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 of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

/pdf/map-matching-for-low-sampling-rate-gps-trajectories-4hstm6zk4l.pdf

Map-matching for low-sampling-rate GPS trajectories

Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Learning transportation modes from raw gps data

https://eis.hu.edu.jo/deanshipfiles/pub110915421.pdf

Knowledge-Based Risk Management framework for Information Technology project

Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.

Recommending points of interests in a region

A system for logging life experiences using geographic cues. The system variously provides a comprehensive life-logging tool for recording each life event; a vacation album for revisiting and reliving vacation routes and associated photos; an information service for finding popular routes and locations; a statistical tool for analyzing metrics of a person's life; and a personal website service for sharing personal information. In one implementation, the system receives a user's GPS log files and multimedia content at a website. The system segments the GPS log files into geographic routes corresponding to user trips, and tags the multimedia content with geographic cues from the GPS log files. Then, the system indexes the geographic routes so that users can retrieve the geographic routes by browsing or by search techniques. The system displays animations of selected routes on a map, and displays the multimedia content at corresponding locations along the map route, as the route is replayed. The system also provides browsing and spatial and temporal techniques to search a person's travels and can provide graphical displays of the person's activity statistics.

System for logging life experiences using geographic cues

Knowledge management is the new phrase of modern enterprise management theory and it challenges the traditional information management means which can not fulfill the needs of knowledge management any more. This dissertation tries to analyze the position and function of decision support system based on knowledge management, puts forward a logical model of decision support system based on knowledge management, and gives an analysis and evaluation of its key technologies.

Decision support systems based on knowledge management

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Decision support systems based on knowledge management