Travel Time Enabled Bus Route Navigation System Experiment in Beijing

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.

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.

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.

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.

Solving in real-time the dynamic and stochastic shortest path problem for electric vehicles by a prognostic decision making strategy

. 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.

Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status. With significantly improved positioning accuracy and coverage, trajectory data collected from GPS-equipped probe vehicles have great potential for traffic state recognition. This paper presents a machine learning enabled travel time estimation method based on the GPS-equipped probe vehicles data. This research considers the spatial-temporal relevancy while solving the travel time allocation problem: the travel time of target segment might be associated with its previous travel times and/or the traffic states of nearby relevant segments. After data normalization and network clustering, an artificial neural network (ANN) algorithm considering such spatial-temporal relevancy was conducted to infer the travel time distribution among the traveled segments within one path. Furthermore, a weighted summation of the travel time estimation result from various trajectories was calculated to better represent the segment travel time in one time step. The proposed method was evaluated by evaluating the estimation results with automatic vehicle identification obtained ground truth. The experimental results illustrated that by utilizing the ANN to consider the spatial-temporal relevancy, the proposed method is effective and efficient in estimating the travel time.

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.

In this paper, online stochastic routing policies are developed to identify the optimal next action (path choice) at the current decision node (intersection) for travelers on the basis of their preference for future paths with the shortest travel time, the lowest travel time variability, or a combination, given the network conditions. A modified label-correcting algorithm is provided to solve for the shortest path that results from the proposed routing policies. Its running time is bounded by O(mn2), where m and n are the number of arcs and nodes, respectively, in the network. Since real-time traffic information is usually available with a certain level of accuracy, the proposed online routing policy integrates an existing information fusion model, which provides real-time short-term arc travel time distributions by considering information accuracy. Numerical experiments are used to demonstrate the performance of the proposed routing policies and algorithms, as well as the impacts of real-time information accuracy on the online stochastic routing.

A dynamic vehicle routing problem with time-dependent travel times

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.

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.

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.

Trials are underway in South Korea to update maps and traffic information in real time, using data collected from connected vehicles via a 5G network

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.