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.

先进的路线规划算法是新兴电动和自主移动的关键支持技术之一。需要大量真实的数据集来在捕获自然时变交通模式和相应的旅行时间和能源使用预测的条件下测试此类算法。此外，可能需要充电基础设施随时间变化的可用性和车辆特定的充电功率曲线来支持高级规划。虽然一些数据集和合成数据生成器捕获了上述某些方面，但没有集成测试平台包含所有这些方面。我们贡献了一个模块化的测试平台架构。首先，它包括一个半合成数据生成器，该生成器使用最先进的交通模拟器、真实交通量分布模式、EV 特定数据和高程数据。这些元素支持在道路网络图中生成与时间相关的旅行时间和能源使用权重。生成器确保数据满足 FIFO 属性，这对于依赖于时间的路由至关重要。接下来，测试平台提供了一个薄层服务，可以作为未来高级路由算法的构建块。实验研究表明，试验台可以重现类似于商业服务的长途旅行的旅行时间和能源使用模式。