5G Tests for C-V2X Live Map Tech

Arrival time dependent shortest path finding is an important function in the field of traffic information systems or telematics. However large number of mobile objects on the road network results in a scalability problem for frequently updating and handling their real-time location. In this paper, we propose a query processing method in MANET(Mobile Ad-hoc Network) environment to find an arrival time dependent shortest path with a consideration of both traffic and location in real time. Since our method does not need a centralized server, time dependent shortest path query is processed by in-network way. In order to reduce the number of messages to forward and nodes to relay, we introduce an on-road routing, where messages are forwarded to neighboring nodes on the same or adjacent road segments. This routing method allows to collect traffic information in real time and to reduce the number of routing messages. Experiments show that the number of forwarded messages is reduced in an order of magnitude with our on-road routing method compared to LAR-like method. At best, our method reduces about 57 times less messages.

Common bus route navigation system provides distance and transferring information based on the user-determined start point and end point on the map. This paper presents the research and experiment of an advanced bus route navigation system in Beijing, which can additionally provide travel time information from start to end. The travel time is predicted based on statistical and real-time traffic information. In Beijing, about 10000 taxies send GPS data to a traffic information center to generate real-time traffic data, and the center accumulates the historical traffic data for generating statistical database. Field test of our system was carried out in January 2007, and the experiment result shows the real-time and statistical traffic information can be used not only for car navigation system, but also for public transit service. In our experiment, the average error rate of predicted travel time is about 10%, which is comparable to car navigation system.

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 real-time traffic information on the relaxation process is studied. The provision of traffic information on a real-time basis to individual vehicles is expected as an effective way to distribute the traffic flow to a network of roads. However, it has been also pointed out that real-time information prevents the distribution of the traffic flow on a network of roads because of the concentration of the traffic flow to the recommended route. We can see that the real-time traffic information based on averaged velocity or travel time give rise to fluctuation with chaotic structure on the relaxation process of traffic flow formed by cellular automata. It is also proposed that the real-time traffic information composed by differentiation of averaged velocity of vehicle is effective to reduce the fluctuation of traffic flow.

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.

Feedback contents of previous information feedback strategies in advanced traveler information systems are almost real-time traffic information. Compared with real-time information, prediction traffic information obtained by a reliable and effective prediction algorithm has many undisputable advantages. In prediction information environment, a traveler is prone to making a more rational route-choice. For these considerations, a mean velocity prediction information feedback strategy (MVPFS) is presented. The approach adopts the autoregressive-integrated moving average model (ARIMA) to forecast short-term traffic flow. Furthermore, prediction results of mean velocity are taken as feedback contents and displayed on a variable message sign to guide travelers' route-choice. Meanwhile, discrete choice model (Logit model) is selected to imitate more appropriately travelers' route-choice behavior. In order to investigate the performance of MVPFS, a cellular automaton model with ARIMA is adopted to simulate a two-route scenario. The simulation shows that such innovative prediction feedback strategy is feasible and efficient. Even more importantly, this study demonstrates the excellence of prediction feedback ideology.

. Aiming at the characteristics of Electric Vehicles (EVs) combined with transportation and mobile load, and their charging and driving behaviors will interact with the traffic system and power grid system. On the basis of this, a route planning and charging navigation strategy for EVs based on real-time traffic information and grid information was presented in this paper. First, a dynamic traffic network model was established according to the time-varying feature of traffic information. And also, the road resistance model with road segment impedance and intersection node impedance was proposed depending on the attribute of urban roads. Furthermore, based on the establishment of traffic network model, distribution network model and single EV model, a multi-objective optimization function integrated with the road travel time, the charging station load and the quantity of vehicles entering into the station was determined, which was solved to recommend the optimal driving and charging paths by dynamic Dijkstra dynamic search algorithm. At last, the actual road network of a certain zone in Nanjing was selected as an example to analyze the spatial-temporal distribution of EV charging load, and evaluated the impact of its charging and driving on traffic network and distribution network. Simulation results demonstrate its effectiveness and feasibility.

Location based services (LBS) are capturing global spotlights increasingly, in which users tend to pick up navigation service as their favorites. This paper examines the dynamic routing problems based on real-time traffic information in LBS. At first, the overall structure of dynamic navigation system is illustrated, which is composed of four ingredients: traffic data collection, digital road map, static routing and dynamic re-routing. Traffic data collection is the task of traffic information center (TIC), where various sources of data are fused into link travel times that are used as criteria to choose candidates in path determining. Digital road map includes digital road network and historical traffic data, where the topology relationship of road network is elementarily explored and the method to represent statistical traffic data is referred to. Routing with historical traffic data and dynamic re-routing with live traffic data are two main functions of dynamic navigation service. The flow of dynamic re-routing algorithm is exhibited. Lastly, a first-in-first-out (FIFO) time-depend shortest path algorithm is discussed for dynamic routing, which adopts the adaptive A^* algorithm based on binary heap priority queue and RB-tree.

Dangerous driving behaviors can lead to traffic accidents. However, there is a lack of systematic real-time driving monitoring and alerting mechanism. In this paper, nine major dangerous driving behaviors are identified and defined based on past researches and experiences. An on-board safety driving assistance system, which utilizes in-vehicle dynamics and real-time traffic information, is proposed to instantly detect dangerous driving behaviors and alert the driver during driving periods. The in-vehicle dynamics are collected from On-Board Diagnostics (OBD-II), an accelerometer and a gyroscope. The real-time traffic information is collected from a camera with image processing. The proposed system is implemented on a CPU board with a user interface (UI), which shows alerting icons when dangerous behaviors occur.

In offer of the traffic information to the drivers, it is the big factor that decides quality of the traffic information whether the traffic information and conformity with the real condition experienced by a drivers. The OD time is useful information because the drivers can know the time of arrival to the destination. However only using the real-time traffic information, it is different from the time of arrival to display at origin and the time of arrival at destination because it can’t consider the change of traffic condition when they arrive at the destination. So it is generally to raise the benefit of the drivers with the traffic information prediction that can decrease the difference. But now (in particularly the prediction) in evaluating the quality of the traffic information, there are methods to evaluate the traffic jam but not any effective methods to evaluate the traffic information with link travel time. This article suggests the method of evaluating the quality of the prediction with the RMSE in addition to the standard deviation and considers the quality of the traffic information prediction that can offer useful information to the drivers.

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.

When well testing is conducted, the reservoir response during the initial surge to stable drawdown and pressure buildup will define productivity expectations for a new well. The quality of the reservoir fluid samples further defines the value of the reservoir assets. To ensure that operations will be conducted safely, rig-time efficiency will be maximized, and the overall well test objectives will be met, several critical well testing decisions must be made. This paper describes the value of real-time access to reservoir information and tools to control well testing operations. Also described is a system that has been used to provide immediate access to the reservoir through the use of wireless downhole telemetry, data acquisition and control, and instantaneous visibility of data from any location around the world. While the idea of providing access to some reservoir information in near real time is not a new concept, the system described is not dependent on formation geology and is the only one with the capability to provide access to all reservoir information in real time, perform analysis using this information, and control well testing operations remotely with the click of a mouse button. Case histories will be presented to demonstrate the capabilities of the system described.

Nearest neighbor (NN) searches represent an important class of queries in geographic information systems (GIS). Most nearest neighbor algorithms rely on static distance information to compute NN queries (e.g., Euclidean distance or spatial network distance). However, the final goal of a user when performing an NN search is often to travel to one of the points of the search result. In this case, finding the nearest neighbors in terms of travel time is more important than the actual distance. In the existing NN algorithms dynamic real-time events (e.g., traffic congestions, detours, etc.) are usually not considered and hence the pre-computed nearest neighbor objects may not accurately reflect the shortest travel time. In this paper we propose a novel travel time network that integrates both spatial networks and real-time traffic event information. Based on this foundation of the travel time network, we develop a local-based greedy nearest neighbor algorithm and a global-based adaptive nearest neighbor algorithm that both utilize real-time traffic information to provide adaptive nearest neighbor search results. We have performed a theoretical analysis and simulations to verify our methods. The results indicate that our algorithms remarkably reduce the travel time compared with previous nearest neighbor solutions.

Urban Global Driving Cycle Construction Method and Global Optimal Energy Management in Plug-in Hybrid Electric Vehicle

최근 환경문제로 인해 보행, 자전거, 대중교통과 같은 친 환경 교통수단에 대한 관심이 높아지고 있다. 하지만 보행과 자전거는 경우에 따라 장거리 이동이 불가능하기 때문에 이용범위가 제한적이라는 단점이 있다. 반면에 대중교통은 교통체증이라는 사회적 문제점까지 해결할 수 있는 교통수단이다. 이러한 대중교통은 시간에 따라 운행여부가 달라진다는 특징을 갖고 있다. 하지만 현재 웹상에서 서비스중인 대중교통정보 안내 서비스는 이러한 특징을 반영하지 못하기 때문에 경우에 따라 사용자에게 잘못된 정보를 제공할 수 있다. 이러한 한계점을 해결하고 대중교통의 운행정보를 고려한 정보제공을 위해 본 연구에서는 STP(Space Time Point)데이터 모델을 제안하였다. STP데이터 모델은 객체의 최하위 개념을 포인트 단위로 인식하고 이들을 계층적으로 구성하여 객체를 표현할 수 있으며, 기존의 시공간 데이터 모델과는 달리 시간에 따라 객체정보가 변하는 다양한 동적 공간객체의 구현이 가능하다. 대전지역을 대상으로 대중교통 운행정보로 버스 및 지하철에 대한 STP모델을 설계 및 구축하고 이를 이용하여 동적 경로 안내 기능을 구현하였다. 구현한 경로안내 기능을 통해 대전국립현충원에서 한남대학교로 가는 경로를 시간대별로 탐색해 본 결과 대중교통의 운행에 제한이 발생하는 심야시간에는 운행이 제한되는 노선을 우회하는 경로를 안내함으로써 시간에 따라 서로 다른 경로를 나타내었다. 이는 기존 데이터 모델에서는 제공할 수 없는 결과로 실시간 운행정보를 고려한 경로안내가 가능함을 확인하였다. 이러한 기능은 실시간 교통정보나 통행이력정보등과 같은 다양한 데이터 모델의 적용을 통해 추가적인 기능 확장이 가능할 것으로 기대한다. Recent environmental concerns have made interest in environment-friendly transportation means such as walking, biking, and public transportation. However, since it is difficult to move long distance by walking or biking, their scope of application is rather limited. On the other hand, public transportation can solve traffic congestion, a recent social issue, though its usability may depend on its time schedule. Currently available information services on public transportation in the Web do not reflect well such traits of the public transportation; thus, in some cases, they may provide wrong information to end users. To solve such problems and provide information based on timetable of public transportations, this paper proposes a STP(Space Time Point) data model. Unlike existing space-time data models, this model recognizes the bottommost element of an object as a point and structures these points in hierarchical way to define an object. In particular, It can make it possible to implement a variety of dynamic spatial objects changing object information according to time. An objective of this study is to design a STP model for bus and subway based on timetables of public transportation in Daejeon area and builds a system to provide path navigation. With the designed navigation function, a path from the Daejeon National Cemetery to Hannam University was searched by time slot. The result showed that the system provided different paths by time, as the system guided different paths when bus operation was limited in midnight. As existing data model could not provide such results, it is confirmed that the system can provide path navigation based on real-time traffic information. It is expected that based on such functionality, it is possible to provide additional functionalities by applying diverse data models such as real-time transport information or traffic history information.