Collection Of Real-time Traffic Information Research Articles

Data Acquisition and Mining Algorithm of Car Networking under Big Data Background

In order to solve the problem of traffic jams, intelligent traffic technology and car networking technology were applied. In the context of big data, data acquisition and mining algorithms for vehicular network were studied. First, the overall architecture of the system was introduced. Then, the data acquisition technology based on the car network and the data mining technology based on the cloud plat-form were introduced. Finally, simulation experiments of real-time traffic information collection and recognition algorithms were performed. The results showed that the proposed mining algorithm had better data repair effect and better clustering effect, and the probability of misjudgment was smaller. Therefore, the algorithm can obtain accurate road traffic conditions.

The analysis of speed-reporting rates from a cellular network based on a fingerprint-positioning algorithm

The collection of real-time traffic information is an important part of intelligent transportation systems. In particular, cellular floating vehicle data (CFVD) technology has become increasingly widespread, and more and more popular for measuring and forecasting real-time traffic information, based on anonymous sampling of the positions of mobile phones. This study proposes an analytical model to analyse the speed-reporting rates – based on communication behaviour, traffic conditions, and the two consecutive fingerprintpositioning locations from the call arrival and call completion signals of the same call – for a feasibility evaluation of CFVD.

A Real-Time Traffic Information Model Using GPS-Based Probe Car Data

In recent year, the rise of economic growth and technology advance leads to improve the quality of service of traditional transport system. Intelligent Transportation System (ITS) has become more and more popular. At present, the collection of real-time traffic information is executed in two ways: (1) Stationary Vehicle Detectors (VD) and (2) Global Position System (GPS)-based probe cars reporting. However, VD devices need a large sum of money to build and maintain. Therefore, we propose the linear regression model to infer the equation between vehicle speed and traffic flow. The traffic flow can be estimated from the speed which is obtained from GPS-based probe cars. In experiments, the Speed Error Ratio (SER) and Flow Error Ratio (FER) of linear regression model are 4.60% and 24.63% respectively. The estimated speed and traffic flow by using linear regression model is better than by using linear model, power law model, exponential model, and normal distribution model. Therefore, the linear regression model can be used to estimate traffic information for ITS.

Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system

Real-time traffic information collection and data fusion is one of the most important tasks in the advanced traffic management system (ATMS), and sharing traffic information to users is an essential part of the advance traveler information system (ATIS) among the intelligent transportation systems (ITS). Traditionally, sensor-based schemes or probing-vehicle based schemes have been used for collecting traffic information, but the coverage, cost, and real-time issues have remained unsolved. In this paper, a wiki-like collaborative real-time traffic information collection, fusion and sharing framework is proposed, which includes user-centric traffic event reacting mechanism, and automatic agent-centric traffic information aggregating scheme. Smart traffic agents (STA) developed for various front-end devices have the location-aware two-way real-time traffic exchange capability, and built-in event-reporting mechanism to allow users to report the real-time traffic events around their locations. In addition to collecting traffic information, the framework also integrates heterogeneous external real-time traffic information data sources and internal historical traffic information database to predict real-time traffic status by knowledge base system technique.

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.