Big Data and Discrete Optimization for Electric Urban Bus Operations

Abstract

The electrification of urban bus fleets is a challenging task, especially for smaller public transport operators. The main challenge lies in the uncertainty about many technical aspects, like range of vehicles under different circumstances or charging times, that are new for the operators. The purpose of this research is to introduce an approach to solve this problem by incorporating all available data from an existing bus fleet and finding an optimal solution with discrete mathematical optimization. Extensive data logging in the project enabled us to leverage tracking data from the whole bus network including trajectories, powertrain data, and operational data. This enabled us to validate assumptions about the energy demand, waiting times, and different traffic situations during the day. To get better insights into the requirements of an urban bus fleet, we simulated the potential electric buses in detail and extracted other necessary data like actual dwell times. Based on the simulation results and processed data, we implemented a linear programming model to search for a cost-optimal configuration of vehicles and charging infrastructure. We tested the framework with a scenario in which we analyzed the solutions with different numbers of diesel buses in the fleet. The application of our algorithm shows that it can produce optimal results in a short amount of time, for a medium-sized city in Germany. We also demonstrate that the flexible and constraint-based formulation of this approach allows it to be incorporated in the planning process of most public transport operators.