and consequently produces inaccurate trajectory data. To become useful, the data has to be related to the underlying road network by means of map matching algorithms. We present three such algorithms that consider especially the trajectory nature of the data rather than simply the current position

Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount

system plays an important role in improving urban traffic jam, increasing the efficiency of city traffic network and people’s lives. In this paper, the key research of map-matching method in vehicle guidance system is committed. Firstly the real time dynamic position accuracy of single point of GPS

data with digital road network data so that vehicles can have accurate positioning solutions across different environments, especially in urban canyons. Map matching characteristics are described through discussions on geometric limitations, and a variety of error sources and their propagation

A novel method of map matching using the Global Positioning System (GPS) has been developed which uses digital mapping and height data to augment point position computation (Taylor, 1999). Map matching algorithms attempt to locate a given vehicle position on a map. In a moving vehicle

the measurement noise statistics to optimally translate the raw position measurements onto the road network. The theoratical performance of this es-timator is derived and compared with that achieved using GPS measurements. The effect that Selective Availabil-ity (SA) has on the map-aided estimator is analy ed

We present a robust method for solving the map matching problem exploiting massive GPS trace data. Map matching is the problem of determining the path of a user on a map from a sequence of GPS positions of that user — what we call a trajectory. Commonly obtained from GPS devices, such trajectory

/loss decision. Recent results by Tesauro in the domain of backgammon indicate that a neural network, trained by TD methods to evaluate positions generated by self-play, can reach an advanced level of backgammon skill. For my summer thesis project, I first implemented the TD/neural network learning algorithms

methods. Accumulation of positioning errors, however, cannot be avoided. One of the solutions to reduce or modify these errors is “Map matching ” technique, which modifies estimated user’s position assuming that users are always along road networks. In this paper, a method of Map Matching for Personal

Wireless Positioning Systems (WPS) are popular alternative localization methods, especially in dense urban areas where GPS has known limitations. Map-matching (MM) has been used as an approach to improve the accuracy of the estimated locations of WiFi Access Points (APs), and thus the accuracy of a