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From data to knowledge: city-wide traffic flows analysis and prediction using bing maps

Published: 11 August 2013 Publication History

Abstract

Traffic jam is a common contemporary society issue in urban areas. City-wide traffic modeling, visualization, analysis, and prediction are still challenges in this context. Based on Bing Maps information, this work aims to acquire, aggregate, analyze, visualize, and predict traffic jam. Chicago area was evaluated as case study. The flow intensity (free or congested) was analyzed to allow the identification of phase transitions (shocks in the system). Also, a prediction model was developed based on logistic regression to correct discovery future flow intensities for a target street.

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cover image ACM Conferences
UrbComp '13: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
August 2013
135 pages
ISBN:9781450323314
DOI:10.1145/2505821
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 August 2013

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Author Tags

  1. analysis
  2. human mobility
  3. traffic prediction
  4. urban computing
  5. visualization

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