Abstract:

Mobility as a Service platform such as Demand Responsive Traffic, car-sharing and ride-sharing platforms have increasingly become popular around the world as remedial measures to reduce traffic congestion. The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs to minimise the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent-based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn, derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Considering that the number of EVs has been increasing significantly over the last years, even more CSs will have to be constructed to cover the charging demand. The results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.