Optimizing the duration of delivery tours is a crucial issue in urban logistics. In most cases, travel times between locations are considered as constant for the whole optimization horizon. Making these travel times time-dependent is particularly relevant in real urban traffic environments as traffic conditions and thus travel speeds vary according to the time of the day. In this paper, we review the literature on time-dependent routing problems, with a specific focus on benchmarks and performance criteria used to experimentally evaluate the interest of exploiting time-dependent data, showing the lack of studies on the impact of spatio-temporal features of the benchmark on solutions. Hence, we introduce a new benchmark produced from a realistic traffic flow micro-simulation of Lyon city, allowing us to consider different levels of spatial granularity (i.e., number of sensors used to measure traffic conditions) and temporal granularity (i.e., frequency of measures). Finally, we experimentally evaluate the impact of the spatio-temporal granularity on the quality of solutions for different classical problems, including the traveling salesman problem, the pickup and delivery problem, and the dial-a-ride problem.

优化送货行程的时间是城市物流中的关键问题。在大多数情况下，整个优化范围的位置之间的旅行时间被认为是恒定的。使这些旅行时间与时间相关，这在实际的城市交通环境中尤其重要，因为交通状况和旅行速度会根据一天中的时间而变化。在本文中，我们回顾了有关时间相关路由问题的文献，特别关注用于通过实验评估利用时间相关数据的兴趣的基准和性能标准，这表明缺乏对时空特征影响的研究解决方案基准测试。因此，我们引入了一个新基准，它是由里昂市的现实交通流量微观模拟得出的，例如，用于测量交通状况的传感器数量）和时间粒度（即测量频率）。最后，我们通过实验评估时空粒度对不同经典问题（包括旅行商问题，接送问题和转搭问题）解决方案质量的影响。