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Smart city article details
| Title | Analysis Of Urban Traffic Incidents Through Road Network Features |
|---|---|
| ID_Doc | 9402 |
| Authors | Saber T.; Capatina L.; Ventresque A. |
| Year | 2020 |
| Published | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 |
| DOI | http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00144 |
| Abstract | Road traffic prediction is crucial for transport operators. Traffic operators use traffic simulators with different precision levels (from microscopic to macroscopic simulators) to capture the complex nature of mobility, especially in urban environments. Predicting the impact of traffic incidents (e.g., accidents, events and protests) is one of the major challenges faced by traffic operators due to their direct impact on traffic congestion with its negative effects on many aspects of our lives (economy, wellbeing, health, pollution, etc.). In this work, we analyse how we can characterise the impact of road incidents through features of the road network on a microscopic simulation platform as a benchmark for measuring incident impact. We confirm that the impact severity of a road incident varies between crowded and uncrowded roads. However, we show that features of the road where the incident happened on their own are not enough to infer the impact severity of the incident. By extending the characterisation of the incident to its surrounding region, we show that the impact of a road incident is also affected by its location and the characteristics of its neighbouring roads. Furthermore, we identify that the impact of road incidents spans beyond the surrounding area, thus requiring further features for an accurate prediction of road incidents. © 2020 IEEE. |
| Author Keywords | Incident Impact; Road Network Features; Urban Traffic Simulation |
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