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

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