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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

A reinforcement learning approach for reducing traffic congestion using deep Q learning.

S M Masfequier Rahman Swapno¹, S M Nuruzzaman Nobel¹, Preeti Meena²

¹Department of CSE, Bangladesh University of Business and Technology, Dhaka, Bangladesh.

Scientific Reports

|December 12, 2024

Summary

This summary is machine-generated.

This study introduces a novel reinforcement learning (RL) method to combat global traffic congestion. The RL approach significantly reduced traffic queue lengths by 49%, enhancing urban transport efficiency and sustainability.

Keywords:

Agent DQL Intersection Queue length RL Rewards Smart city Traffic reduction

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Area of Science:

Artificial Intelligence
Transportation Engineering
Urban Planning

Background:

Global traffic congestion is a critical issue, exacerbated by increasing vehicle numbers and insufficient infrastructure.
Traffic congestion leads to detrimental environmental pollution and negatively impacts public health.
Existing transportation systems struggle to cope with escalating traffic demands.

Purpose of the Study:

To propose and evaluate a novel reinforcement learning (RL)-based method for reducing traffic congestion.
To demonstrate the effectiveness of a Deep Q-Network (DQN) in managing urban traffic flow.
To improve transport efficiency and sustainability in metropolitan areas.

Main Methods:

Development and integration of a sophisticated Deep Q-Network (DQN) model.
Implementation of an RL-based system for real-time traffic management.
Utilizing RL algorithms to optimize traffic signal control and lane incentives.

Main Results:

A significant reduction in traffic queue lengths by 49% was achieved.
Incentives for each traffic lane were increased by 9% through the RL system.
The study validated the effectiveness of the RL method in setting traffic reduction standards.

Conclusions:

Reinforcement learning (RL) shows substantial potential for improving urban transport efficiency and sustainability.
The proposed DQN-based method offers a viable solution for easing traffic congestion in metropolitan areas.
RL-driven traffic management can significantly enhance standards for traffic reduction and urban mobility.