The existing variable speed limit (VSL) control strategies rely on variable
message signs, leading to slow response times and sensitivity to driver
compliance. These methods struggle to adapt to environments where both connected
automated vehicles (CAVs) and manual vehicles coexist. This article proposes a
VSL control strategy using the deep deterministic policy gradient (DDPG)
algorithm to optimize travel time, reduce collision risks, and minimize energy
consumption. The algorithm leverages real-time traffic data and prior speed
limits to generate new control actions. A reward function is designed within a
DDPG-based actor-critic framework to determine optimal speed limits. The
proposed strategy was tested in two scenarios and compared against no-control,
rule-based control, and DDQN-based control methods. The simulation results
indicate that the proposed control strategy outperforms existing approaches in
terms of improving TTS (total time spent), enhancing the throughput efficiency
of the bottleneck area, and reducing the spatial and temporal extent of traffic
congestion. Compared to the suboptimal DDQN-based VSL control, the proposed
strategy improves TTS by 9.3% in Scenario 1 and by 11% in Scenario 2. The
sensitivity analysis shows that the proposed control strategy improves
performance as the penetration rate of CAVs increases. However, when the
penetration rate reaches a certain threshold, the potential for further
optimization becomes limited. Furthermore, higher time-to-collision (TTC)
values, influenced by the reward function r
2, enhance traffic safety.
