A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Python Extension in Sumo for Simulating Traffic Incidents and Emergency Service Vehicles
Traffic signal retiming and coordination play a significant role in traffic management, especially when the traffic incidents disrupt the network. Many researchers have developed artificially intelligent (AI) traffic signal controllers based on goal-oriented machine learning frameworks to try and optimize network performance. However, previous efforts have lacked the means to evaluate these networks under traffic incident conditions. To make these AI traffic signal controllers more robust, current research needs to consider AI traffic controller performance under traffic incidents and accompanying emergency response. Obtaining field incident data and converting into inputs for simulation models to evaluate these machine learning models has been a huge hurdle because it is expensive, time-consuming, and sometimes even unfeasible. This paper provides an integrated traffic incident and response simulation tool for a grid network made in Simulation of Urban MObility (SUMO) to overcome this gap. The tool includes random traffic incident generation (location and duration), incident detection, random emergency vehicle generation, and emergency vehicle dispatching. With this tool, users can simulate a road network with traffic incidents and emergency vehicle response to produce substantial amounts of data for training robust reinforcement learning models. In addition, this tool will save future researchers and practitioners both time and effort in testing the impact of their proposed AI traffic control models and allow for a more complete evaluation of performance.
A Python Extension in Sumo for Simulating Traffic Incidents and Emergency Service Vehicles
Traffic signal retiming and coordination play a significant role in traffic management, especially when the traffic incidents disrupt the network. Many researchers have developed artificially intelligent (AI) traffic signal controllers based on goal-oriented machine learning frameworks to try and optimize network performance. However, previous efforts have lacked the means to evaluate these networks under traffic incident conditions. To make these AI traffic signal controllers more robust, current research needs to consider AI traffic controller performance under traffic incidents and accompanying emergency response. Obtaining field incident data and converting into inputs for simulation models to evaluate these machine learning models has been a huge hurdle because it is expensive, time-consuming, and sometimes even unfeasible. This paper provides an integrated traffic incident and response simulation tool for a grid network made in Simulation of Urban MObility (SUMO) to overcome this gap. The tool includes random traffic incident generation (location and duration), incident detection, random emergency vehicle generation, and emergency vehicle dispatching. With this tool, users can simulate a road network with traffic incidents and emergency vehicle response to produce substantial amounts of data for training robust reinforcement learning models. In addition, this tool will save future researchers and practitioners both time and effort in testing the impact of their proposed AI traffic control models and allow for a more complete evaluation of performance.
A Python Extension in Sumo for Simulating Traffic Incidents and Emergency Service Vehicles
Lecture Notes in Civil Engineering
Gupta, Rishi (editor) / Sun, Min (editor) / Brzev, Svetlana (editor) / Alam, M. Shahria (editor) / Ng, Kelvin Tsun Wai (editor) / Li, Jianbing (editor) / El Damatty, Ashraf (editor) / Lim, Clark (editor) / Li, Tianxin (author) / Zhao, Wei (author)
Canadian Society of Civil Engineering Annual Conference ; 2022 ; Whistler, BC, BC, Canada
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 ; Chapter: 35 ; 527-539
2024-02-06
13 pages
Article/Chapter (Book)
Electronic Resource
English
A SUMO Extension for Norm-Based Traffic Control Systems
| Springer Verlag | 2018
| British Library Conference Proceedings | 2011