Fast and exact network trajectory similarity computation: A case-study on bicycle corridor planning

Michael R. Evans, Dev Oliver, Shashi Shekhar, Francis Harvey

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

14 Scopus citations

Abstract

Given a set of trajectories on a road network, the goal of the All-Pair Network Trajectory Similarity (APNTS) problem is to calculate the similarity between all trajectories using the Network Hausdorff Distance. This problem is important for a variety of societal applications, such as facilitating greener travel via bicycle corridor identification. The APNTS problem is challenging due to the high cost of computing the exact Network Hausdorff Distance between trajectories in spatial big datasets. Previous work on the APNTS problem takes over 16 hours of computation time on a real-world dataset of bicycle GPS trajectories in Minneapolis, MN. In contrast, this paper focuses on a scalable method for the APNTS problem using the idea of row-wise computation, resulting in a computation time of less than 6 minutes on the same datasets. We provide a case study for transportation services using a data-driven approach to identify primary bicycle corridors for public transportation by leveraging emerging GPS trajectory datasets.

Original languageEnglish (US)
Title of host publication2nd International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with KDD 2013
DOIs
StatePublished - Sep 19 2013
Event2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, IL, United States
Duration: Aug 11 2013Aug 11 2013

Publication series

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

Other

Other2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Country/TerritoryUnited States
CityChicago, IL
Period8/11/138/11/13

Keywords

  • network hausdorff distance
  • spatial data mining
  • trajectory similarity

Fingerprint

Dive into the research topics of 'Fast and exact network trajectory similarity computation: A case-study on bicycle corridor planning'. Together they form a unique fingerprint.

Cite this