![]() This paper aims to do the tedious but necessary legwork for the road-network algorithms community. In another study, to perform real-world experiments, the simulation framework was extended, and traffic patterns were taken from LuST Scenario data. For example, to implement Eur-PTV and Ger-PTV benchmarks, the following data from different sources were preprocessed and integrated: road network, elevation information, energy consumption, road traffic data, and locations of charging stations. Research studies that do explore advanced routing problems expend much effort to prepare their experiments. This means that research on advanced routing algorithms in academia either has to resort to simplistic synthetic test data and workloads or, more often, is not tackled at all, looking instead for more accessible problems. Furthermore, making any such data sets public is often burdened by privacy concerns. Real test datasets are readily available only at very few commercial service providers such as Google, TomTom, or HERE. Only extensive experimental studies on large datasets and workloads can verify the efficiency and efficacy of such algorithms. Furthermore, as real-world routing problems are often formulated as multi-objective optimization involving multiple constraints, the optimal algorithms are intractable thus, only heuristic algorithms are possible. Such algorithms will, in turn, depend on data-driven travel-time and energy-use predictions. For example, the efficiency of a fleet of autonomous electric vehicles will be highly dependent on effective routing and scheduling algorithms. The change is driven by the emergence of new automotive technologies and business models, such as electric and autonomous vehicles, and ridesharing. Like many areas of human activity, transportation is undergoing a profound transformation influenced by the continued digitalization of all aspects of the field. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services. Next, the testbed provides a thin layer of services that can serve as building blocks for future advanced routing algorithms. The generator ensures that the data satisfies the FIFO property, which is essential for time-dependent routing. ![]() These elements support the generation of time-dependent travel-time and energy-use weights in a road-network graph. 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. We contribute with a modular testbed architecture. ![]() While some data sets and synthetic data generators capture some of the aspects mentioned above, no integrated testbeds include all of them. Further, the time-varying availability of charging infrastructure and vehicle-specific charging-power curves may be necessary to support advanced planning. 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. Advanced route planning algorithms are one of the key enabling technologies for emerging electric and autonomous mobility. ![]()
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