Abstract
Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. We model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. We specifically deal with the potential presence of noisy measurements, and the unpredictability of vehicles paths. Moreover, we address the issue of optimally placing the monitoring cameras in order to maximize coverage, while minimizing the inference error, and the overall cost. We provide extensive experimental assessment on the topology of downtown San Francisco, CA, USA, using real measurements obtained through the Google Maps APIs, and on realistic synthetic networks. Our approach provides a very low error in estimating the traveling times over 95% of all roads even when as few as 20% of road intersections are equipped with cameras.
Recommended Citation
R. Zhang et al., "A Network Tomography Approach for Traffic Monitoring in Smart Cities," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 7, pp. 2268 - 2278, Institute of Electrical and Electronics Engineers, Jul 2018.
The definitive version is available at https://doi.org/10.1109/TITS.2018.2829086
Department(s)
Computer Science
Keywords and Phrases
network tomography; smart cities; Traffic monitoring
International Standard Serial Number (ISSN)
1524-9050
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2018
