Table 1.
Summary of related works on traffic control features.
Fig 1.
Overview of TD3P-ITC framework.
Fig 2.
Traffic Environment Analysis a) Feature Correlation b) Accident Rate c) Average Speed Across Hours and d) Traffic Volume.
Table 2.
State Space feature specification.
Fig 3.
Traffic Control Analysis Over Time Across a) Traffic Volume, b) Average Speed, and c) Reward Function.
Fig 4.
System interaction sequence flow of TD3P-ITC.
Fig 5.
Real-time traffic signal optimization across multiple intersections.
Fig 6.
Impact of PER on learning performance.
Table 3.
Hyperparameter tuning settings for TD3P-ITC framework.
Fig 7.
Simulation Analysis of Traffic Control and Agent Actions Over Time a) Traffic Volume and Speed, b) Accident Risk and Occurrence, c) Signal Timing Adjustments, and d) Average Reward by Weather Condition.
Fig 8.
Average Waiting Time for Different Traffic Signal Control Models Across Various Traffic Scenarios a) Low Traffic (50 Veh/h), b) Moderate (300 Veh/h), c) High Traffic (600 Veh/h), and d) Very High Traffic (998 veh/h).
Table 4.
Throughput comparison of traffic signal control methods.
Fig 9.
Queue Length Analysis on Various Traffic Intersections: a) Residential, b) Commercial, c) Highway, d) Mixed-Use, and e) Transport Hub.
Fig 10.
Training Performance Analysis: a) TD3P-ITC Model and b) Convergence.
Table 5.
Statistical significance analysis (Two-tailed t-test Results).
Fig 11.
Performance summary of research models.