NDLI: Mining traffic data from probe-car system for travel time prediction

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Mining traffic data from probe-car system for travel time prediction

Content Provider	ACM Digital Library
Author	Nakata, Takayuki Takeuchi, Jun-ichi
Abstract	We are developing a technique to predict travel time of a vehicle for an objective road section, based on real time traffic data collected through a probe-car system. In the area of Intelligent Transport System (ITS), travel time prediction is an important subject. Probe-car system is an upcoming data collection method, in which a number of vehicles are used as moving sensors to detect actual traffic situation. It can collect data concerning much larger area, compared with traditional fixed detectors. Our prediction technique is based on statistical analysis using AR model with seasonal adjustment and MDL (Minimum Description Length) criterion. Seasonal adjustment is used to handle periodicities of 24 hours in traffic data. Alternatively, we employ state space model, which can handle time series with periodicities. It is important to select really effective data for prediction, among the data from widespread area, which are collected via probe-car system. We do this using MDL criterion. That is, we find the explanatory variables that really have influence on the future travel time. In this paper, we experimentally show effectiveness of our method using probe-car data collected in Nagoya Metropolitan Area in 2002.
Starting Page	817
Ending Page	822
Page Count	6
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1016920
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Travel time Information criterion Time series Its Probe-car
Content Type	Text
Resource Type	Article

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