Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers

Yingjie Zhang (), Beibei Li and Ramayya Krishnan ()
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Yingjie Zhang: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080
Ramayya Krishnan: Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Information Systems Research, 2020, vol. 31, issue 4, 1301-1321

Abstract: The ubiquitous deployment of mobile and sensor technologies enables observation and recording of human behavior in physical (off-line) 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 this study, using a Bayesian learning model with a rich data set consisting of approximately two million fine-grained Global Positioning System (GPS) observations, we analyze 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 data (i.e., whether the taxi is occupied or not), and taxi drivers’ daily incomes. This capacity to use data for which 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 focus on pertain to actions drivers take to find new passengers after they have dropped off current passengers. In particular, we study 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 find 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 aggregated across relevant spatial and temporal dimensions. Moreover, we find 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 because of potential market expansion. Efficient information sharing can bring, within the taxi market, additional income-generating opportunities that could be unfulfilled. Overall, this study not only explains driver decision-making behavior but also provides taxi companies with an implementable information-sharing strategy to improve overall market efficiency.

Keywords: mobility analytics; GPS trajectory; Bayesian learning; taxi industry; driver heterogeneity; information sharing; driver welfare (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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