Optimizing the Scheduling of Electrified Public Transport System in Malta

Abstract

In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands.

Simulation and Analysis of Line 1 of Mexico City’s Metrobus: Evaluating System Performance through Passenger Satisfaction

“…Other approaches have been proposed to optimize bus schedules. For example, Sharma et al [17] proposed a multi-agent framework to plan the schedules of one-way routes in Malta, and it was shown that this has a better performance compared to the traditional approach using mixed integer linear programming. Another approach was proposed by Xue et al [18] in which they used uncertainty theory and a bilevel programming model to optimize the frequency and time between departures of the 207 bus routes of Nanchang city, China; they demonstrated that this is an effective approach for reducing passengers' travel times, but it has some limitations.…”