Yikai Chen

An Approach to Urban Traffic State Estimation by Fusing Multisource Information

This paper presents an information-fusion-based approach to the estimation of urban traffic states. The approach can fuse online data from underground loop detectors and global positioning system (GPS)-equipped probe vehicles to more accurately and completely obtain traffic state estimation than using either of them alone. In this approach, three parts of the algorithms are developed for fusion computing and the data processing of loop detectors and GPS probe vehicles. First, a fusion algorithm, which integrates the federated Kalman filter and evidence theory (ET), is proposed to prepare a robust, credible, and extensible fusion platform for the fusion of multisensor data. After that, a novel algorithm based on the traffic wave theory is employed to estimate the link mean speed using single-loop detectors buried at the end of links. With the GPS data, a series of technologies are combined with the geographic information systems for transportation (GIS-T) map to compute another link mean speed. These two speeds are taken as the inputs of the proposed fusion platform. Finally, tests on the accuracy, conflict resistance, robustness, and operation speed by real-world traffic data illustrate that the proposed approach can well be used in urban traffic applications on a large scale.

A New Method For Urban Traffic State Estimation Based On Vehicle Tracking Algorithm

In this paper, a new method for urban traffic state estimation is proposed. Real-time GPS locational data are collected to implement the vehicle tracking algorithm throughout the urban GIS network. Average velocities along these tracks are calculated and distributed proportionally. By integrating the velocity contributions on each road link, traffic states are finally estimated along rolling time periods. Compared with conventional methods, the proposed method keeps the continuity of vehicle travels and runs quickly without additional data sources. Experiments on real taxi scheduling signals indicate that the new method is both reasonable and practical.

An Improved Evidential Fusion Approach for Real-time Urban Link Speed Estimation

On the basis of previous researches, an improved evidential fusion approach is presented to integrate heterogeneous multi-sensor data in Urban Advanced Traveler Information Systems. The method inherits the advantages of the previous model in terms of the real-time processing feature; meanwhile, its performance is improved by adding a mechanism to evaluate the dynamic reliability of sensors. In this paper, the whole evidence reliability algorithms, which contain both the static one and the dynamic one, are provided primarily. After that, the frame and computational procedures of the new approach are given. Finally, a simulation test and a real-world data experiment are discussed to explain the advantage of the proposed method in comparison with the previous model. The real-world data of loop detectors and GPS probe vehicles were collected from an urban arterial road section in Shanghai.

A new method for GPS-based urban vehicle tracking using pareto frontier and fuzzy comprehensive judgment

Vehicle Tracking is an effective approach to urban traffic flow estimation. Based on the data of GIS and GPS, a Vehicle Tracking process continually judges, from a road network, the most reliable path between every two successively detected locations of a certain vehicle, and then links the paths to restore the real track. Conventional works focused mostly on the driving distance and selected the shortest path as the solution. However, as is in many situations, the shortest path is not necessarily the best choice. Instead, a path with the least time expense may be more reasonable. Therefore, a new hybrid real-time Vehicle Tracking method is proposed in this paper considering comprehensively two main time-costing factors, the total driving distance and the number of traversed crossroads, using the concept of Pareto Frontier and Fuzzy Theory. Analytical experiment in Shanghai yields promising results.

Nonlinear Analysis of a Dynamical Model with Next-nearest-neighbor Interaction for Traffic

We analyze a dynamical model of car-following model analytically and numerically, which is a continuation of our previous research on traffic flow in stable region. Linear stability analysis shows that our model is stabilized for larger delay time by taking into account the relative velocity of the immediately preceding car. Moreover, the Burgers equation is derived for the density wave in stable region of traffic flow from nonlinear analysis, the result of direct simulation show that the triangular shock wave appears as the density wave at later stage in stable region.