Paper Info
Title
Uncovering cabdrivers' behavior patterns from their digital traces
Abstract
Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers' operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers' behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers' operation patterns and compare the different cabdrivers' behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers' operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence. Published by Elsevier Ltd.
Year
DOI
Venue
2010
10.1016/j.compenvurbsys.2010.07.004
Computers, Environment and Urban Systems
Keywords
DocType
Volume
Cabdriver,Operation behavior,Mobility pattern,GPS,Spatial–temporal data mining,Pervasive computing
Journal
34
Issue
ISSN
Citations 
6
0198-9715
54
PageRank 
References 
Authors
3.26
7
3
Name
Order
Citations
PageRank
Liu Liang120913.46
Clio Andris21179.67
Carlo Ratti31211113.38