Efficient traffic speed forecasting based on massive heterogenous historical data

Xing Yu Chen, Hsing Kuo Pao, Yuh-Jye Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

Drivers dream of foreseeing traffic condition to enjoy efficient driving experience at all times. Given the historical patterns for different locations and different time, people should be able to guess the possible traffic speed in a near future moment. What is difficult and interesting for this task is that we need to filter the useful data that could help us for the next moment traffic speed prediction from a massive amount of historical data. On the other hand, the traffic condition could be highly dynamic and we can only give a reliable traffic prediction by using the most updated model for prediction. This implies that frequent retraining is necessary. To conquer the task, we propose a lazy learning approach for traffic speed prediction given massive historical data. The approach integrates the kNN and Gaussian process regression for efficient and robust traffic speed prediction. kNN can help us to select the most informative data for Gaussian process Regression using a big data framework. Thanks for the most recent progress of big data research, the processing of massive data for prediction in close to real time has become possible now compared to any time in the past. We aim at using a Hadoop framework for the prediction given heterogeneous data including traffic data such as speed, flow, occupancy, and weather data.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages10-17
Number of pages8
ISBN (Electronic)9781479956654
DOIs
StatePublished - 1 Jan 2014
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: 27 Oct 201430 Oct 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Conference

Conference2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period27/10/1430/10/14

Keywords

  • Big data
  • Gaussian Process Regression
  • Hadoop
  • Intelligent Transportation Systems (ITS)
  • K Nearest Neighbors (KNN)
  • MapReduce

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