[论文解读] Sensor Placement for Urban Traffic Interpolation: A Data-Driven Evaluation to Inform Policy

Silke K. Kaiser|arXiv (Cornell University)|Jan 12, 2026

Traffic Prediction and Management Techniques被引用 0

一句话总结

该论文基准城市级交通插值的数据驱动传感器布点策略，即使在空间覆盖较高的情况下，主动学习也能显著提升准确性，临时部署接近永久部署的性能。

ABSTRACT

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.

研究动机与目标

• 推动需要数据驱动的传感器布点以改善全市交通插值的必要性。
• 在现实预算下系统性比较多种空间布点策略（中心性、特征/空间覆盖、主动学习）的效果。
• 评估临时传感器的时序部署方案，并比较临时部署与永久部署。
• 为城市提供可迁移的指南，帮助在临时与永久传感器部署之间做出选择。

提出的方法

• 将道路网络表示为带特征的街段，并将数据分为训练、验证和测试集。
• 在最多 K 个传感器下（K ∈ {10,25,50,75,100}），使用多种布点策略进行评估，包括中介性、接近性、特征多样性、特征冗余、特征覆盖、空间分散、Voronoi 基尼系数以及主动学习。
• 使用 XGBoost 根据传感器观测值对城市级交通量进行插值；以均值绝对误差（MAE）衡量性能。
• 在相同预算下，将永久传感器布点（连续观测）与临时布点（时空选择）进行比较。
• 评估基线情景：随机布点、现有部署，以及全部训练数据的上界。

(a) Cycling network in Berlin.

实验结果

研究问题

• RQ1 在给定预算和城市/模式下，哪种空间传感器布点策略能实现最低的城市级插值误差？
• RQ2 临时传感器的时序部署选择如何影响插值精度，临时部署在类似预算下与永久部署相比如何？
• RQ3 结果是否能在不同城市环境中泛化（如柏林的 Strava 自行车计数与曼哈顿的出租车计数）以及不同交通方式（自行车 vs 机动车交通）？

主要发现

• 强调均匀空间覆盖和主动学习的空间策略能实现最低的预测误差。
• 在预算为 10 个传感器时，这些策略相较其他方法在柏林 MAE 降低超过 60%，在曼哈顿降低超过 70%。
• 将观测均匀分布在工作日的时序部署在柏林额外降低 MAE 约 7%，在曼哈顿额外降低约 21%。
• 在遵循空间和时间设计原则时，临时部署可以接近最优永久部署的性能。
• 本研究提供证据表明数据驱动的传感器布点策略可以显著提升数据对城市政策的可用性，同时在临时与永久部署之间提供灵活性。

(b) Street network in Manhattan, NYC.