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

Takayuki Nakata, Jun Ichi Takeuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

47 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery (ACM)
Pages817-822
Number of pages6
ISBN (Print)1581138881, 9781581138887
DOIs
Publication statusPublished - 2004
Externally publishedYes
EventKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Seattle, WA, United States
Duration: Aug 22 2004Aug 25 2004

Publication series

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

Other

OtherKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySeattle, WA
Period8/22/048/25/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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