Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning

TL;DR

This paper presents a scalable multi-agent reinforcement learning framework that dynamically configures on-street parking to reduce traffic congestion, utilizing deep learning architectures to capture complex spatio-temporal patterns.

Contribution

It introduces a novel two-layer multi-agent RL framework with deep Q-learning, LSTM, and graph attention networks for optimizing parking configurations in urban areas.

Findings

Reduced average travel time loss by up to 47%

Effective parking configuration with minimal increase in walking distance

Validated on synthetic and real-world data from Melbourne

Abstract

With increased travelling needs more than ever, traffic congestion has become a major concern in most urban areas. Allocating spaces for on-street parking, further hinders traffic flow, by limiting the effective road width available for driving. With the advancement of vehicle-to-infrastructure connectivity technologies, we explore how the impact of on-street parking on traffic congestion could be minimized, by dynamically configuring on-street parking spaces. Towards that end, we formulate dynamic on-street parking space configuration as an optimization problem, and we follow a data driven approach, considering the nature of our problem. Our proposed solution comprises a two-layer multi agent reinforcement learning based framework, which is inherently scalable to large road networks. The lane level agents are responsible for deciding the optimal parking space configuration for each lane, and we introduce a novel Deep Q-learning architecture which effectively utilizes long short term memory networks and graph attention networks to capture the spatio-temporal correlations evident in the given problem. The block level agents control the actions of the lane level agents and maintain a sufficient level of parking around the block. We conduct a set of comprehensive experiments using SUMO, on both synthetic data as well as real-world data from the city of Melbourne. Our experiments show that the proposed framework could reduce the average travel time loss of vehicles significantly, reaching upto 47%, with a negligible increase in the walking distance for parking.