Zhong, Hui (The hong kong university of science and techonology (Guangzhou)), Lu, Qing-Long (National University of Singapore), Zhang, Qiming (The Hong Kong University of Science and Technology in Guangzhou), Lu, Hongliang (The Hong Kong University of Science and Technology (Guangzhou)), Zheng, Xinhu (The HongKong University of Science and Technology (Guangzhou))

Enhancing Urban Sensing Utility with Sensor-Enabled Vehicles and Easily Accessible Data

Scheduled for presentation during the Regular Session "S36b-Behavior Modeling and Decision-Making in Traffic Systems" (FR-EA-T36), Friday, November 21, 2025, 13:50−14:10, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologies, modern vehicles are transforming from solely mobility tools to valuable sensors for urban data collection, and hold the potential of improving traffic congestion, transport sustainability, and infrastructure inspection.Vehicle-based sensing is increasingly recognized as a promising technology due to its flexibility, cost-effectiveness, and extensive spatiotemporal coverage. However, optimizing sensing strategies to balance spatial and temporal coverage, minimize redundancy, and address budget constraints remains a key challenge. This study proposes an adaptive framework for enhancing the sensing utility of sensor-equipped vehicles. By integrating heterogeneous open-source data, the framework leverages spatiotemporal weighting to optimize vehicle selection and sensing coverage across various urban contexts. An entropy-based vehicle selection strategy, Improved OptiFleet, is developed to maximize sensing utility while minimizing redundancy. The framework is validated using real-world air quality data from 320 sensor-equipped vehicles operating in Guangzhou, China, over two months. Key findings show that the proposed method outperforms baseline strategies, providing up to 5% higher sensing utility with reduced fleet sizes, and also highlights the critical role of dynamic urban data in optimizing mobile sensing strategies.