# A survey of urban drive-by sensing: An optimization perspective

arXiv: https://arxiv.org/abs/2302.00622 · HTML: https://mlsanity.com/paper/2302.00622/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2023.

## Built on by (most cited first, 4 of 4)

- [AgentSense: LLMs Empower Generalizable and Explainable Web-Based Participatory Urban Sensing](https://mlsanity.com/paper/2510.19661/) (2025)
- [SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing](https://mlsanity.com/paper/2411.18432/) (2024)
- [Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing](https://mlsanity.com/paper/2603.24014/) (2026)
- [Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments](https://mlsanity.com/paper/2607.18874/) (2026)

## Abstract (arXiv)

Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to the mobility patterns of their hosts, which may include third-party (e.g. taxis, buses) or for-hire (e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is therefore essential to understand, assess and optimize the sensing power of vehicle fleets under a wide range of urban sensing scenarios. To this end, this paper offers an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategic/tactical); and (4) vehicle maneuvers (tactical/operational). By compiling research findings and practical insights in this way, this review article not only highlights the optimization aspect of drive-by sensing, but also serves as a practical guide for configuring and deploying vehicle-based urban sensing systems.