Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy

Qiao, Zhimin; Ke, Liangjun; Zhang, Gewei; Wang, Xiaoqiang

doi:10.1007/s10489-021-02256-y

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…Results showed that MAIS achieved better results compared to most of the other metaheuristics. In a recent study, Qiao et al proposed a novel hybrid algorithm, known as the Immune-Fireworks algorithm (IM-FWA) for effective traffic management on large-scale urban transportation networks [106]. The proposed hybrid algorithm was developed based on fireworks and artificial immune algorithms.…”

Section: Artificial Immune System (Ais)/immune Network Algorithm (Ina)mentioning

confidence: 99%

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Jamal¹,

Al-Ahmadi²,

Butt³

et al. 2023

Search Algorithm - Essence of Optimization

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Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review.

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Section: Artificial Immune System (Ais)/immune Network Algorithm (Ina)mentioning

confidence: 99%

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Jamal¹,

Al-Ahmadi²,

Butt³

et al. 2023

Search Algorithm - Essence of Optimization

View full text Add to dashboard Cite

show abstract

“…Traffic delays caused by excessive vehicles represent a major challenge in modern transportation systems. To solve this problem, Qiao [40] proposed a new algorithm, called the immune fireworks algorithm. In 2021, an improved multiple disturbances fireworks algorithm was proposed to solve the combinatorial optimization problem of three-valued variables [41].…”

Section: A Research Progress Regarding Theory and Applications Of Fir...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…With the development of intelligent transportation, there is some updated work on intelligent traffic signal control systems, such as [5][6][7][8]. ese new research results bring to our control system a better signal timing schedule to effectively reduce traffic delays.…”

Section: Existing Workmentioning

confidence: 99%

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Luo

Zhang

2021

Discrete Dynamics in Nature and Society

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In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.

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Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy

Cited by 12 publications

References 36 publications

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

A survey on several new popular swarm intelligence optimization algorithms

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

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