A Reinforcement Learning-based Adaptive Control Model for Future Street Planning, An Algorithm and A Case Study

TL;DR

This paper introduces a reinforcement learning-based adaptive control model for future street planning that dynamically allocates road space among pedestrians, autonomous vehicles, and parking, improving safety and efficiency.

Contribution

It formulates the adaptive street control as a Markov Game and applies a multi-agent Deep Deterministic Policy Gradient algorithm, a novel approach in this context.

Findings

Achieved a 3.87% reduction in parking space allocation.

Achieved a 6.26% reduction in vehicular operation space.

Increased sidewalk proportion by 10.13%.

Abstract

With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%.