Towards fine-grained urban traffic knowledge extraction using mobile sensing

Xuegang Ban, Marco Gruteser

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

13 Scopus citations

Abstract

We introduce our vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, we show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. We explore privacy algorithms based on the virtual trip lines (VTL) concept to regulate where and when the mobile data should be collected. The traffic modeling techniques feature an integration of traffic principles and learning/optimization techniques. The proposed methods are illustrated using two case studies for extracting traffic knowledge for urban signalized intersection.

Original languageEnglish (US)
Title of host publicationInternational Workshop on Urban Computing, UrbComp 2012 - Held in Conjunction with KDD 2012
Pages111-117
Number of pages7
DOIs
StatePublished - 2012
EventInternational Workshop on Urban Computing, UrbComp 2012 - Held in Conjunctionwith KDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 12 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherInternational Workshop on Urban Computing, UrbComp 2012 - Held in Conjunctionwith KDD 2012
CountryChina
CityBeijing
Period8/12/128/12/12

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • location traces
  • mobile sensing
  • traffic theory
  • urban traffic knowledge

Fingerprint Dive into the research topics of 'Towards fine-grained urban traffic knowledge extraction using mobile sensing'. Together they form a unique fingerprint.

Cite this