In this paper, we employ large‐scale sensor data to examine the impact of data‐based intelligence and work‐related experience on the time efficiency of individual taxi drivers, measured by their propensity of choosing the fastest routes. The identification strategy is built on (1) a unique exogenous policy shock‐banning taxi‐hailing app with an embedded GPS system, and (2) a measure of nonrecurring congestion avoidance, enabled by the real‐time sensor data, which serves as a proxy for GPS usage. Our empirical model provides evidence that data‐based intelligence improves taxi drivers’ routing decisions by close to 3% as measured by trip speed. Our results further demonstrate that inexperienced drivers have a higher chance of choosing the fastest route, as they are more likely to rely on the real‐time traffic information from GPS technology than experienced drivers. The general implications of our findings on the adoption and utilization of data‐based performance‐enhancing technology are discussed in closing.
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