# Sensor Placement for Urban Traffic Interpolation: A Data-Driven Evaluation to Inform Policy

arXiv: https://arxiv.org/abs/2601.10747 · HTML: https://mlsanity.com/paper/2601.10747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 85 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Diversity in Machine Learning](https://mlsanity.com/paper/1807.01477/) (2018)
- [Centrality Measures in Networks](https://mlsanity.com/paper/1608.05845/) (2016)
- [Estimating Historical Hourly Traffic Volumes via Machine Learning and Vehicle Probe Data: A Maryland Case Study](https://mlsanity.com/paper/1711.00721/) (2017)
- [Optimizing sensors placement in complex networks for localization of hidden signal source: A review](https://mlsanity.com/paper/2012.01876/) (2020)
- [Measuring diversity. A review and an empirical analysis](https://mlsanity.com/paper/2401.12365/) (2024)
- [From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation](https://mlsanity.com/paper/2406.18454/) (2024)
- [Where Should Traffic Sensors Be Placed on Highways?](https://mlsanity.com/paper/2110.00912/) (2021)
- [Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume](https://mlsanity.com/paper/2505.06292/) (2025)
- [Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement](https://mlsanity.com/paper/2403.07228/) (2024)
- [INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks](https://mlsanity.com/paper/2508.00141/) (2025)

## Abstract (arXiv)

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.