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Traffic control through video/image processing is a trending research topic within Intelligent Transportation Systems. The number of moving vehicles, the density of vehicles at the junctions, traffic anomalies, and traffic flow directly influence real-time traffic congestion. The primary goal of this work is to design an architecture that is simple and fast enough to use in real-time heterogeneous traffic scenes. The proposed methodology uses user-defined zones for determining vehicle occupancy and count based on traffic surveillance video. The angular integral projection function accompanies the background subtraction method for better localization. Traffic anomaly detection is implemented by incorporating intelligent sensor technology. Also, the skipping of frames at fixed intervals has increased the processing speed with minimal data loss. The compatibility with any Internet Protocol camera further distinguishes this approach from other state-of-the-art methods. The proposed algorithm has been tested on a publicly available traffic surveillance video dataset and real-time surveillance feeds. Experimental results from the real-time intersection scenes show an overall accuracy of 97.14% and an average processing speed of 92.91%. The accuracy of the detection module in the custom-made dataset is 98.73%. [ABSTRACT FROM AUTHOR]

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads. For evaluation, the model has been simulated on a single intersection consisting of four roads: east, west, north, and south. A comparison of the developed adaptive quantized Q-learning (AQQL) framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5% over the greedy approach and 340% over the state-of-the-art approach. Hence, AQQL provides an effective traffic control that can be applied in today's intelligent traffic system. [ABSTRACT FROM AUTHOR]

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or partieswith zero trust,which impedes themulti-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the trade-off between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy. [ABSTRACT FROM AUTHOR]

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO 2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%. [ABSTRACT FROM AUTHOR]

The traffic control issue in the smart city scenario gives rise to the higher requirements of Global Navigation Satellite System (GNSS) services, especially in terms of navigation accuracy, together with coverage continuity, and multiplicity. The dense urban environment leads to higher elevation angles for navigation in such areas, which requires a lower altitude of the constellation, as well as a larger number of satellites. In the existing literature, the design and maintenance of the Low Earth Orbit (LEO) navigation constellation that fulfills the requirements of the smart city are not provided. Hence, based on the requirements and constraints of the smart city scenario, this article studies the relation between orbital height, user elevation angle, and coverage. It designs the configuration of an LEO navigation constellation that not only achieves global sensing coverage, but also provides a continuous lane-level navigation service with multiple coverages for the key area. In addition, considering the atmospheric drag in low orbits and the constraint of satellite power and attitude control, a method is proposed by rotating solar panels to change the effective frontal area of the satellite to achieve relative configuration maintenance of the LEO constellation. The results show that the LEO navigation constellation has a 0 s revisit time in five chosen smart cities, and each city has more than four-times coverage every second; the Geographic Dilution of Precision (GDOP) values of five cities are smaller than 0.47. The average navigation accuracy of five cities is 2.01. With the conduction of the one-year station-keeping simulation, the phase deviation of two satellites is less than 0.6° and it gradually converges to 0.1°, where the semi-major axis deviation is less than 80 m. With our proposed method, the active station-keeping control is not needed in one year, and the fuel consumption can be reduced. Finally, the continuity of the navigation service can be assured. [ABSTRACT FROM AUTHOR]

The growth of smart cities is fueled by the vast rise in wireless smart gadgets and uninterrupted connectivity. WiFi is the dominant wireless technology, enabling Internet-of-Things (IoT) connectivity in smart cities due to its ubiquitous access points and low deployment cost. However, smart city applications offer a wide range of services with different quality-of-service (QoS) demands. This paper addresses packet delivery latency as one of the QoS metrics affecting many time-sensitive smart city services. Thus, the paper proposes employing software-defined networking (SDN) to control the traffic load of WiFi access points (APs), preserving its symmetry in a city-wide coverage of WiFi-connected IoT gateways or fog nodes. These gateways receive data packets from smart city/IoT devices via wireless links and forward them over a city-deployed WiFi network to their management entities or servers. Three SDN-based algorithms are devised to reduce the gateways' packet-forwarding delay and keep a symmetric traffic load at the WiFi network APs. The algorithms are developed and tested using a real hardware setup constituting WiFi devices without additional requirements on the IoT gateways (WiFi clients) or the APs, such as support for a specific roaming protocol or bandwidth-consuming signaling such as sending probe packets. Extensive hardware experimentation shows that the SDN controller, via the proposed algorithms, can effectively reduce the packet forwarding latency of IoT gateways by carefully selecting the IoT gateway with the highest packet latency and seamlessly handing it over to the least-loaded covering AP. [ABSTRACT FROM AUTHOR]

Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities. The proposed system provides innovative services to the drivers that enable a view of traffic flow and the volume of vehicles available on the road remotely, intending to avoid traffic jams. The proposed model improves traffic flow and decreases congestion. The proposed system provides an accuracy of 95% and a miss rate of 5%, which is better than previous approaches. [ABSTRACT FROM AUTHOR]

Traffic congestion alleviation around intersections has been a growing challenge, and a competent traffic signal control scheme plays a pivotal role in intelligent transportation systems. Recent studies using deep reinforcement learning techniques have shown promising results for traffic signal control, but they only focus on extracting features from traffic conditions of isolated or adjacent intersections. In this work, we employed navigation information for traffic signal control, greatly enriching the features for traffic signal control with deep reinforcement learning. In addition, we are the first to propose a novel scheme DeepNavi to exploit the temporal-spatial relations from numerous navigation routes and extract dynamic real-time and future traffic features. We tested our scheme on a challenging real-world traffic dataset with 16 intersections in a residential district of Hangzhou, China. Extensive experiments were conducted and the results demonstrated that our DeepNavi scheme achieves superior performance over five popular and state-of-the-art baseline methods on different metrics, including queue length, speed, travel time and accumulative waiting time. In addition, with our method, vehicles suffer the least red lights and enjoy the most green waves, which further validates that our scheme greatly relieves the congestion and provides excellent experience for drivers. Simulations with different penetration levels of navigation routes showed that even with only part of navigation routes available in the traffic network, our scheme can obtain superior performance, further demonstrating the effectiveness and feasibility of DeepNavi. [ABSTRACT FROM AUTHOR]

Urban mobility is one of the critical challenges in modern cities that should be tackled carefully. The exponential growth of cars number badly impacts the transportation system (TS) on which most cities are living on. Traffic control is one of the most critical issues in TS, which depends on a set of cooperative traffic lights. Smart Traffic lights, which can receive and analyze traffic data, can solve traffic problems by efficiently predict the accurate waiting time for each traffic lane at the intersections. This can improve the traffic flow and accordingly promotes the transportation system performance. This paper introduces a Fog-Based Traffic Light Management Strategy (TLMS) based on Fuzzy Inference Engine. TLMS can accurately calculate the optimal waiting time for each traffic lane at the intersections to decrease the average waiting time for the stopped vehicles. TLMS applies Vehicle to infrastructure protocol (V2I) that allows vehicles to interact directly with the infrastructure of the road such as GPS sensors and the traffic light signals. At each traffic intersection, the number of waiting vehicles, their locations relative to TLMS, and their sizes are detected and sent to TLMS in real time. Then, based on fuzzy inference, TLMS can calculate the optimal waiting time for each lane, which optimizes the traffic flow at the intersection by minimizing the waiting time for the vehicles. The performance of the proposed TLMS has been compared and tested against recently proposed techniques via simulation. The results of the experiment showed that TLMS outperforms recent technologies as it minimizes the average waiting time of vehicles around the intersections and accordingly maximizes the performance of the traffic system. [ABSTRACT FROM AUTHOR]

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of '5' phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Driverless automatically guided vehicles are becoming a new trend in transportation envisioned in a smart city environment. Automatically guided vehicles depend on environmental data for executing target-oriented navigation and movements. The traffic knowledge can be used by automatically guided vehicles by measuring their intensity and delivery date in the green light. Therefore, considering sensor data's significance in this guided vehicle environment, this article presents the Internet of Things (IoT) assisted automated guided vehicle (IoT-AGV) scheme. IoT-AGV scheme discusses the implementation of an autonomous vehicle traffic control program at crossings. The traffic knowledge operator is ready to retrieve details from the sensor utilizing quick communication through a mobile network to measure optimum travel time and transmit the information collected to automated cars. In an automated guided vehicle (AGV) scheme, the automotive-into-loop model is introduced to eliminate delays. Error indicator integrates various hurdle imbalance statistics calculated from the sensor based on the road slide. A deep neural network focuses on a minimal collection of the most critical features, eliminating the fitting problem. This aids determine the error producing sensor data and alleviate them from fusion to enhance the target's accuracy. The experimental results show that the suggested system has been validated by the images from BIT vehicle datasets and enhances the accuracy ratio of 96.22% to measure optimum travel time and transmit the information collected to automated guided vehicles. [ABSTRACT FROM AUTHOR]

The traditional transportation system is based on a fixed-timed strategy to control the traffic congestion on urban roads. However, the increase of vehicle density in a smart city implies the variation and the conflict on the demand pattern of the drivers. The smart city development requires to create an efficient control plan for an intelligent traffic management system. In this paper, we aim to reduce the congestion at signalized intersections, and satisfy the needs of drivers according to their degree of displacement urgency. We use two optimization methods, namely the synchronization and the genetic algorithm (GA ), where we develop three scheduling protocols. The first is the intelligent context-aware negotiation protocol (ICANP ). This protocol allows the negotiating vehicles to cross the intersection. It enables each traffic light at the signalized intersection to negotiate the green time assigned to its phase. ICANP uses GA to optimize the crossing time in order to minimize the total waiting time of negotiating vehicles. Moreover, we introduce a negotiation protocol based on reputation, which minimises the congestion effect from incoming dishonest drivers. Finally, we propose an intelligent context-aware priority protocol (ICAPP ), that considers the existence of priority vehicles. Upon arrival of at least one priority vehicle at the signalized intersection, ICAPP interrupts the green time of negotiating vehicles. A series of simulation showed that the proposed protocols reduce the total waiting time and the emissions of CO 2 of vehicles at signalized intersection, in comparison with circular, ITLC and CATLS scheduling algorithms. Furthermore, formal complexity analysis and performance evaluation show the effectivity of our protocols. [ABSTRACT FROM AUTHOR]

• We investigated we can save optimizing delivery time and intensity of a street light. • Pre-defined regulation save a power consumption of 38%. • Traffic adaptive regulation save a power consumption of 60%. The aim of this work is to simulate and compare the energy savings potentially applicable to the consumption data of the Smart Street pilot system located at the ENEA Casaccia R.C. (Rome). The astronomical lighting system energy consumption (baseline) is compared to the simulation of a pre-defined regulation: it allows the lights dimming (and therefore a reduction of consumptions) based on a statistics averages of the traffic flow rate, differentiated according to the day of the week. Then the baseline consumption is compared to the simulation of an adaptive configuration based on the traffic flow rate. [ABSTRACT FROM AUTHOR]

Abstract The rapid development in machine learning and in the emergence of new data sources makes it possible to examine and predict traffic conditions in smart cities more accurately than ever. This can help to optimize the design and management of transport services in a future automated city. In this paper, we provide a detailed presentation of the traffic prediction methods for such intelligent cities, also giving an overview of the existing data sources and prediction models. [ABSTRACT FROM AUTHOR]

Modern cities are currently facing rapid urban growth and struggle to maintain a sustainable development. In this context, “eco-neighbourhoods” became the perfect place for testing new innovative ideas that would reduce congestion and optimize traffic flow. The main motivation of this work is a true and stated need of the Department of Transport in Nancy, France, to improve the traffic flow in a central eco-neighbourhood currently under reconfiguration, reduce travel times and test various traffic control scenarios for a better interconnectivity between urban intersections. Therefore, this paper addresses a multi-objective simulation-based signal control problem through the case study of “Nancy Grand Cœur” (NGC) eco-neighbourhood with the purpose of finding the optimal traffic control plan to reduce congestion during peak hours. Firstly, we build the 3D mesoscopic simulation model of the most circulated intersection (C129) based on specifications from the traffic management centre. The simulation outputs from various scenario testing will be then used as inputs for the optimisation and comparative analysis modules. Secondly, we propose a multi-objective optimization method by using evolutionary algorithms and find the optimal traffic control plan to be used in C129 during morning and evening rush hours. Lastly, we take a more global view and extend the 3D simulation model to three other interconnected intersections, in order to analyse the impact of local optimisation on the surrounding traffic conditions in the eco-neighbourhood. The current proposed simulation-optimisation framework aims at supporting the traffic engineering decision-making process and the smart city dynamic by favouring a sustainable mobility. [ABSTRACT FROM AUTHOR]

Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a graph-based decision making mechanism for smart transportation by gathering continuously the real-time city road traffic information. The data is harvested using the Internet of Things (IoT) based road sensors and vehicular network. The weight calculations are proposed using the current traffic parameters for various graph algorithms to make smart transportation decisions to facilitate the citizens as well as the metropolitan authorities. Furthermore, to process incoming Big Data from IOT devices by generating big graphs and respond to the users at real-time, we proposed an efficient architecture that uses the Apache GraphX tool with parallel processing servers of Hadoop ecosystem. Vehicular datasets representing the real road traffic are used for analysis and evaluation purpose. Moreover, the efficiency of the system is tested in terms of system throughput and processing time. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major mobility problem, which generates enormous economic resources and causes serious problems for the most dense of cities. Intelligent transportation systems based on vehicular ad-doc network (VANET) communications will improve many services, expressively, related to transport, security, reliability, management and including the assistance in the reduction of traffic congestion. In this context, we propose an intelligent system based, firstly, on a new clustering technique to control and maintain the stability of routes during inter-vehicular communications, and secondly, a bio-inspired systematically conducting mobility measurement of agent, intervening in cases of traffic congestion, in order to find new routes to those offered by the global positioning system. Obviously, by providing an appropriate route selection process, such routing concept could be helpful in exchanging control messages to inform the nearest medics willing to give first aid and routing of another alert to the nearest ambulance. Thus, in order to deal with these sophisticated optimization techniques, we evaluate in this article means of simulation experiments. According to that, we have anticipated particular incidents suitable to estimate the pertinence of the proposed system. The analysis and implementation in VANET will be based on three simulators, especially, SUMO, MOVE and NS2. The results prove the effectiveness of the approach by reducing fuel consumption and $$\hbox {CO}_{2}$$ emissions along with the rest of pollutant emissions in the case of an incident. [ABSTRACT FROM AUTHOR]

Traffic enumeration is a frequently used approach in smart city applications for estimating the traffic load of the road network. Many solutions use a camera and state-of-the-art image processing technologies. As image processing is relative resource consuming, either very simple methods are used, or the processing is performed off-site, on a central computer. The approach presented in this paper employs a powerful embedded system to process the video images in real-time, so that the communication traffic is limited to the transmission of retrieved statistics. In order to fit into the limited resources and still allow vehicle size and speed measurements, special data representations and processing pipeline has to be used. This paper presents the operation principles of the sensor, followed by the corresponding evaluation results. [ABSTRACT FROM AUTHOR]

The unprecedented growth of today's cities together with increased population mobility are fueling the avalanche in the numbers of vehicles on the roads. This development led to the new challenges for the traffic management, including the mitigation of road congestion, accidents, and air pollution. Over the last decade, researchers have been focusing their efforts on leveraging the recent advances in sensing, communications, and dynamic adaptive technologies to prepare the deployed road traffic management systems (TMS) for resolving these important challenges in future smart cities. However, the existing solutions may still be insufficient to construct a reliable and secure TMS that is capable of handling the anticipated influx of the population and vehicles in urban areas. Along these lines, this work systematically outlines a perspective on a novel modular environment for traffic modeling, which allows to recreate the examined road networks in their full resemblance. Our developed solution is targeted to incorporate the progress in the Internet of Things (IoT) technologies, where low-power, embedded devices integrate as part of a next-generation TMS. To mimic the real traffic conditions, we recreated and evaluated a practical traffic scenario built after a complex road intersection within a large European city. [ABSTRACT FROM AUTHOR]

One of the main concept objectives of smart cities is to create a quality living environment that is long-term sustainable and economically justified. In that context, modern cities are aware of the exposure to various forms of physical and non-physical pollution that needs to be remediated, eliminated or reduced. To achieve that it is necessary to quality determine the sources and reasons of each pollution. The most prominent examples of physical pollution that affects the quality of life of citizens in cities are light and noise pollution. Noise pollution or noise, is mostly the consequence of road and rail traffic in cities and it directly affects the health of citizens. Traffic control, reduction of peak congestion, dispersion and traffic redirection or building protective barriers, are ways that cities use to reduce the amount of noise or its effects. To make these measures efficient it is necessary to obtain the information related to the level of noise in certain areas, streets, cities. To achieve this, smart cities use noise mapping. The city of Zagreb since 2012, participates in the i-SCOPE project (interoperable Smart City services trough Open Platform for urban Ecosystems). i-SCOPE delivers an open platform on top of which it develops, three "smart city" services: optimization of energy consumption through a service for accurate assessment of solar energy potential and energy loss at building level, environmental monitoring through a real-time environmental noise mapping service leveraging citizen's involvement will who act as distributed sensors city-wide measuring noise levels through an application on their mobile phones and improved inclusion and personal mobility of aging and diversely able citizens through an accurate personal routing service. The students of Faculty of Geodesy University of Zagreb, who enrolled in the course Thematic Cartography, were actively involved in the voluntary data acquisition in order to monitor the noise in real time. In this paper are presented the voluntary acquisitioned data of noise level measurement in Zagreb through a mobile application named Noise Tube, which were used as the basis for creating the dynamic noise map. The paper describes how citizens through voluntarily collected geoinformation can directly influence decision-making in their community, which certainly affects the quality of life. [ABSTRACT FROM AUTHOR]

Many vehicular ad-hoc network protocols have been validated using complex urban mobility simulators or by means of the few publicly available real mobility traces. This work presents an extensive measurement campaign of the positions of a fleet of 370 taxi cabs moving around the city of Rome, Italy. Due to its street network and its traffic conditions, Rome presents a characteristic mobility pattern representative of an ancient city with heavy road congestion, and therefor provides a valuable test case to experiment VANET protocols. We exploit these traces to run a set of experiments to assess the performance of a simple epidemic protocol that we compare with the basic random waypoint model in order to quantify how far the performance metrics are from this baseline. The results show the possible outcomes of implementing data dissemination through an opportunistic network that uses taxi cabs as an information vector. [ABSTRACT FROM AUTHOR]

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Introduced a novel model of smart traffic control using moving vehicle-cameras. • Developed a GPU-based video processing module for an inside-car fog device. • Designed a lane and vehicle detection system as an initial step to detect violations. • Considered two use cases as traffic violations: (1) U-turn and (2) divider line violations. • Finally, the practicality of the system is evaluated on its accuracy and efficiency. Growing vehicular traffic in urban areas creates a mess for authorities to handle city traffic. With the lack of human resources, authorities are moving towards the use of smart and auto-traffic control systems to manage an increasing volume of traffic. Mostly, these systems monitor traffic using street cameras and identify illegal traffic behaviors, such as signal violations. However, it is not feasible to employ humans or static cameras everywhere in the city in order to cover all the urban roads. These days, modern automobiles come with cameras to store videos as a black-box in case of an accident. In this paper, we exploited the use of vehicular cameras and proposed a smart traffic control model to report any traffic violation on the road. To this end, the vehicle's camera monitors all front cars on the road and transmits videos to the car's attached fog device. The fog device analyses the captured video for unlawful behavior and reports to traffic authorities in case of any violation. Initially, front vehicles are recognized using Single Shot MultiBox Detector (SSD), whereas road lanes are marked using Hough transform. Later, the violations are identified using the violation-detection algorithm. As a use case, the algorithm is designed for the fog device to identify driving violations, including wrong U-turn and driving on a central divider line or a yellow line. The role of the fog device is implemented on a GTX750-Ti GPU-based machine. Finally, the system's performance is evaluated in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant focus on smart cities, on how they operate and develop, and on their technical and social challenges. The importance of infrastructure as a major pillar of support in cities, in addition to the rapid developments in smart city research, necessitate an up-to-date review of smart cities' infrastructure issues and challenges. Traditionally, a majority of studies have focused on traffic control and management, transport network design, smart grid initiatives, IoT (Internet of Things) integration, big data, land use development, and how urbanization processes impact land use in the long run. The work presented herein proposes a novel review framework that analyzes how smart city infrastructure is related to the urbanization process while presenting developments in IoT sensor networks, big data analysis of the generated information, and green construction. A classification framework was proposed to give insights on new initiatives regarding smart city infrastructure through answering the following questions: (i) What are the various dimensions on which smart city infrastructure research focuses? (ii) What are the themes and classes associated with these dimensions? (iii) What are the main shortcomings in current approaches, and what would be a good research agenda for the future? A bibliometric analysis was conducted, presenting cluster maps that can be used to understand different research trends and refine further searches. A bibliographic analysis was then followed, presenting a review of the most relevant studies over the last five years. The method proposed serves to stress where future research into understanding smart systems, their implementation and functionality would be best directed. This research concluded that future research on the topic should conceptualize smart cities as an emergent socio-techno phenomenon. [ABSTRACT FROM AUTHOR]

Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented. [ABSTRACT FROM AUTHOR]

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control. [ABSTRACT FROM AUTHOR]

Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods. [ABSTRACT FROM AUTHOR]