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... r is a uniform random number in the range [0,1]. Fig. 2 shows a traffic network with nine intersections, where traffic signals on every intersection are shown by the "Green" color or the "Red" color. A vector is employed to represent the traffic signals in one time interval and the values are shown in Table I. Two binary bits are used to show the traffic lights on different directions. For ...
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... This allows for measurement of the current state of roads in terms of traffic congestion and usage thereby allowing for the use of optimization techniques to improve trip experiences for users and make the transportation system more efficient. The authors in [119][120][121][122][123] work on the minimization of time (wait and travel) in traffic signal control. The aim of such systems is to reduce traffic build up on signal intersections. ...
... The aim of such systems is to reduce traffic build up on signal intersections. Of these, the work in [119][120][121] use the artificial bee colony and the genetic algorithm respectively for a single objective function of minimizing delay time. An interesting approach for this problem is presented by Li et al. [123] who use a multi objective formulation targeting the minimization of the average travel time both overall and individually for all vehiclesl. ...
... Data types for Smart Transportation.Self-collected/Presented/Generated[119][120][121][122][123][124]126,[128][129][130][131][132][133]135,138,139] GovernmentAgency/other research work[119,121,123,124,128,130,[134][135][136][137] ...
One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.
... The simulation results show that this modified PSO [29] is able to provide a better result than simple PSO in solving the TLCOP. In addition to PSO, several studies [31][32][33][34][35] have also attempted to use other swarm intelligent search algorithms to solve the TLCOP. In [31], de Oliveira and Ana used ant colony optimization (ACO) to solve the TLCOP in which they take into account the waiting time of all the vehicles for the green light in a lane at a particular time. ...
... In [31], de Oliveira and Ana used ant colony optimization (ACO) to solve the TLCOP in which they take into account the waiting time of all the vehicles for the green light in a lane at a particular time. In [32], Gao et al. used an improved artificial bee colony algorithm (ABC) with local search method to solve the TLCOP in urban areas. The simulation results show that ABC with local search method can provide a better result than common mixed-integer linear programming (MILP). ...
The traffic light cycle optimization problem (TLCOP) is certainly one of the most critical problems in a modern traffic management system because a “good solution” will be able to reduce the total waiting time of vehicles on roads of an entire city. In addition to heuristic algorithms, metaheuristic algorithms provide an alternative way for solving this optimization problem in the sense that they provide a better solution to adjust the traffic lights to mitigate the traffic congestion problem. However, there is still plenty of room for improvement. One of the open issues is that most metaheuristic algorithms will converge to a few regions at the later stage of the convergence process and thus are likely to fall into local optima. The proposed algorithm—a hybrid heuristic algorithm called grey wolf with grasshopper optimization (GWGO)—is developed to leverage the strength of grey wolf and grasshopper optimization algorithms. The underlying idea is to use the grey wolf optimization to avoid falling into local optimum too quickly while using the grasshopper optimization to dynamically adjust the convergence speed of the search algorithm. The experimental results show that the proposed algorithm is able to find out better results than all the other state-of-the-art search algorithms for the TLCOP evaluated in this study in terms of the quality of the end results.
... Similarly, deep reinforcement learning is proposed in [22]. Ant colony optimization approach is proposed in [23] and the artificial bee colony optimization approach is proposed in [24]. Social IoV [25] proposed for managing traffic is presented in [26]. ...
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.
... Bien que ces méthodes en ligne s'avèrent efficaces à petite échelle, elles sont généralement difficiles à transposer à l'échelle d'une ville entière [160,114,54]. En outre, la grande majorité des feux de circulation est encore paramétrée par des réglages fixes [9,55,121,132,137,106]. Aussi, les contributions présentées dans la section 2.4.1 ainsi qu'aux chapitres 3, 4 et 5 se concentrent essentiellement sur des problèmes d'optimisation hors ligne pour la recherche du réglage optimal des feux de signalisation préprogrammés, selon la vraisemblance du trafic urbain dans les villes étudiées. ...
... Finalement, il n'est pas inhabituel de rencontrer des approches quelque peu insolites parmi la profusion de littérature traitant du sujet. Par exemple, des algorithmes basés sur le comportement collectif de systèmes auto-organisés, aussi qualifiés par le terme de colonies d'abeilles, se montrent convaincants pour la recherche du réglage optimal des feux sur un réseau routier fictif composé de neuf intersections [55]. ...
La conception et la planification efficace des infrastructures urbaines représentent un enjeu essentiel pour les experts urbanistes qui souhaitent atteindre une mobilité fiable et durable pour les citadins. Les villes en perpétuelles évolution et les habitudes changeantes des populations incitent les urbanistes à réinventer constamment leur utilisation de l'espace urbain afin de contribuer à la fluidité des déplacements et à la sécurité des voyageurs, tout en limitant les impacts environnementaux des flux de trafic. De nombreuses méthodes d'optimisation permettent de résoudre une variété de problèmes associés à des questions de mobilité urbaine, tels que l'optimisation du réglage des feux de signalisation, la conception de réseaux routiers fiables ou la planification optimale d'un système de transports en commun. Toutefois, ces méthodes sont généralement conçues et paramétrées pour l'étude d'espaces urbains très spécifiques, et semblent difficilement généralisables à d'autres villes du monde pour l'optimisation de leur mobilité. Aussi, ce mémoire introduit une méthodologie pour la modélisation, la simulation et l'optimisation des flux urbains. Les travaux rapportés proposent une analyse rigoureuse des caractéristiques intrinsèques aux problèmes d'optimisation de la mobilité urbaine, afin de parvenir à une meilleure compréhension de ces défis, et de permettre la conception d'algorithmes d'optimisation robustes et efficaces. Finalement, les travaux rapportés par ce mémoire visent à susciter la réflexion sur des problèmes de mobilité, en s'appuyant sur les points de vue des experts urbanistes et des experts en optimisation, afin de parvenir à une mobilité urbaine optimale et durable.
... The latter are based on the collective behavior of self-organized systems. These works were carried on a synthetic network made of nine intersections, undergoing 16 different signals configurations [16]. ...
Finding optimal traffic light timings at road intersections is a mandatory step for urban planners wishing to achieve a sustainable mobility in modern cities. Increasing congestion situations constantly require urbanists to enhance traffic fluidity, while limiting pollutant emissions and vehicle consumption to improve inhabitants’ welfare. Various mono or multi-objective optimization methods, such as evolutionary algorithms, fuzzy logic algorithms or even particle swarm optimizations, help to reach optimal traffic signal settings. However, those methods are usually designed to tackle very specific transportation configurations.
Here, we introduce an extended version of the sialac benchmark, bringing together several real-world-like study cases with various features related to population, working activities, or traffic light devices. We drive a fitness landscape analysis on these various benchmark instances, which helps to improve the design of optimization algorithms for this class of real-world mobility problems. Thereby, we propose a new adaptive optimization algorithm to tackle each scenario of the benchmark.
... The work proposed in Gao, Zhang, Sadollah, and Su (2016) tries to solve the signal timing problem using a networked approach with a centralized model with an objective to minimize the networkwise total delay time. Another centralized method has been proposed in Gao, Zhang, Sadollah, and Su (2017) to minimize the network-wise total delay time of all vehicles within a fixed time window. Ghanim and Abu-Lebdeh (2015) have proposed a preferential signaling algorithm using a coordinated approach to reduce transit vehicle delay to improve schedule adherence of vehicles. ...
Computer vision-guided traffic management is an emerging area of research. Intelligent traffic signal control using computer vision is a less explored area of research. In this paper, we propose a new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model. First, we propose a new inference scheme that works approximately 5-times faster as compared to the one originally proposed in TUIC in a dense traffic intersection. The new inference scheme can trace clusters representing moving objects that may be occluded while being tracked. Cluster counts of approach roads have been used for signal timing for traffic intersections. It is done by detecting cluster motion inside the regions-of-interest (ROI) marked at the entry and exit locations of intersection approaches. Departure rates are learned using Gaussian regression to parameterize traffic variations. Using the learned parameters as a function of cluster count, an adaptive signal timing algorithm, namely Throughput and Average Waiting Time Optimization (TAWTO) has been proposed. Experimental results reveal that the proposed method can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms. We intend to publish two datasets as part of this work for enabling the research community to explore computer vision aided solutions for typical problems such as intelligent traffic controlling, violation detection in chaotic road intersections, etc.
... V2V/V2I communications [87], [152], [153], [154], [155], [156], [157], [158], [159] [92], [160], [159], [161], [162], [163], [164], [165], [166] [87], [91], [95], [167], [168], [169], [170], [171] [172], [173] [174], [175], [176], [177] [34], [35], [108], [178], [179], [180] [181] [182], [183], [184], [185] Traffic management Traffic prediction [186], [187], [188], [189], [190] [191], [192], [193], [194], [195] [ 196], [197], [198] [16], [186], [188], [189], [192], [193], [196], [199], [200] [196], [201], [202], [203], [204], [205], [206], [207], [208] [209], [210], [211], [212] - [213] Routing [214], [215], [216], [217], [218], [219], [220], [221] [214], [220], [222], [223], [224], [225], [226], [227], [228], [229], [230], [231], [232], [233], [234], [235] [214], [216], [220], [236], [223], [224], [225], [229], [232], [237], [238], [239] [240], [241], [242] [243] [214], [242], [244] [240], [245] [246], [247], [248], [249], [250], [251] Traffic congestion [252], [253], [254], [255], [256] [257], [258], [259] [254] [123], [260], [261] [261] [209], [254], [255], [259], [262] [254] [249], [263], [264] Signaling [265], [266], [267], [268], [269] [270], [271], [272], [273], [274], [275] [276], [277], [278] [272], [279], [280] [281], [282], [283] [272], [284], [285], [286], [287] [288], [289], [290] [251], [273], [281], [291], [292] Others (including decentralized traffic control, traffic monitoring, traffic characterization) [293], [294], [295], [296], [297] [293], [294], [296], [298], [299], [300], [301], [302], [303], [304], [305] [295], [304], [306], [307], [308], [309] [310], [311] [312], [313], [314] [294], [315] [14], [316], [317], [318] [319], [320] Smart city ...
... Olivera, Garcia-Nieto and Alba in [425] and [373] elaborate on the TLTP using PSO and SUMO. Other examples of bio-inspired optimization techniques applied to the TLTP and UTLSP can be respectively found in [427] (using multi-objective solvers) and [235], where an improved Artificial Bee Colony (ABC [428]) optimization approach is presented. ...
This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.
... Sanchez et al. [32,31] optimized traffic light controls with genetic algorithms employing microscopic traffic simulation and cluster computing. Gao et al. [9] on the other hand used artificial bee colony optimization for a similar task. Recently, Leprtre et al. [19] presented benchmark data for the optimization of traffic light controls and analysed the emerging fitness landscapes in a push for more universal traffic optimization algorithms. ...
This study addresses the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks. A macroscopic model, which strikes an appropriate balance between pedestrians’ needs and vehicle drivers’ needs, is employed to describe the traffic light scheduling problem in a scheduling framework. The objective of this problem is to minimize the total network-wise delay time of vehicles and pedestrians within a given finite-time window, which is crucial to avoid traffic congestion in urban road networks. To achieve this objective, the present study first uses a well-known optimization solver called GUROBI to obtain the optimal solution by converting the problem into mixed-integer linear programming. The obtained results indicate the computational inefficiency of the solver for large network sizes. To overcome this computational inefficiency, three novel metaheuristic methods based on the sine–cosine algorithm are proposed. These methods are denoted by discrete sine–cosine algorithm, discrete sine–cosine algorithm with local search operator, and discrete sine–cosine algorithm with local search operator and memory utilization inspired by harmony search. Each of these methods is developed hierarchically by taking the advantages of previously developed method(s) in terms of a better search process to provide more accurate solutions and a better convergence rate. To validate all these proposed metaheuristics, extensive computational experiments are carried out using the real traffic infrastructure of Singapore. Moreover, various performance measures such as statistical optimization results, relative percentage deviation, computational time, statistical analysis, and convergence behavior analysis have been employed to evaluate the performance of algorithms. The comparison of the proposed SCA variants is done with GUROBI solver and other metaheuristics namely, harmony search, firefly algorithm, bat algorithm, artificial bee colony, genetic algorithm, salp swarm algorithm, and harris hawks optimization. Overall comparison analysis concludes that the proposed methods are very efficient to solve the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks with different network sizes and prediction time horizons.
