Traffic density-based discovery of hot routes in road networks

Xiaolei Li, Jiawei Han, Jae Gil Lee, Hector Gonzalez

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


Finding hot routes (traffic flow patterns) in a road network is an important problem. They are beneficial to city planners, police departments, real estate developers, and many others. Knowing the hot routes allows the city to better direct traffic or analyze congestion causes. In the past, this problem has largely been addressed with domain knowledge of city. But in recent years, detailed information about vehicles in the road network have become available. With the development and adoption of RFID and other location sensors, an enormous amount of moving object trajectories are being collected and can be used towards finding hot routes. This is a challenging problem due to the complex nature of the data. If objects traveled in organized clusters, it would be straightforward to use a clustering algorithm to find the hot routes. But, in the real world, objects move in unpredictable ways. Variations in speed, time, route, and other factors cause them to travel in rather fleeting "clusters." These properties make the problem difficult for a naive approach. To this end, we propose a new density-based algorithm named FlowScan. Instead of clustering the moving objects, road segments are clustered based on the density of common traffic they share. We implemented FlowScan and tested it under various conditions. Our experiments show that the system is both efficient and effective at discovering hot routes.

Original languageEnglish (US)
Title of host publicationAdvances in Spatial and Temporal Databases - 10th International Symposium, SSTD 2007, Proceedings
Number of pages19
ISBN (Print)9783540735397
StatePublished - 2007
Event10th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2007 - Boston, MA, United States
Duration: Jul 16 2007Jul 18 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4605 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2007
Country/TerritoryUnited States
CityBoston, MA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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