Semi-synthetic Data and Testbed for Long-Distance E-Vehicle Routing

Electric and autonomous mobility will increasingly rely on advanced route planning algorithms. Robust testing of these algorithms is dependent on the availability of large realistic data sets. Such data sets should capture realistic time-varying traffic patterns and corresponding travel-time and energy-use predictions. Ideally, time-varying availability of charging infrastructure and vehicle-specific charging-power curves should be included in the data to support advanced planning. We contribute with a modular testbed architecture including a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic distribution patterns, EV-specific data, and elevation data to generate time-dependent travel-time and energy-use weights in a road-network graph. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.