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

Abstract

In this study, using a Bayesian learning model with a rich data set consisting of 2 million fine-grained GPS observations, we study the role of information observable by or made available to taxi 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 because of potential market expansion. Efficient information sharing can bring 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.