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

Takayuki Nakata, Jun Ichi Takeuchi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

47 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
出版社Association for Computing Machinery (ACM)
ページ817-822
ページ数6
ISBN(印刷版)1581138881, 9781581138887
DOI
出版ステータス出版済み - 2004
外部発表はい
イベントKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Seattle, WA, 米国
継続期間: 8 22 20048 25 2004

出版物シリーズ

名前KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

その他

その他KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country米国
CitySeattle, WA
Period8/22/048/25/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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