Shanghai Institute of Applied Physics, Chinese Academy of Sciences
Recommending good driving paths is valuable to taxi drivers for reducing unnecessary waste in fuel and increasing revenue. Driving only according to personal experience may lead to poor performance. With the availability of large-scale GPS traces collected from urban taxis, the authors have the curiosity about whether they can discover the hidden knowledge in the trace data for smart driving recommendation. This paper focuses on developing a smart recommender system based on mining large-scale GPS trace datasets from a large number of urban taxis. However, such the trace datasets are in nature complex, large-scale, and dynamic, which makes mining the datasets particularly challenging.