Taxi-Passenger-Demand Modeling Based on Big Data from a Roving Sensor Network

Abstract

Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on offline data collected by manual investigations, which are often dated and inaccurate for real-time analysis. To address this issue, we propose Dmodel, employing roving taxicabs as real-time mobile sensors to (i) infer passenger arriving moments by interactions of vacant taxicabs, and then (ii) infer passenger demand by customized online training with both historical and real-time data. Dmodel utilizes a novel parameter called pickup pattern based on an entropy of pickup events (accounts for various real-world logical information, e.g., bad weather) to reduce the size of big historical taxicab data to be processed. We evaluate Dmodel with a real-world 450 GB dataset of 14,000 taxicabs for a half year, and results show that compared to the ground truth, Dmodel achieves 83 percent accuracy and outperforms a statistical model by 42 percent. We further present an application where Dmodel is used to dispatch vacant taxicabs to achieve an equilibrium between passenger demand and taxicab supply across urban regions.