Edge-Level Real-Time Traffic Estimation with Limited Infrastructure

Blockchain, widely known as one of the disruptive technologies emerged in recent years, is experiencing rapid development and has the full potential of revolutionizing the increasingly centralized intelligent transportation systems (ITS) in applications. Blockchain can be utilized to establish a secured, trusted and decentralized autonomous ITS ecosystem, creating better usage of the legacy ITS infrastructure and resources, especially effective for crowdsourcing technology. This paper conducts a preliminary study of Blockchain-based ITS (B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS). We outline an ITS-oriented, seven-layer conceptual model for blockchain, and on this basis address the key research issues in B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS. We consider that blockchain is one of the secured and trusted architectures for building the newly developed parallel transportation management systems (PtMS) , and thereby discuss the relationship between B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS and PtMS. Finally, we present a case study for blockchain-based realtime ride-sharing services. In our viewpoint, B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS represents the future trend of ITS research and practice, and this paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.

Electronic Toll Collection (ETC) serves a major function in an Intelligent Transportation System (ITS). Recently, ETC has been widespread and prevalent within our national ITS infrastructure construction. However, with the advanced application of new technologies in ETC, people have to face lots of privacy issues. Based on privacy concerns and issues, the paper first analyzes privacy risks in ETC system and then conducts research on technical solutions to and legal strategies for privacy protection. Finally, the paper proposes suitable suggestions for solving privacy issues and risks in our domestic ETC system.

The intelligent transportation system has demonstrated its strong advantages in solving the urban transport problem. One of its important roles is able to reflect the traffic conditions timely through the floating car. The key problem is to find out the candidate road sections from the vast road network quickly. Then we make the floating car match to the corresponding road by the map-matching algorithm. So we can get the real location of the floating car on the map. Every floating car needs to select candidate road sections from the whole road network, so the computing time is an important factor in affecting the real-time performance of the whole system. The commonly used method is to build an ellipse according to the probability criterion. It needs to determine the size of the ellipse, which is based on the statistic theory. It also needs to find these road sections which are in the ellipse from the whole road network. The whole process is complicated and time-consuming. Therefore, this paper proposes the k-nearest neighbors algorithm based on KD tree to get the candidate road sections.

Advance Traveller Information Systems (ATIS) are considered a promising tool to alleviate traffic congestion and improve road network performance. They provide real time traffic information and route recommendation to road users, in order to increase their ability to choose the best alternative path. Though such systems have reached a high technical standard, their actual impact in traffic pattern and network performance is controversial. The methodology used is based on a Multi Agent Simulation to model how the presence of information influences the driver's reactive behavior and the network efficiency. The case study is the well known network of the Braess' paradox and the specific aim is to find the proper route recommendation strategy to avoid that adding a new road to traffic network may result in increasing the total travel time. Through a software platform able to simulate a virtual road network, where single drivers interact with each other and with the spatial environment according to a defined behavior, that is their reaction to external outputs, two behavioral patterns will be simultaneously considered. The first refers to the driver's path choice among those available for a fixed origin-destination pair; the second refers, once the path is chosen, to the microscopic motion of each vehicle as a function of the leader vehicle along each link of the network. To simulate the presence of drivers equipped with ATIS system and drivers who are not, or equivalently to simulate different reactive behavior to the information provided, it has been used a variable “probability of feedback”. Pattern arrival vehicle flow can be varied together with speed and acceleration of the vehicles. The general purpose of the paper is to contribute to the analysis of the impact of ITS (Intelligent Transport Systems) technology in traffic pattern and network performance. The specific objective is modelling driver's behavior in road networks when real time traffic information is provided. The results show that a proper rate of provided information is able to reduce the effect of the Braess' paradox and that network performance increases when drivers' behavior is affected by their ability to see local traffic conditions.

Prediction of accurate vehicle location is essential for various (Intelligent Transport System) ITS applications. Existing vehicle location systems are work on different position sensor and smart computing devices to execute map matching algorithm. There exist necessary needs for enhancing the performance of receivers in terms of position towards the error characteristics of (Global Position System) GPS signals. Simple map matching algorithm faces difficulties in testing on road trial. The proposed algorithm and approach overcome many of the drawbacks of existing methods and is capable of achieving higher accuracy, precise navigation in critical conditions for high carrier frequency signaling on road network.

Efficient fastest path computation on a large scale road network is an essential problem in logistics distribution, especially based on real-time traffic information. In this paper, we present an adaptive fastest path algorithm capable of efficiently accounting for the network with real-time traffic data. (1) Logistics vehicle routing between two cities usually choose the road network through the largest roads. According to this traveler character and the natural hierarchy present in road network, we classify the roads into largest roads and smaller ones. (2) Based on the largest roads and smaller roads, we can partition the road network into areas. (3) Finally, we propose an efficient A* algorithm that uses the road network partition to limit the search space and considers the real-time traffic information. Through an extensive experimental evaluation on real road network we show that our algorithm provides desirable routes, and that it is significantly fast and effective.

Information and communication technologies have improved the quality of Intelligent Transportation Systems (ITS). The real-time traffic information has traditionally been collected via stationary vehicle detectors or GPS-based probe cars. Compared to the traditional ways, estimating traffic information from Cellular Floating Vehicle Data (CFVD) is more cost-effective, and easier to acquire. In this paper, this study proposes a novel approach to evaluate the relation of call arrival, handover, traffic flow, and traffic density. Moreover, the traffic speed is estimated by the proposed approach according to CFVD. Through the analytical analysis, this study analyzes the effects of traffic information (e.g. traffic flow and vehicle speed) and communication behaviors (e.g. call arrival rate and call holding time) on handovers and call arrivals. In the simulation, this study compares the estimated traffic information with the real traffic information. The experiment results show that the accuracy of traffic speed estimation is 89.75%. Therefore, the proposed approach can be used to estimate traffic speed from CFVD for ITS.

The global positioning system (GPS) trajectory data are extensively utilized in various fields, such as driving behavior analysis, vehicle navigation systems, and traffic management. GPS sensors installed in numerous driving recorders and smartphones facilitate data collection on a large-scale in a high-frequency manner. Therefore, map-matching algorithms are indispensable to identify the GPS trajectories on a road network. Although the local map-matching algorithm reduces computation time, it lacks sufficient accuracy. Conversely, the global map-matching algorithm enhances matching accuracy; however, the computations are time consuming in the case of large-scale data. Therefore, this study proposes a method to improve the accuracy of the local map-matching algorithm without affecting its efficiency. The proposed method first executes the incremental map-matching algorithm. It then identifies the mismatching links in the results based on the connectivity of the links. Finally, the shortest path algorithm and the longest common subsequence are used to correct these error links. An elderly driver’s driving recorder data were used to conduct the experiment to compare the proposed method with four state-of-the-art map-matching algorithms in terms of accuracy and efficiency. The experimental results indicate that the proposed method can significantly increase the accuracy and efficiency of the map-matching process when considering high-frequency and large-scale data. Particularly, compared with the two-benchmark global map-matching algorithms, the proposed method can reduce the error rate of map-matching by nearly half, only consuming 18% and 58% of the computation time of the two global algorithms, respectively.

Urban traffic situation information is the basis of effective traffic guidance and traffic control. In order to promote communication and practical applications of traffic information, then can provide full, accurate, real time and sufficient traffic information service to public, government and correlative enterprises, in this paper, the authors proposed the dynamic traffic information collection and processing methods of intelligent transportation systems (ITS) common information platform (CIP) based on floating car technology. According to the characteristics of the ITS CIP data collection, they were required large scale, accurate and dynamic data collecting. A complete set of dynamic traffic information processing methods based on floating car technology were studied, including road data storage by two queues, error data elimination by taximeter, map matching by region dividing method and boundary rectangle, and traffic condition calculation by circle queues and A* algorithm. The application of the methods in the ITS CIP of Guangzhou City proved that the methods were quite effective and valuable to be popularized.

Effect of time interval on speed estimation using single loop detector measurements

Vehicle growth in Indonesia is not supported by the number of the road. This fact has caused traffic congestion easily occurred, especially in big cities. Intelligent Transportation System (ITS) is one of the solutions for this problem. In ITS, vehicle tracking and counting is a challenging issue for traffic surveillance. In this paper, Bhattacharyya distance-based tracking and vehicle counting is proposed. We propose a vehicle tracking method and vehicle counting system. We use Adaptive Background Learning with Hole Filling Algorithm to separate background and foreground in a frame. We detect objects in foreground region using contour of each object. Each detected object tracked using Bhattacharyya distance. Each tracked object is counted when the tracked object passed a predefined counting line. The proposed algorithm is evaluated by comparing tracking estimates with ground truth. We use precision and recall as metric to evaluate our tracking estimates with ground truth. Our vehicle counting result also evaluated by comparing to ground truth. Our tracking result have 96% recall in all experiment scenarios. Also, we have 1.3% and 7.4% error rate in our counting result compared to ground truth.

Because of shortage of raw materials, the energy-saving issues become popular in Vehicular Ad-hoc Networks (VANETs). A VANET is formed by traveling vehicles with communicating capability and thus it brings various applications. Navigation system is one of important applications in VANETs. The traditional navigation system usually plans a shortest path for users according to geographic maps but the planned path may become a slower one due to the traffic congestion. A congestion path not only delays the traveling time but wastes fuel. So the real-time traffic information should be considered while constructing a navigation path. In this paper, we propose a navigation system to find a fuel-saving navigation path by considering real-time traffic information. In order to estimate the traffic load on the future path, we maintain a table of historical traffic information by Intelligent Transport Systems (ITS). Furthermore, our system can dynamically change the navigation path by updating traffic information. In the simulation results, our approach can save more than 20% fuel consumption when vehicles travel in a traffic congestion environment.

Download a PDF of "Guidebook for Implementing Intelligent Transportation Systems Elements to Improve Airport Traveler Access Information" by the National Academies of Sciences, Engineering, and Medicine for free.

Speech recognition technology has potential for application to intelligent transportation systems (ITS) in the area of advanced traveler information systems (ATIS). ATIS areas which can greatly benefit from speech recognition are: pre-trip traffic information; en-route traffic information; route guidance; ride matching and reservation; transit information services; and traffic and transit management control centers. This paper concentrates on telephony applications of speech recognition to two ATIS applications, real-time traffic information and public transit information. We first discuss the general considerations involved in development of a multiline speech interface to a traffic or transit database, including system architecture and sizing, and the development of dialogs and grammars which are user friendly and efficient. We then discuss two prototype applications of this technology using BBN's HARK speaker-independent speech recognition product. The first is to SmartRoute System's SmarTraveler traveler information system, which was an FHWA Operational Test, and the second to Montgomery County Maryland's public transit system. These were small scale tests to help us develop the technology required for full-scale implementations, but showed that the speech interface was practical, and provided much improved ease of use and performance over conventional touch tone based interactive voice response (IVR) systems.

Intelligent Transport Systems consists of applications such as accident warning and avoidance systems. This is an area of interest to us in this research. The accident detection and alert system in the field of ITS (intelligent transportation system) are implemented and analyzed using many statistical methods for proving the accident detection point, GPS Coordinates points search, etc. In this paper, the analysis is performed on how accidents can be prevented using statistical analysis applied to the flow of vehicles in the construction area. This study helps to compare different statistical methods for vehicle detection in accident warning systems. Different statistical methods like Poisson’s probability distribution, Binomial distribution and Negative Binomial probability distributions were evaluated considering the count of vehicles and their distances from the area near or far from the prone area considered in the construction site. The aim of this paper is to evaluate and compare the statistical methods on the real-time datasets collected by hardware configuration of Arduino-Mega, Node-MCU and cloud service thingsspeak.com and shows that Poisson’s statistical distribution performs better than other methods in terms of probability of success. The different statistics like mean, standard deviation, and variance for different statistical methods are evaluated, out of these Poisson performs better.KeywordsAccident avoidanceIntelligent transportation system (ITS)Poisson’s distributionBinomial distributionNegative binomial distribution