Reinforcement Learning Method with Dynamic Learning Rate for Real-Time Route Guidance Based on SUMO

Traffic congestion is a frequent problem on most urban roads. This may be due to incorrect configuration of traffic signals but planning analysis should also include a study of human behavior, which, often imprudent, contributes to traffic congestion. The aim of this paper is to analyze the influence of human factors and their reaction to the environment on the impact of traffic performance through waiting time. For this purpose, an agent-based simulation is developed to represent the autonomous and social behavior of road users. The waiting of vehicles at signals is modeled on the basis of a queuing system. Simulations and experiments are based on the analysis of the age of the people and the condition of the pavement. Results show that people’s age is the most important factor influencing their behavior on the road. It is also shown external factors that also affect driver response and thus signal impact, such as the condition of the pavement. Finally, traffic performance, measured by waiting time, depends strongly on the behavior of people facing signals, according to their characteristics and factors present in the environment.

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Reinforcement Learning Method with Dynamic Learning Rate for Real-Time Route Guidance Based on SUMO

Cited by 3 publications

References 21 publications

Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

A Co-Simulation Framework for Autonomous Mobility in Urban Mixed Traffic Context^*

Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation

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Reinforcement Learning Method with Dynamic Learning Rate for Real-Time Route Guidance Based on SUMO

Cited by 3 publications

References 21 publications

Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

A Co-Simulation Framework for Autonomous Mobility in Urban Mixed Traffic Context*

Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation

Contact Info

Product

Resources

About

A Co-Simulation Framework for Autonomous Mobility in Urban Mixed Traffic Context^*