Test-data generation and integration for long-distance e-vehicle routing

Advanced route planning algorithms are one of the key enabling technologies for emerging electric and autonomous mobility. Large realistic data sets are needed to test such algorithms under conditions that capture natural time-varying traffic patterns and corresponding travel-time and energy-use predictions. Further, the time-varying availability of charging infrastructure and vehicle-specific charging-power curves may be necessary to support advanced planning. While some data sets and synthetic data generators capture some of the aspects mentioned above, no integrated testbeds include all of them. We contribute with a modular testbed architecture. First, it includes a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic volume distribution patterns, EV-specific data, and elevation data. These elements support the generation of time-dependent travel-time and energy-use weights in a road-network graph. The generator ensures that the data satisfies the FIFO property, which is essential for time-dependent routing. Next, the testbed provides a thin layer of services that can serve as building blocks for future advanced routing algorithms. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.