In the field of ITS, various techniques are known for estimating/predicting travel time required for travel of a vehicle or traffic conditions such as occurrence of gridlock for the purpose of providing route guidance. In particular, probe-car systems, in which a vehicle itself is utilized as a sensor for acquiring road traffic information using vehicle-mounted equipment, have started to be used. Literature relating to these techniques will be set forth below.
The paper “Traffic Information Prediction Method on Feature Space Projection” by Kumagai et al. set forth in the IPSJ SIG Technical Report “Sophisticated Traffic System” No. 014-009 proposes a method of classifying one day of a travel-time fluctuation pattern into several categories by principal-component analysis, and correlates a category, to which a prediction-target day is to belong, based upon a label (day of the week or weather, etc) that represents the type of day. This method is a technique applied to prediction over a long-term range, namely half a day or one full day. Further, it is believed that a road segment in which prediction is possible by this method is limited to highways or the like where measurements can be made at fixed points.
In a “Travel-Time Prediction Apparatus” described in the specification of Japanese Patent Kokai Publication No. JP-P2000-235692A, there is disclosed a method of obtaining the ranking of current segment travel time in a travel-time cumulative distribution for every time period with regard to a travel-time prediction-target segment, obtaining a predicted ranking from this ranking and extracting travel time, which corresponds to the predicted ranking, from the travel-time cumulative distribution. Since a predicted value based upon this method depends greatly upon the ranking at the present time, it is believed that this technique is one suited to a prediction from the immediate future to about one hour ahead. Although application is possible if the segment of road is one on which measurements can be made at fixed points, it can be said that the method is suited to high-speed roads in terms of the characteristics of the above-described technique.
In “Travel-Time Prediction Method, Apparatus and Program” described in Japanese Patent Kokai Publication No. JP-P2003-303390A, use is made of a method of retrieving a travel-time transition pattern that resembles a current travel-time transition pattern from past current-time performance data that has been accumulated, and estimating travel time using the resembling travel-time transition pattern. It is believed that a segment in which prediction is possible by this method also is limited to highways or the like where measurements can be made at fixed points.
In a “Traffic Information Prediction-Function Learning Apparatus, Traffic Information Prediction Apparatus, Traffic Information Fluctuation Rule Acquisition Apparatus and Method Thereof” described in Japanese Patent Kokai Publication No. JP-P2006-11572A filed by the present applicant, there is proposed a method of analyzing, by an autoregression model, the difference between time-series data acquired from a probe-car system and a travel-time transition pattern created based upon past travel-time performance, and predicting travel time. Since this method is premised on data acquisition by a probe-car system and not measurement at fixed points, it is in principle applicable to all road segments but finds application in the prediction of travel time into the immediate future.
In a “Required Driving Time Prediction Apparatus” described in the specification of Japanese Patent Kokai Publication No. JP-P2004-118700A, travel time is predicted by combining a short-term prediction of required driving time utilizing predicted traffic data for that day and an intermediate-term prediction of required driving time based upon retrieval of a similar pattern. The apparatus of this publication is premised on use of data acquired from fixed sensors such as a vehicle sensor, AVI (Automatic Vehicle Identification) system and sensors at toll booths. Prediction along segments where these sensors have not been deployed is not considered.
In a “Matching Correction Method of Estimated Link Travel-Time Data” disclosed in Japanese Patent Kokai Publication No. JP-P2005-208034A, there is described a method in which travel-time data (past statistical data) of a segment relating to a period of from several hours to one day is modified based upon current-condition data to thereby perform prediction accurately over a period of from several tens of minutes to several hours. A segment over which a prediction is possible by this method is only a segment obtained from past statistical data and current-condition data in a manner similar to the techniques described above. This disclosure does not touch upon a prediction over all road segments.
[Patent Document 1]
    Japanese Patent Kokai Publication No. JP-P2000-235692A[Patent Document 2]    Japanese Patent Kokai Publication No. JP-P2003-303390A[Patent Document 3]    Japanese Patent Kokai Publication No. JP-P2006-11572A[Patent Document 4]    Japanese Patent Kokai Publication No. JP-P2004-118700A[Patent Document 5]    Japanese Patent Kokai Publication No. JP-P2005-208034A[Non-Patent Document 1]    IPSJ SIG Technical Report “Sophisticated Traffic System” No. 014-009, “Traffic Information Prediction Method on Feature Space Projection,” pp. 51-57, Masatoshi Kumagai et al.[Non-Patent Document 2]    IEEE Transactions on Information Theory, vol. 44, No. 4, pp. 1424-1439 “A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning,” K. Yamanishi, 1998[Non-Patent Document 3]    Eighth Information-Based Induction Sciences “Hierarchical State Space Model for Long-Term Prediction,” Takayuki Nakata, Jun-ichi Takeuchi (2005)
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