Learning from the offline trace: A case study of the taxi industry

Yingjie Zhang, Beibei Li, Ramayya Krishnan, Siyuan Liu

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

The growth of mobile and sensor technologies today leads to the digitization of individual's offline behavior. Such large-scale and fine-grained information can help better understand individual decision making. We instantiate our research by analyzing the digitized taxi trails to study the impact of information on driver behavior and economic outcome. We propose homogeneous and heterogeneous Bayesian learning models and validate them using a unique data set containing complete information on 10.6M trip records from 11,196 taxis in a large Asian city in 2009. We find strong heterogeneity in individual learning behavior and driving decisions, which significantly associate with individual economic outcome. Interestingly, our policy simulations indicate information that is noisy at individual level can become most valuable after being aggregated across various spatial and temporal dimensions. Overall, our work demonstrates the potential of analyzing the digitized offline behavioral trace to infer demand as well as to improve individual decision efficiency.

Original languageEnglish (US)
StatePublished - Jan 1 2015
Event2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015 - Fort Worth, United States
Duration: Dec 13 2015Dec 16 2015

Other

Other2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015
CountryUnited States
CityFort Worth
Period12/13/1512/16/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences
  • Applied Mathematics

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