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

A information processing apparatus including an inquiry section configured to make an inquiry to a server about desired search information on the basis of setting information preliminarily input by a user operation, the server being connected to a network, a receiving section configured to receive the search information about which the inquiry section makes an inquiry to the server, and a notifying section configured to provide notification of the search information, which is received by the receiving section, at a predetermined timing

Information processing apparatus, information processing method, and program

A method is provided for modifying an image. The method comprises displaying an image, the image comprising a portion of an object; and determining if an edge of the object is in a location within the portion. The method further comprises detecting movement, in a member direction, of an operating member with respect to the edge. The method still further comprises moving, if the edge is not in the location, the object in an object direction corresponding to the detected movement; and modifying, if the edge is in the location, the image in response to the detected movement, the modified image comprising the edge in the location.

Information processing apparatus, information processing method and program

Techniques are described for displaying or otherwise providing information to users regarding various types of road traffic condition information in various ways. The information may be provided, for example, as part of a user interface (or “UI”), which may in some situations further include one or more types of user-selectable controls to allow a user to manipulate in various ways what road traffic condition information is displayed and/or how the information is displayed. A variety of types of road traffic condition information may be presented to users in various manners, including by presenting information on graphically displayed maps for geographic areas to indicate various information about road conditions in the geographic area. In addition, provided controls may allow users to select particular times, select particular routes, indicate to perform animation of various types of changing traffic conditions over a sequence of multiple successive times, etc.

Displaying road traffic condition information and user controls

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Determining road traffic conditions using data from multiple data sources

Techniques are described for generating and using information regarding road traffic in various ways, including by obtaining and analyzing road traffic information regarding actual behavior of drivers of vehicles on a network of roads. Obtained actual driver behavior information may in some situations be analyzed to identify decision point locations at which drivers face choices corresponding to possible alternative routes through the network of roads (e.g., intersections, highway exits and/or entrances, etc.), as well as to track the actual use by drivers of particular paths between particular decision points in order to determine preferred compound links between those decision point locations. The identified and determined information from the analysis may then be used in various manners, including in some situations to assist in determining particular recommended or preferred routes of vehicles through the network of roads based at least in part on actual driver behavior information.

Learning road navigation paths based on aggregate driver behavior

Techniques are described for determining and using information regarding expected road traffic flow conditions information for vehicles traveling on roads. The expected road traffic flow conditions for a particular portion of a road may be generated by combining historical representative information about road traffic flow conditions for that road portion with current information about actual traffic flow on or near that road portion. The combination may, for example, provide benefits for estimating expected traffic flow conditions information for roads with structural flow obstructions that cause reduced traffic flow at certain road locations and times - for example, the expected traffic flow conditions information may be based at least in part on fitting or otherwise adapting partial actual traffic flow information about a vehicle's actual travel path to a historical travel profile for a road that includes representative traffic flow information for various combinations of road locations and time periods.

Predicting expected road traffic conditions based on historical and current data

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Assessing road traffic conditions using data from mobile data sources.

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