Networked Sensor-Based Adaptive Traffic Signal Control for Dynamic Flow Optimization

With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that l...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 11; p. 3501
Main Authors: Wang, Xinhai, Shao, Wenhua
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.06.2025
MDPI
Subjects:
adaptive control
Algorithms
Control equipment
Controllers
Efficiency
Emergency communications systems
Energy consumption
Genetic algorithms
Methods
Motor vehicle fleets
Optimization
PID controller
Radar systems
sensor networks
Sensors
Traffic congestion
Traffic control
Traffic flow
traffic signal timing
Urban areas
China
PID controller
adaptive control
sensor networks
traffic signal timing
traffic flow
ISSN:1424-8220, 1424-8220
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s25113501