全城街道路段级交通量数据对城市规划与可持续交通管理至关重要。然而，由于传感器部署与维护成本高昂，此类数据仅覆盖有限数量的街道；其余路段的交通量则需基于现有传感器测量值进行插值估算。当前传感器位置往往由行政优先事项决定，而非数据驱动的优化方法，导致覆盖偏差与估计性能下降。本研究利用柏林（Strava自行车计数）和曼哈顿（出租车计数）的路段级真实数据，对若干易于实施的数据驱动策略进行了大规模、现实场景下的基准评估，以优化永久性与临时性交通传感器的布设。研究比较了基于网络中心性、空间覆盖、特征覆盖及主动学习（active learning）的空间布设策略，并考察了临时传感器的时间部署方案。结果表明，强调均匀空间覆盖并结合主动学习的空间布设策略可实现最低预测误差：仅使用10个传感器时，其在柏林和曼哈顿的平均绝对误差分别较其他方案降低逾60%和70%。时间部署选择可进一步提升性能：在工作日均匀分布测量时段，可使柏林和曼哈顿的误差分别额外降低7%和21%。上述空间与时间原则相结合，可使临时布设方案的性能接近最优布设的永久性方案。从政策角度看，研究结果表明，城市可通过采纳数据驱动的传感器布设策略显著提升数据效用，同时保持

Data on citywide street-segment traffic volumes are essential for urban planning and sustainable mobility management. Yet such data are available only for a limited subset of streets due to the high costs of sensor deployment and maintenance. Traffic volumes on the remaining network are therefore interpolated based on existing sensor measurements. However, current sensor locations are often determined by administrative priorities rather than by data-driven optimization, leading to biased coverage and reduced estimation performance. This study provides a large-scale, real-world benchmarking of easily implementable, data-driven strategies for optimizing the placement of permanent and temporary traffic sensors, using segment-level data from Berlin (Strava bicycle counts) and Manhattan (taxi counts). It compares spatial placement strategies based on network centrality, spatial coverage, feature coverage, and active learning. In addition, the study examines temporal deployment schemes for temporary sensors. The findings highlight that spatial placement strategies that emphasize even spatial coverage and employ active learning achieve the lowest prediction errors. With only 10 sensors, they reduce the mean absolute error by over 60% in Berlin and 70% in Manhattan compared to alternatives. Temporal deployment choices further improve performance: distributing measurements evenly across weekdays reduces error by an additional 7% in Berlin and 21% in Manhattan. Together, these spatial and temporal principles allow temporary deployments to closely approximate the performance of optimally placed permanent deployments. From a policy perspective, the results indicate that cities can substantially improve data usefulness by adopting data-driven sensor placement strategies, while retaining flexibility in choosing between temporary and permanent deployments.