scholarly journals A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 280
Author(s):  
Yanjun Shi ◽  
Yuhan Qi ◽  
Lingling Lv ◽  
Donglin Liang
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Particle Swarm ◽  
Traffic Signal ◽  
Particle Swarm Optimisation ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Interval Model ◽  
Real Time Traffic

Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.

Real-time traffic signal control with swarm optimization methods

Measurement ◽  
2020 ◽  
Vol 166 ◽  
pp. 108206 ◽  
Author(s):  
Seyit Alperen Celtek ◽  
Akif Durdu ◽  
Muzamil Eltejani Mohammed Alı
Keyword(s):  
Real Time ◽  
Optimization Methods ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Swarm Optimization ◽  
Real Time Traffic

scholarly journals A Real Time Traffic Signal Control by Self-Evaluating Delay

2003 ◽  
Vol 20 ◽  
pp. 879-886 ◽  
Author(s):  
Miho ASANO ◽  
Akira NAKAJIMA ◽  
Ryota HORIGUCHI ◽  
Hiroyuki ONEYAMA ◽  
Masao KUWAHARA ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Real Time Traffic

Analysis of Trends in Data on Transit Bus Dwell Times

2017 ◽  
Vol 2619 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Isaac K. Isukapati ◽  
Hana Rudová ◽  
Gregory J. Barlow ◽  
Stephen F. Smith
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Distribution Model ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Real Time Control ◽  
Arrival Times ◽  
On The Road

Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles—with frequent stops and uncertain dwell times—may have different flow patterns that fail to match those plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This situation results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues, primarily by addressing delay to the transit vehicles. However, existing TSP strategies give unconditional priority to transit vehicles, exacerbating quality of service for other modes. In networks for which transit vehicles have significant effects on traffic congestion, particularly urban areas, the use of more-realistic models of transit behavior in adaptive traffic signal control could reduce delay for all modes. Estimating the arrival time of a transit vehicle at an intersection requires an accurate model of dwell times at transit stops. As a first step toward developing a model for predicting bus arrival times, this paper analyzes trends in automatic vehicle location data collected over 2 years and allows several inferences to be drawn about the statistical nature of dwell times, particularly for use in real-time control and TSP. On the basis of this trend analysis, the authors argue that an effective predictive dwell time distribution model must treat independent variables as random or stochastic regressors.

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