A Real-Time Decision Support System to Improve Operations in Sustainable Transit Bus Networks

Abstract

Electrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators around the world are facing. Unlike diesel buses, battery electric buses need to recharge several times during the day due to their limited driving range. Hence, electric TBNs are more sensitive to operational delays and uncertainties. Moreover, they are only sustainable when powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this work, we tackle the complicated problem of planning the charging schedule amid these various sources of uncertainty. We develop a real-time decision support system (RDSS) that uses real-time data, predictions, and mathematical optimization to develop and update the charging schedule in order to mitigate the impact of operational uncertainties. We formulate a mixed-integer linear programming problem that is solved via the RDSS to optimize the charging schedule. Our results show the benefits of online optimization compared to other charging strategies. We also show that even conservative offline optimization might not be sufficient to guarantee reliable operation, especially under extreme realizations of uncertainty. On the other hand, the online strategy can maintain higher reliability and renewable energy utilization levels, while reducing the impact on the grid. The study has been carried out in cooperation with the main public transport operator in the city of Rotterdam in the Netherlands to assist them in their TBN transition process. Based on our insights and results, they adopted various essential parts of our models in their planning process.

Keywords: transportation, sustainable public transport, electric transit bus networks, smart charging