Reaction time driven profiling of traffic flow with intelligent vehicles

Abstract

This paper addresses the critical need to characterize traffic flow driven by the reaction time of evolving Intelligent Vehicles (IVs). Macroscopic traffic models play a vital role in understanding traffic conditions, however, the IVs behavior is ignored. Thus, a new traffic model for IVs based on safe reaction velocity, reaction time, and braking time is proposed, incorporating the IVs reaction times. The findings demonstrate a trade-off between the reaction time and braking time, significantly explaining traffic dynamics. Specifically, reducing the reaction time improves traffic operations. Results from three distinct traffic scenarios highlighted the significance of reaction time in shaping traffic safety. In the first scenario, smoother traffic flow demonstrates the impact of reaction time on safety. The shorter reaction time showed improved outcomes. In the second scenario, changes in traffic patterns near the ramp highlighted the importance of smaller reaction times in mitigating safety risks. In the third scenario, chaotic traffic conditions emphasized the role of reaction time in ensuring overall safety. The proposed traffic model offers a more realistic characterization of traffic flow. By understanding the relation between reaction time and braking time, this approach contributes to the development of safer and more efficient automated traffic mobility.

Keywords

• Driving behavior
• Macroscopic models
• Simulation
• Reaction and stimuli
• Traffic safety