Research on Driving Safety Monitoring Device and Urban Traffic Governance Based on Smart Mobility Big Data

DOI

10.2991/978-94-6239-825-2_11How to use a DOI?

Keywords

Driving Safety Monitoring Device; Smart Mobility; Urban Public Administration; Cluster Analysis; Association Rule Mining

Abstract

As a vital component of urban public safety management systems, the effectiveness of driving safety monitoring devices depends on accurate identification and real-time warning of traffic operation states. Based on the Smart Mobility Dataset, this study employs K-means clustering (optimal K=9, silhouette score=0.1952) with PCA dimensionality reduction to finely identify urban road network traffic patterns, and utilizes the Apriori algorithm (min_support=0.02, min_confidence=0.4) to extract key driving factors of high-energy-consumption traffic states. Nine typical traffic state patterns were identified, among which Cluster 0 (76.47% occupancy, 288.11 g/km emission), Cluster 1, Cluster 5, and Cluster 6 were classified as high-emission/high-energy risk clusters, accounting for 42.5% of the total samples. Association rule mining revealed that the combination of foggy weather and red traffic lights (support=0.03, confidence=0.44, lift=1.15) serves as a significant precursor signal for high-energy-consumption traffic states. The findings provide data-driven decision-making support for optimizing deployment strategies of driving safety monitoring devices, traffic emergency management by urban public administration departments, and risk warning mechanisms within public service networks.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaohan Li
AU  - Jiale Gao
AU  - Jiaze Yuan
AU  - Yinghe Wang
PY  - 2026
DA  - 2026/11/10
TI  - Research on Driving Safety Monitoring Device and Urban Traffic Governance Based on Smart Mobility Big Data
BT  - Proceedings of the 2026 5th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2026)
PB  - Atlantis Press
SP  - 101
EP  - 107
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-825-2_11
DO  - 10.2991/978-94-6239-825-2_11
ID  - Li2026
ER  -