A better understanding on traffic light scheduling: New cellular GAs and new in-depth analysis of solutions

Villagra, Andrea; Alba, Enrique; Luque, Gabriel

doi:10.1016/j.jocs.2020.101085

Cited by 27 publications

(13 citation statements)

References 11 publications

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“…Andrea Villagra et al, proposed two versions of a Cellular Genetic Algorithm (CGA) to solve the traffic signal scheduling problem, namely the synchronous and asynchronous versions. They applied their approach to real cases in Spain and France [4], and the results show the superiority of their method. Yousef et al, proposed a history-based traffic management algorithm to tackle the traffic signal scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

A Mathematical Programming Model and a Firefly-Based Heuristic for Real-Time Traffic Signal Scheduling With Physical Constraints

2021

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Traffic congestion is one of the challenges that face urban cities' planners. It affects the environment as it increases the emissions of CO2 and affects the logistics systems as it may increase the travel time of different vehicles. Scheduling traffic signals is one of the ways to solve this problem. In the urban traffic signal scheduling problem, it is desired to get the optimum schedule for each considered traffic signal to maximize or minimize a specific objective function(s); these schedules determine the active and inactive traffic phases during each cycle time. In this paper, a mathematical programming model for solving the urban traffic signal scheduling problem is presented, the proposed mathematical model captures the physical constraints of the problem. Furthermore, a firefly-based rolling horizon approach is proposed to solve the problem. Both methods are used to solve a traffic-responsive system, which is considered the future of traffic control systems. The performance of both methods has been simulated using the SUMO traffic simulator to verify the solutions. The performance of the solutions was measured using the average queue length of the roads, the average waiting time, and the average travel time. The proposed methods have been applied to a real case study, and the results were remarkable.

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Section: Related Workmentioning

confidence: 99%

A Mathematical Programming Model and a Firefly-Based Heuristic for Real-Time Traffic Signal Scheduling With Physical Constraints

2021

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“…The application of AI has led some cities to thrive in their digitisation processes [84][85][86], turning them into more sustainable environments where the available resources are leveraged more intelligently. Even so, there still remain large urban areas that have not made sufficient progress in such digitisation, deeming this process as an unaddressed challenge as of today.…”

Section: Strengths Weaknessesmentioning

confidence: 99%

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

et al. 2021

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The 17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.

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“…Modular Timed Synchronized Petri Net model is proposed in [33] for traffic signal management to reduce environmental impact. Pareto max flow algorithm is presented in [34] and Cellular Genetic Algorithm is proposed in [35].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

Mohamed

Al-Shalfan

2021

Journal of Advanced Transportation

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The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.

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A better understanding on traffic light scheduling: New cellular GAs and new in-depth analysis of solutions

Cited by 27 publications

References 11 publications

A Mathematical Programming Model and a Firefly-Based Heuristic for Real-Time Traffic Signal Scheduling With Physical Constraints

A Mathematical Programming Model and a Firefly-Based Heuristic for Real-Time Traffic Signal Scheduling With Physical Constraints

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)

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