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SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities
Stephan Olariu1, Dimitrie C Popescu2
1Department of Computer Science, Old Dominion University, 3300 Engineering & Computational Sciences Bldg., Norfolk, VA 23529, USA.
Sensors (Basel, Switzerland)
|November 27, 2021
Summary
Smart Cities use Smart Mobility for traffic management. The SEE-TREND system anonymously collects vehicle data to detect traffic events and trends, improving travel decisions and reducing congestion.
Area of Science:
- Computer Science
- Transportation Engineering
- Data Science
Background:
- Smart Mobility is a critical service in Smart Cities and Communities.
- Effective traffic management is essential for urban functionality and sustainability.
- Current traffic monitoring systems may lack efficiency or privacy.
Purpose of the Study:
- To establish the theoretical framework for SEE-TREND, a novel system for Secure Early Traffic-Related Event Detection.
- To enhance Smart Mobility through intelligent traffic data analysis.
- To provide drivers with timely information for optimized travel planning.
Main Methods:
- Implementing an anonymous, probabilistic data collection mechanism from vehicles.
- Aggregating collected traffic-related data.
- Utilizing an inference engine to analyze data, build traffic state beliefs, and detect trends.
Main Results:
- The SEE-TREND system provides a foundation for proactive traffic event detection.
- The system enables the dissemination of traffic information to drivers.
- Potential for preventing or mitigating traffic congestion through informed decision-making.
Conclusions:
- SEE-TREND offers a secure and privacy-preserving approach to traffic monitoring in Smart Cities.
- The system supports the advancement of Smart Mobility by leveraging data analytics.
- Informed travel decisions facilitated by SEE-TREND can significantly improve urban traffic flow.
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