How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm

Fu, Xiancheng; Gao, Hengqiang; Cai, Hongjuan; Wang, Wenwen; Chen, Wei-Ming

doi:10.3390/s21082631

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…As observed in Figure 4, the proposed innovative method enabled considerable diminution of mean vehicle delay, exhibiting a 29.70% attenuation relative to the fixed time scenario. It outperforms literature-documented diminutions of 21.39% [32], 10.25% [33], and 15.64% [34], as well as the 26.3% attained via actuated signal control implementation. The substantial advancements are attributed to the methodology's efficacy in optimizing traffic progression by integrating real-time vehicular volumetrics for the dynamic management of signals.…”

Section: Delay Of Vehiclesmentioning

confidence: 54%

“…Figure 4 compares the reductions in transportation metrics produced by the proposed work, actuation control approaches, and previous research findings compared to the standard fixed-time traffic control. As shown in Figure 4, the proposed technique exhibits higher performance in various crucial parameters than traditional fixed-time methods, actuated control systems, and other research efforts recorded in papers [31][32][33][34]. More precisely, the suggested approach demonstrates a notable improvement in minimizing the delay of vehicles, reducing fuel usage, decreasing travel time, and reducing the number of vehicles in the intersection, indicating a more effective traffic flow and energy utilization.…”

Section: Results Discussion and Benchmarking Against Existing Approachesmentioning

confidence: 89%

“…To evaluate the model's efficiency, comparative experiments were conducted to analyze its performance in different circumstances. The purpose of these tests was to assess the enhancement of the algorithm in contrast to the default fixedtime scheduling approach, the actuated control, and four different recent methodologies [31][32][33][34]. The actuated control scheme is common in Germany.…”

Section: Results Discussion and Benchmarking Against Existing Approachesmentioning

confidence: 99%

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Beyond Sensors: IntelliSignal’s Map-Integrated Intelligence in Traffic Flow Optimization

Sreejith,

Mathi,

Pradeep

2024

IEEE Access

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The burgeoning growth of vehicular traffic, fuelled by rapid urbanization and an everexpanding population, has resulted in congested road networks. Traditional and sensor-based adaptive traffic light management systems have shown commendable progress in some scenarios, but they suffer from inherent disadvantages that hinder their effectiveness and scalability. To combat these challenges, a dynamic and adaptable traffic control system is imperative to optimize traffic flow. The present paper explores the limitations of sensor-based adaptive traffic light management and advocates for integrating a novel algorithm to overcome the existing drawbacks. It proposes an intelligent traffic management algorithm called IntelliSignal that leverages the map service for fetching real-time traffic information to calculate the optimal green time and a penalty-based road selection to optimize traffic flow. The proposed IntelliSignal is designed to provide equal chances for all roads while prioritizing higher-density roads with more green time, effectively mitigating traffic congestion and improving overall transportation efficiency. It incorporates Qlearning, a reinforcement learning technique that enables the system to adapt and learn from traffic patterns. The proposed IntelliSignal's performance is assessed through rigorous evaluations conducted on the simulation platform SUMO. The acquired results demonstrate substantial enhancements across various crucial metrics, including average waiting time, vehicle density, travel time, CO2 emissions, and queue length. Furthermore, the simulation results demonstrate that the proposed IntelliSignal algorithm exhibits a remarkable 30.52% increment in system throughput compared to the traditional approach. This significant enhancement underscores the efficacy of the proposed IntelliSignal in optimizing system performance and merits consideration for practical implementation.

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Section: Delay Of Vehiclesmentioning

confidence: 54%

Section: Results Discussion and Benchmarking Against Existing Approachesmentioning

confidence: 89%

Section: Results Discussion and Benchmarking Against Existing Approachesmentioning

confidence: 99%

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Beyond Sensors: IntelliSignal’s Map-Integrated Intelligence in Traffic Flow Optimization

Sreejith,

Mathi,

Pradeep

2024

IEEE Access

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“…Hence, they have been widely used to solve the traffic light control non-convex problem. The work (Fu et al, 2021) employed a labour division model set by ACO to adjust green time durations of phases at intersections. The obtained results showed improvements in terms of three performance indicators; intersection capacity, vehicles delay, and vehicles stop number.…”

Section: Related Workmentioning

confidence: 99%

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Yektamoghadam,

Nikoofard,

Behzadi

et al. 2023

Expert Systems

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Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi‐criteria decision‐making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub‐optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and Teaching‐Learning‐based Optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real‐world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.

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“…Human factors impact on traffic are evaluated in [ 57 ] and proved that they have a big impact on traffic stability and can lead to sudden traffic breakdowns. A more targeted approach used to improve urban traffic and reduce congestion is the work in [ 58 ]. The proposed solution tries to optimize the lighting systems at the road network intersections by applying the Swarm Intelligence Algorithm which considers the average delay time of vehicles, the average number of stops of the vehicles and the traffic capacity as the evaluation indexes.…”

Section: Related Workmentioning

confidence: 99%

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Stan

Suciu

Potolea

2021

Sensors

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Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

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How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm

Cited by 14 publications

References 25 publications

Beyond Sensors: IntelliSignal’s Map-Integrated Intelligence in Traffic Flow Optimization

Beyond Sensors: IntelliSignal’s Map-Integrated Intelligence in Traffic Flow Optimization

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

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