Vehicle-to-vehicle connectivity for real-time traffic incident detection on motorways: a traffic microsimulation study
File(s)
Author(s)
Koliou, Paraskevi
Type
Thesis
Abstract
The rapid pace of urbanisation and advancements in Intelligent Mobility (IM) bring both opportunities and challenges. While improved mobility options enhance convenience, they also contribute to traffic congestion and increased incident risks. Innovations in artificial intelligence—such as machine learning, big data, and image recognition—have transformed vehicles from mechanical systems into intelligent, connected entities. Connected Vehicle (CV) technology, which includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, enables real-time data exchange, improving traffic safety and efficiency.
Addressing traffic congestion and incidents—two key transport issues—requires advanced solutions. Incident Detection (ID) algorithms, supported by V2V connectivity, offer real-time alerts to road users, helping mitigate congestion and reduce accident risks. This study developed a V2V-based ID algorithm, evaluated using indicators like vehicle delay, queue length, macroscopic fundamental diagrams (MFDs), and conflict numbers. Using VISSIM traffic microsimulation and real-world data from the M1 motorway, scenarios with varying vehicle types and CV market penetration rates (MPRs) from 0% to 100% were assessed.
Simulations revealed that CV integration significantly improves traffic flow and safety. At 100% MPR, average delays dropped by 50%, and conflicts decreased by 55–70%, depending on incident duration. Five incident scenarios with different lane closures and durations further demonstrated that higher CV penetration leads to reduced disruption and better traffic performance.
The study also analysed 2019 traffic data on the M1 to explore incident and congestion patterns, revealing notable correlations. The findings support the potential of CV-enabled incident detection to enhance real-time traffic management and safety. This research provides a robust framework for integrating CV technology into existing infrastructure, aiding transport planners and policymakers in reducing incidents and improving congestion strategies.
Addressing traffic congestion and incidents—two key transport issues—requires advanced solutions. Incident Detection (ID) algorithms, supported by V2V connectivity, offer real-time alerts to road users, helping mitigate congestion and reduce accident risks. This study developed a V2V-based ID algorithm, evaluated using indicators like vehicle delay, queue length, macroscopic fundamental diagrams (MFDs), and conflict numbers. Using VISSIM traffic microsimulation and real-world data from the M1 motorway, scenarios with varying vehicle types and CV market penetration rates (MPRs) from 0% to 100% were assessed.
Simulations revealed that CV integration significantly improves traffic flow and safety. At 100% MPR, average delays dropped by 50%, and conflicts decreased by 55–70%, depending on incident duration. Five incident scenarios with different lane closures and durations further demonstrated that higher CV penetration leads to reduced disruption and better traffic performance.
The study also analysed 2019 traffic data on the M1 to explore incident and congestion patterns, revealing notable correlations. The findings support the potential of CV-enabled incident detection to enhance real-time traffic management and safety. This research provides a robust framework for integrating CV technology into existing infrastructure, aiding transport planners and policymakers in reducing incidents and improving congestion strategies.
Version
Open Access
Date Issued
2024-01-31
Date Awarded
01/04/2025
License URL
Advisor
Quddus, Mohammed
Michalaki, Paraskevi
Publisher Department
Department of Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)