Learning Individual Behavior Using Sensor Data: The Case of GPS Traces and Taxi Drivers

Forthcoming in Information Systems Research

61 Pages Posted: 13 May 2016 Last revised: 18 May 2020

See all articles by Yingjie Zhang

Yingjie Zhang

Peking University - Guanghua School of Management

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Krishnan Ramayya

School of Information Systems and Management, The H. John Heinz III College

Date Written: May 12, 2020

Abstract

The ubiquitous deployment of mobile and sensor technologies enables observation and recording of human behavior in physical (offline) settings in a manner similar to what has been possible to date in online settings. This provides researchers with a new lens through which to study and better understand previously unobservable individual decision-making processes. In the present study, using a Bayesian learning model with a rich data set consisting of approximately 2 million fine-grained GPS observations, we analyzed the decision-making behavior of 2,467 single-shift taxi drivers in a large Asian city with the objective of understanding key factors that drive the supply side of urban mobility markets. The data set includes detailed taxi GPS trajectories, taxi occupancy (i.e., whether the taxi is occupied or not) data, and taxi drivers' daily incomes. This capacity to use data where occupancy of the taxi is known is a distinctive feature of our data set and sets our work apart from prior work in the literature. The specific decisions we focused on pertain to actions drivers take to find new passengers after they have dropped off current passengers. In particular, we studied the role of information derivable from GPS trace data (e.g., where passengers were
dropped off, where they were picked up, longitudinal taxicab travel history with fine-grained time stamps) observable by or made available to drivers in enabling them to learn the distribution of demand for their services over space and time. We found significant differences between new and experienced drivers in both learning behavior and driving decisions. Drivers benefit significantly from their ability to learn from not only information directly observable in the local market but also aggregate information on demand flows across markets. Interestingly, our policy simulations indicate that information that is noisy at the individual level becomes valuable when being aggregated across relevant spatial and temporal dimensions. Moreover, we found that the value of information does not increase monotonically with the scale and frequency of information sharing. Our results also provide important evidence that efficient information sharing can lead to a welfare increase among drivers due to potential market expansion. Efficient information sharing can bring, within the taxi market, additional income generating opportunities that could be unfulfilled. Overall,
the present study not only explains driver decision-making behavior but also provides taxi companies with an implementable information-sharing strategy to improve overall market efficiency.

Suggested Citation

Zhang, Yingjie and Li, Beibei and Ramayya, Krishnan, Learning Individual Behavior Using Sensor Data: The Case of GPS Traces and Taxi Drivers (May 12, 2020). Forthcoming in Information Systems Research, Available at SSRN: https://ssrn.com/abstract=2779328. or http://dx.doi.org/10.2139/ssrn.2779328

Yingjie Zhang

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Beibei Li (Contact Author)

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Krishnan Ramayya

School of Information Systems and Management, The H. John Heinz III College ( email )

Pittsburgh, PA 15213-3890
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
787
Abstract Views
3,027
Rank
58,405
PlumX Metrics