Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this paper, we explore drivers’ refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand, from which macro-scale economic decisions can be made, and a spatial, temporal and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose CATF (context-aware tensor factorization), a factorization model that considers a variety of contextual factors (e.g., price, brand and weather condition) that affect consumers’ refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain four-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show our system can determine refueling events with an accuracy of more than 90%, estimate time spent with less than-2-minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.