Taxis' Short-Term Out-of-Service Behaviors Detection Using Big Trace Data

Existing studies of big data taxi GPS tracesdo not consider the characteristics and demands of out-of-service taxi driver activities, such as refueling, dining, and shifting activities. This paper studies the these short-term out-of-service behaviors, extracts short-term out-of-service behaviors from taxi trace data, and analyzes the spatio temporal distribution of these events with kernel density estimation (KDE) for linear features. We also analyze the spatial correlation between short-term taxi out-of-service behaviors and locations of gas stations, using Ripley's K function. Our experimental results show that this approach effectively uncovers short-term taxi driver out-of-service demands and exposed the ineffective allocation of urban public resources, by analyzing spatio temporal distribution of short-term out-of-service taxi activities. Our results couldsupport decision-making concerning adjustment and optimization of public resources.