Problem

TL;DR

This paper addresses the BRT investment problem by developing a bi-objective optimization model to maximize passenger attraction within budget constraints, considering multiple stakeholders and upgrade restrictions, with practical case study insights.

Contribution

It introduces a bi-objective model for BRT upgrades accounting for multiple investors and passenger responses, and provides an epsilon-constraint algorithm to generate the full Pareto front.

Findings

Full Pareto front can be generated for real-life instances.

Trade-off between investment and passenger attraction depends on demand and passenger response.

Pareto plots assist decision makers in selecting optimal route upgrades.

Abstract

Bus Rapid Transit (BRT) systems can provide a fast and reliable service to passengers at low investment costs compared to tram, metro and train systems. Therefore, they can be of great value to attract more passengers to use public transport. This paper thus focuses on the BRT investment problem: Which segments of a single bus line should be upgraded such that the number of newly attracted passengers is maximized? Motivated by the construction of a new BRT line around Copenhagen, we consider a setting in which multiple parties are responsible for the financing of different segments of the line. As each party has a limited willingness to invest, we solve a bi-objective problem to quantify the trade-off between the number of attracted passengers and the investment budget. We model different problem variants: First, we consider two potential passenger responses to upgrades on the line. Second, to prevent scattered upgrades along the line, we consider different restrictions on the number of upgraded connected components on the line. We propose an epsilon-constraint-based algorithm to enumerate the complete set of non-dominated points and investigate the complexity of this problem. Moreover, we perform extensive numerical experiments on artificial instances and a case study based on the BRT line around Copenhagen. Our results show that we can generate the full Pareto front for real-life instances and that the resulting trade-off between investment budget and attracted passengers depends both on the origin-destination demand and on the passenger response to upgrades. Moreover, we illustrate how the generated Pareto plots can assist decision makers in selecting from a set of geographical route alternatives in our case study.