Traffic Information Collection System Research Articles

Research on Optimization Model of Multisource Traffic Information Collection Combination Based on Genetic Algorithm.

In order to reduce the excessive use of multisource traffic information collection system, a multisource traffic information collection combination optimization mode is proposed based on genetic algorithm in this paper. This model is mainly used to analyze the traffic management data in the city. According to the collected data information, the characteristics of the traffic equipment can be effectively analyzed. Basing on the market demand and supply relationship, the multisource traffic information collection combination optimization model is used to complete the reorganization and optimization of the traffic information in this paper, to acquire the main convolution feature variables of the model. The data information combination processing is performed according to the acquired feature variables, and the genetic algorithm is used to adjust the multisource traffic information. During the process of information fusion data analysis, the multisource traffic information clustering and fuzzy constraint control can be performed effectively to realize the optimization of the team's traffic information collection combination. Finally, the simulation results show that the method proposed in this paper is more accurate in realizing the optimization process of multisource traffic information collection and combination and has a better degree of information fusion.

TrafficChain: A Blockchain-Based Secure and Privacy-Preserving Traffic Map

Intelligent Connected Vehicles (ICVs) can provide smart, safe, and efficient transportation services and have attracted intensive attention recently. Obtaining timely and accurate traffic information is one of the most important problems in transportation systems, which would allow people to select fast routes and avoid congestions, thus saving their travel time on the road. Currently, the most popular ways to obtain traffic information is to inquire navigation agents, e.g., Apple map, and Google map. However, these navigation agents are essentially centralized systems, which are vulnerable to service congestions, a single point of failure, and attacks. Furthermore, users' privacy gets compromised as the agents can know their home and work addresses and hence their identities, track them in real-time, etc. In this paper, we propose TrafficChain, a secure and privacy-preserving decentralized traffic information collection system on the blockchain, by taking advantage of fog/edge computing infrastructure. In particular, we employ a two-layer blockchain architecture in TrafficChain to improve system efficiency, design a privacy-preserving scheme to protect users' identities and travel traces, and devise LSTM based deep learning mechanisms that can defend against Byzantine attacks and Sybil attacks in our system. Furthermore, an incentive mechanism is designed to motivate users to participate in the system. Simulation results show that TrafficChain works very efficiently and is resilient to both Byzantine attacks and Sybil attacks.

Wireless Sensor Traffic Information Collection System Based on Congestion Control Algorithm

<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">In order to develop a fast and convenient traffic information collection system, the wireless sensor traffic information collection system based on congestion control algorithm is designed. This new system reduces the operation and maintenance costs and improves the efficiency of traffic information collection. By analyzing the wireless sensor network, the application of wireless sensor network in intelligent traffic information collection system is discussed. In addition, the design scheme of traffic information collection system is designed. After analyzing the size of the network in the scheme, the congestion problem in the scheme is discussed finally. The simulation results show that the node communication radius is 50 m when the simulation scene is located in the monitoring area, while the MAC protocol is 802.11 DCF and the initial energy of all nodes is 1 J. Based on the above finding, it is concluded that the congestion control algorithm has better throughput, network delay and network energy consumption, which is suitable for the wireless sensor traffic information collection system.</span></span></p>

Study on Path Optimization Method of Urban Traffic Flow Guidance System Based on Internet of Things

This paper analyzes the existing traffic guidance systems and conducts a detailed explanation of things based on traffic flow guidance system and describes its characteristics and advantages and disadvantages. After a comparison beteween them, this paper designs the optimal way and introduce its features detaily. On this basis, this paper studies the structure and composition traffic information system, including the service application platform designed to induce traffic information systems and traffic information collection system and vehicle location system to provide basic information on traffic flow guidance system design. This paper analyzes the general method of route optimization to build a theoretical model based route optimization and this lay a foundation for things urban traffic guidance system path optimization.

Traffic State Forecasting of Typical Roads in Beijing

The comprehensive analysis of traffic states are effective ways to eliminate some traffic problems and enhance the supervising ability of the traffic management departments, which includes real-time monitoring, evaluation and prediction the states of the entire city. With analyzing the traffic flow characteristic on several typical roads in Beijing and summarizing the existing traffic prediction models, this paper proposes a combination forecasting model. It is mainly based on the nonparametric regression model. The combination of Fourier's history estimated model, nonparametric autoregressive model and nonparametric neighborhood regression model are used to predict the traffic state of typical Roads. Considering the reality of the traffic flow information collection system of Beijing and the needs of predicting information released by graphical way in the future, the paper also presents the heterogeneous data fusion methods and road traffic code model.

Traffic Information Collection System for Congestion Identification and Relief

While research focuses on using available traffic data sources to identify and relieve traffic congestion, less work is devoted answering the question of how and where to collect traffic data, so that traffic control systems can perform in an optimal, and cost-efficient manner. In this paper, We present a framework to assess traffic detection systems, by introducing the level of detection as value to allow for an objective comparison of multiple detector placement scenarios. The framework allows the usage of the framework for network operations, as well as planning purposes. By translating traffic operation goals into data demand functions, and detector capabilities, combined with their location, into data supply functions, it is possible to optimize detector locations with well know tools from operations research. The latter one is important, since it allows for including additional boundary conditions, such as costs.

Applied Research on Traffic Information Collection Based on Wireless Sensor Networks

Traditional traffic information collection network bears some disadvantages, such as high-concentrated system load, low-usage of network, limited network pavement, and shocking operation cost and so on. Therefore, this paper proposes to design a brand new traffic information collection system based on wireless sensor networks, in which the distributed treatment of traffic information collection is realized. This paper also puts forward the structure framework of traffic information collection system based on wireless sensor network, and discusses the potential problems in the implementation and related resolution strategies.

Mixed Traffic Information Collection System based on Pressure Sensor

Abstract The traffic information collection is the base of Intelligent Traffic.At present, there exist mixed traffic situation in urban road in China. This paper researched and implemented a system through collecting the vehicle and bicycle mixed traffic flow parameters based on pressure sensor. According to information collection requirements, we selected pressure sensor, designed the data collection, storage and other hardware circuitries and information processing software. The experiment shows that the system can meet the demand of traffic information collection in the actual.

다중 가상 검지선을 이용한 실시간 교통정보 수집

ATIS(Advanced Traveler Information System)는 교통 상황이나 주행 정보 등을 실시간으로 제공하여 이용자의 편익을 도모하는 시스템이다. ATIS를 위해 영상 이미지를 분석하여 교통정보를 수집 수집하는 방식은 크게 영역에 루프 검지기를 설정하여 측정하는 방식과, 영상 분석을 통해 차량을 검출하고 추적하는 방식으로 나뉜다. 본 연구에서는 차선마다 가상 검지기를 설치하는 방식을 기초로, 영상 분석 방법의 차량 위치 추적 방식을 응용하여 교통정보를 측정하는 시스템을 제안한다. 이때 가상 검지기 방식의 단점을 보완하기 위해 차선마다 다중의 가상 검지선을 설정하여, 검지 영역 내에 여러 대의 차량이 진입하거나 차량의 차선 변경 시에도 효과적으로 교통정보를 추출할 수 있게 하였고, 영상분석 방식의 문제점인 검지영역의 크기 증가에 따른 차량 추출 시간의 증가를 해결하기 위해 검지 영역 내에서 대표 픽셀을 추출하여 교통정보를 측정하였다. CCTV를 이용하여 취득한 영상에 제안하는 시스템을 이용하여 차량의 평균 속도와 차선별 교통량 측정한 결과 실제 교통량대비 평균 92.32%의 차량 검지율를 보였다. ATIS(Advanced Traveler Information System) is the system to offer a real-time traffic information or traffic situation for the benefit of the client. One of traffic information collection methods for ATIS research is the method of image analysis. The method is divided into two : one is the method to set two loop detectors at the area and the other is the method detecting the vehicle through an image analysis. In this paper, we propose a real-time traffic information collection system to mix two methods. The system installs multiple virtual detection lines and traces the location of the vehicle. Use of multiple virtual detection lines supplements the defect of the method of loop detectors. And we drew a representative pixels in the detecting area and used it for image analysis. This is to solve the problem of time delay which increases as the image size increases. We gathered traffic images and experimented using the system and got 92.32% of detection accuracy.