Development of real-time intersection movement traffic volume counting system using microscopic traffic simulation

2021 ◽  
Vol 22 (5) ◽  
pp. 831-837
Author(s):  
Inchul Yang ◽  
Woo-Hoon Jeon
Keyword(s):  
Real Time ◽  
Traffic Simulation ◽  
Traffic Volume ◽  
Counting System ◽  
Microscopic Traffic Simulation

scholarly journals Fuzzy Logic Based Microscopic Traffic Simulation for Urban Transport Policy Evaluation

2009 ◽  
Vol 26 (0) ◽  
pp. 923-932
Author(s):  
Masashi OKUSHIMA ◽  
Takamasa AKIYAMA ◽  
Madhu ERRAMPALLI
Keyword(s):  
Fuzzy Logic ◽  
Policy Evaluation ◽  
Traffic Simulation ◽  
Transport Policy ◽  
Urban Transport ◽  
Microscopic Traffic Simulation

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

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