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Related Experiment Video

Updated: Jul 20, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network.

Xinghui Zhang1,2, Xiumei Fan1, Shunyuan Yu2

  • 1Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new optimization method for urban intersection traffic signals, reducing delays by over 30% without new infrastructure. The novel approach balances traffic efficiency and capacity for smoother urban mobility.

Keywords:
NSGA-IIIdenoising autoencoderintelligent transportationmulti-objective optimizationsignalized intersections

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Area of Science:

  • Traffic Engineering
  • Optimization Algorithms
  • Urban Planning

Background:

  • Urban intersections are major sources of traffic congestion, necessitating efficient traffic flow regulation.
  • Effective signal timing is critical for urban traffic operations, requiring robust traffic models and optimization algorithms.

Purpose of the Study:

  • To develop an advanced optimization method for signalized intersections that improves traffic efficiency.
  • To integrate a multi-objective model with a novel NSGAIII-DAE algorithm to address complex traffic dynamics.

Main Methods:

  • Constructed a multi-objective model incorporating signal control delay, traffic capacity, and conflict delay from right-turning vehicles.
  • Employed a denoising autoencoder (DAE) to create a compact representation of high-dimensional search spaces for the NSGA-III algorithm.
  • Performed genetic operations in the compressed space, mapping back to the original space to balance local and global search.

Main Results:

  • Achieved significant reductions in signal control delay (up to 33.7%) and conflict delay (up to 31.3%).
  • Improved traffic capacity by 11.5% compared to current schemes, despite a lower capacity value than a compared algorithm.
  • Demonstrated enhanced traffic efficiency and a better balance between traffic efficiency and intersection capacity.

Conclusions:

  • The proposed NSGAIII-DAE algorithm effectively optimizes signal timing for urban intersections.
  • The method successfully balances traffic efficiency and capacity without requiring additional infrastructure.
  • Validated through numerical experiments with real traffic data, showing significant improvements over existing methods.