Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

Nesmachnow, Sergio; Массобрио, Рензо; Arreche, Efraín; Mumford, Christine L.; Olivera, Ana Carolina; Vidal, Pablo Javier; Tchernykh, Andrei

doi:10.1016/j.ijtst.2018.10.002

Cited by 30 publications

(16 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Maximum capacity, delay, and overflow at intersection were chosen as optimization objectives. Bargegol et al proposed a novel method based on the kinematic wave theory to optimize the signal timing plan at the signalized intersection [31]. The study conducted that the proposed algorithm was more efficient in reducing delays and enhance the level of service for signalized intersections.…”

Section: Previous Studiesmentioning

confidence: 99%

“…The past decade has witnessed rapid development in information and communication technologies such as fourth generation (4G), dedicated short range communication (DSRC), Internet of Things (IOT) sensors that provide unprecedented opportunities for detailed data collection. Consequently, the fourth class of methods are based on simulations and intelligent algorithms to enhance operations at signalized intersections by optimizing cycle length, green splits and offsets [28][29][30][31]. Methods based on simulation and heuristics are more realistic, robust, and efficient in capturing the non-linear and stochastic traffic conditions [32,33].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

View full text Add to dashboard Cite

Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

show abstract

Section: Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

View full text Add to dashboard Cite

show abstract

“…An interesting study selecting the best charging station for electric vehicles according to the traffic condition to minimize commuting time is presented in [41]. A parallel algorithm to synchronize intersections in large and dense zones suggesting improving the average speed-based Bus Rapid Transit is proposed in [42]. A similar study based on a hybrid heuristic approach is presented in [43].…”

Section: Related Workmentioning

confidence: 99%

“…As stated before, this study does not require any extra hardware like counting cables, computer vision, and camera systems [21,36]. ere is no need for expensive and complex systems to extract useful features such as computer vision or deep learning approaches [5,39,40] or parallel processing [42].…”

Section: Related Workmentioning

confidence: 99%

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

Mohamed

Al-Shalfan

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

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.

show abstract

“…Los experimentos se llevaron a cabo en tres áreas de la ciudad de Montevideo, Uruguay. Recientemente, Nesmachnow et al (2019) han aplicado su propuesta al problema de Autobús de Tránsito Rápido logrando mejoras con respecto a versiones secuenciales.…”

Section: Introductionunclassified

Movilidad inteligente: reducción de emisión de gases

Carballo¹,

Villagra²,

Errecalde³

2019

ICTUNPA

View full text Add to dashboard Cite

El concepto de movilidad inteligente se enmarca en el conjunto de acciones, técnicas e infraestructuras encaminadas a la mejora de la movilidad y organización del tráfico en las ciudades. Durante las últimas décadas uno de los principales problemas en las diferentes ciudades del mundo, es el congestionamiento vehicular y los efectos que esto ocasiona. Un mayor nivel de contaminación, emisiones y consumo de combustible se convirtieron en serios problemas. Aumenta también la cantidad de semáforos en las grandes urbes y la complejidad para su programación. En este trabajo se utilizan dos algoritmos metaheurísticos, el algoritmo genético celular (cGA) y el algoritmo de Recocido Simulado (en inglés, Simulated Annealing, SA) para optimizar la planificación de los programas de ciclos semafóricos. Las soluciones obtenidas por los algoritmos aplicados a dos grandes escenarios cercanos a la realidad en áreas urbanas ubicadas en las ciudades de Málaga (España) y París (Francia), son simuladas por el popular micro-simulador SUMO (Simulator Urban MObility). La comparación con un enfoque paralelo de la literatura revela la bondad del algoritmo genético celular. Además, se muestra una reducción significativa en términos de las tasas de emisión y del consumo total de combustible por parte del algoritmo genético.

show abstract

Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

Cited by 30 publications

References 24 publications

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

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

Movilidad inteligente: reducción de emisión de gases

Contact Info

Product

Resources

About