Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

Zhao, Hongxing; He, Ruichun; Jiangsheng, Su

doi:10.5604/01.3001.0012.2109

Cited by 15 publications

(9 citation statements)

References 16 publications

(12 reference statements)

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Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

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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 Bee Colony (Abc)mentioning

confidence: 99%

“…ABC algorithm is widely used in optimizing traffic-related problems by previous researchers [60,68,96]. Zhao et al investigated a typical intersection as a case study at Lanzhou city [60]. The green time length of each phase of the signal cycle and signal cycle were considered as decision variables.…”

Section: Artificial Bee Colony (Abc)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

“…To overcome the limitations of mono-objective optimization methods and linear programming, Zhao et al proposed a non-dominated sorting artificial bee colony (ABC) multi-objective algorithm for optimizing delay and vehicle stops at unsaturated isolated signalized intersections [68]. It was found that the suggested method could efficiently solve the Pareto front by improving the stops and delay simultaneously.…”

Section: Related Workmentioning

confidence: 99%

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

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Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

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“…However, the general practice of optimizing signals to minimize delay does not necessarily minimize extra stops; hence emissions could increase [6][7][8]. Thereby, several studies [9][10][11] have been conducted to find a Pareto-optimal signal timings solution to balance delay and stops. Over time, in some studies, the balancing between delay and stops has shifted gradually to a tradeoff process between delay and sustainable metrics (e.g., fuel consumption and emissions) [6,7,[9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…However, most current literature does not differentiate between reducing fuel consumption and emissions [6,7,[9][10][11][12][13]. Thus, a question that needs to be raised is whether minimizing fuel consumption truly minimizes a few, some, or all emission types?…”

Section: Introductionmentioning

confidence: 99%

Impact of Various Operating Conditions on Simulated Emissions-Based Stop Penalty at Signalized Intersections

2021

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Sustainability has become one of the most important goals when optimizing traffic signals. This goal is achieved through utilizing various objective functions to reduce sustainability metrics (e.g., fuel consumption and emissions). However, most available objective functions do not distinguish between the reduction mechanism of various types of emissions. Further, such functions do not consider the compound impact of multiple operational conditions (e.g., road gradient) influencing emissions on the optimized signal plans. This study derives a new Environmental Performance Index representing a surrogate measure for emission estimates that can be used as an objective function in signal timings optimization to reduce emissions under various operational conditions. The Environmental Performance Index is a linear combination of delays and stops. The key factor of the Environmental Performance Index is the emissions-based stop penalty, which represents an emission stop equivalency measured in seconds of delay. This study also uses traffic simulation and emission models to investigate the compound impact of several operational conditions on the stop penalty. Results show that the stop penalty varies significantly with all the investigated conditions and that the stop penalty is unique for different types of emissions. These findings may have significant implications on the current practice of sustainable signal timing optimization.

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Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

Cited by 15 publications

References 16 publications

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

Impact of Various Operating Conditions on Simulated Emissions-Based Stop Penalty at Signalized Intersections

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