Search

Traffic congestion in urban areas poses several challenges to municipal authorities including pollution, productivity loss, reckless driving, and delays in dealing with emergencies. Smart cities can use modern IoT infrastructure to solve the congestion problem and reduce pollution and delays. In this article, we focus on congestion mapping and mitigation for emergency vehicles in smart cities. We use a novel traffic light control technique to change the flow of cars on lights of interest thereby making way for emergency vehicles. We use a simulation model for a selected area of Manhattan to implement congestion mapping and to help find the fastest path for routing emergency vehicles based on the congestion metrics. The system controls traffic lights to block off the roads feeding into congestion and allows flow away from the congested path. This helps in clearing the preferred route to help emergency vehicles reach the destination faster. We show that the proposed algorithm can map congestion on city roads with accuracy thus helping to improve the response time of the emergency services and saving precious lives. [ABSTRACT FROM AUTHOR]

Traffic congestion is becoming a serious problem with the large number of vehicle on the roads. In the traditional traffic control system, the timing of the green light is adjusted regardless of the average traffic rate at the junction. Many strategies have been introduced to solve and improve vehicle management. However, in order to handle road traffic issues, an intelligent traffic management solution is required. This article represents a self adaptive real-time traffic light control algorithm based on the traffic flow. We present a machine learning approach coupled with image processing to manage the traffic clearance at the signal junction. The proposed system utilizes single image processing via neural network and You Only Look Once (YOLOv3) framework to establish traffic clearance at the signal. We employed YOLO architectures because it is accurate in terms of mean average precision (mAP), interaction over union (IOU) values and fast in object detection tasks as well. It runs significantly faster than other detection methods with comparable performance. The average processing time of single image was estimated to be 1.3 s. Further based on the input from YOLO we estimated the 'on' time period green light for effective traffic clearance. Several real time parameters like number of vehicles (two wheelers, four wheelers), road width and junction crossing time are considered to estimate the 'on'time of green light. Moreover, we used the real traffic images to test the performance and trained the system with different dataset. Our experiments investigation reveals that the predicted vehicle counts were well matched with the actual vehicle count and proposed method apprehended an average accuracy of 81.1%. The reported strategy is self adaptive, highly accurate, fast and has the potential to be implemented in the traffic clearance at the junctions. [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]

With the progress of society and the acceleration of urbanization, the problem of multimedia assisted urban traffic is becoming increasingly apparent. Intelligent transportation system arises at the historic moment, but the road traffic information data accumulation of intelligent transportation system is quite large, and the information analysis is complex. Usually, there are huge amounts of data stored in the database of the traffic system, thus we need to analyze and manage the data with scientific methods. As the visualized tools, the multimedia based analysis for the on-time data conditions should also be integrated, therefore, how to effectively model the scenario is challenging. In this paper, we introduce and analyze the structure and related model of recurrent neural network, and apply RNN to traffic big data mining model. According to the characteristics of traffic flow, this paper analyzes the causes of the error data in the process of traffic data collection, and puts forward the corresponding processing methods. The proposed model uses the TensorFlow for development, and applies the proposed deep learning model to implement traffic data mining, and we use charts to visually show the prediction results. Experimental results show that proposed algorithm can effectively mine large traffic multimedia data and has good robustness. At the same time, the prediction accuracy has reached 97.36%. [ABSTRACT FROM AUTHOR]

Smart cities involve the provision of advanced services for road traffic users. Vehicular ad hoc networks (VANETs) are a promising communication technology in this regard. Preservation of privacy is crucial in these services to foster their acceptance. Previous approaches have mainly focused on PKI-based or ID-based cryptography. However, these works have not fully addressed the minimum information disclosure principle. Thus, questions such as how to prove that a driver is a neighbour of a given zone, without actually disclosing his identity or real address, remain unaddressed. A set of techniques, referred to as Attribute-Based Credentials (ABCs), have been proposed to address this need in traditional computation scenarios. In this paper, we explore the use of ABCs in the vehicular context. For this purpose, we focus on a set of use cases from European Telecommunications Standards Institute (ETSI) Basic Set of Applications, specially appropriate for the early development of smart cities. We assess which ABC techniques are suitable for this scenario, focusing on three representative ones-Idemix, U-Prove and VANET-updated Persiano systems. Our experimental results show that they are feasible in VANETs considering state-of-the-art technologies, and that Idemix is the most promising technique for most of the considered use cases. [ABSTRACT FROM AUTHOR]