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Related Experiment Video

Updated: Sep 11, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Priority Control of Intelligent Connected Dedicated Bus Corridor Based on Deep Deterministic Policy Gradient.

Chunlin Shang^1,2, Fenghua Zhu², Yancai Xu²

¹College of Transportation, Ludong University, Yantai 264025, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This study integrates social vehicle traffic signal coordination with bus priority in dedicated lanes. The new approach significantly reduces average delays for all vehicles and decreases bus stops at intersections.

Keywords:

arterial control bus priority control deep reinforcement learning urban transportation

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Area of Science:

Traffic Engineering
Transportation Science
Intelligent Transportation Systems

Background:

Disparities exist between social vehicle and dedicated bus lane operations.
Current traffic signal coordination often yields sub-optimal results for mixed traffic.

Purpose of the Study:

To develop an integrated traffic signal control strategy for social vehicles and dedicated bus lanes.
To improve traffic flow efficiency and reduce delays for both general traffic and public transit.

Main Methods:

Analysis of travel time distributions and bus intersection passage likelihood.
Development of an incentive mechanism based on delay losses and bus priority benefits.
Implementation of a deep reinforcement learning framework for real-time signal adjustment.

Main Results:

Reduced average per capita delay by 38.63% (conventional flow) and 29.7% (saturated flow) compared to separate controls.
Decreased bus stops at intersections by 52.17% (conventional flow) and up to 39.5% (saturated flow).

Conclusions:

The integrated approach enhances traffic signal coordination effectiveness.
Significant improvements in traffic efficiency and public transit operations are achievable.