Hitachi, Ltd.

Kazuya Kimita

Takayoshi Yokota

Masatoshi Kumagai

Takumi Fushiki

Koichiro Tanikoshi

A traffic information system including a travel history database retaining the data on the travel history of vehicle measured at a constant temporal or running interval; a link cost weighting pattern database for storing patterns for weighting the link costs of road links, used in the determination of the travel paths of vehicles; a vehicle ID/link cost weighting pattern set database retaining the data representing the matching of the vehicle IDs with their associated link cost weighting patterns used to determine the travel paths of the vehicles; a link cost weighting pattern selection unit selecting the patterns for weighting the link costs used to determine the travel paths of the vehicles on the basis of the vehicle IDs; and a vehicle ID/link cost weighting pattern matching unit recording the data representing the matching of the vehicle IDs with their associated link cost weighting patterns.

Traffic information system based on travel history

A traffic information system includes: a past information database for storing past information, which is collected for road links in a predetermined area, of a past mobile object on a road; a current information database for storing running information, which is collected for the road links in the predetermined area, of a current mobile object; link correlation analyzing means in which correlations of traffic information among each road link in the predetermined area are calculated from the past information stored in the past information database, and output as link correlation information among the road links; combination calculating means for calculating weighting information for obtaining the current information as a sum of the link correlation information; and traffic information estimating means for calculating estimated traffic information for a link where the current information is not collected based on the link correlation information and the weighting information.

Traffic information estimating system

There is provided a prediction calculation system capable of allowing a numerical traffic information to be predicted in such a manner that day factors and the like are reflected thereon. Even if a day whose combination of the day factors is the same as that of a prediction-target day, i.e., a day with the same instance, does not exist within a time-period during which past traffic information has been accumulated, the prediction calculation unit merges the past traffic information appropriately, thereby predicting traffic information corresponding to the objective combination of the day factors. Concretely, the prediction calculation unit combines some of characteristic quantities of the past traffic information on a day-unit basis, thereby approximately reconstructing and predicting the instance.

Traffic information prediction device with day-factors and day factor classifications

In a congestion prediction using measurement data which is acquired by an on-road sensor or a probe car, and which includes none of explicit information about bottleneck points, with respect to time-sequence data on congestion ranges accumulated in the past, data on congestion front-end positions are summarized into plural clusters by the clustering. Representative value in each cluster is assumed as position of each bottleneck. A regression analysis, in which day factors are defined as independent variables, is performed with congestion length from each bottleneck point selected as the target. Here, the day factors refer to factors such as day of the week, national holiday/etc. It then becomes possible to precisely predict a future congestion length.

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Hitachi, Japan

Kazuya Kimita