Time series: theory and methods
Time series: theory and methods
A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
A knowledge based real-time travel time prediction system for urban network
Expert Systems with Applications: An International Journal
Automatic congestion detection and visualization using networked GPS unit data
Proceedings of the 47th Annual Southeast Regional Conference
Information Sciences: an International Journal
A system for destination and future route prediction based on trajectory mining
Pervasive and Mobile Computing
A personal route prediction system based on trajectory data mining
Information Sciences: an International Journal
Improved travel time prediction algorithms for intelligent transportation systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
A map ontology driven approach to natural language traffic information processing and services
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
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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.