Research on the travel characteristics of online carpooling and subway multimodal transit based on big data

With the development of intelligent transport, big data has become an important means to analyze traffic problems. Aiming at the problem of commuting demands in suburban areas without rail transit coverage, this paper proposes a data fusion method based on the data of online carpooling and the AFC data of subway, analyzes the travel characteristics of online carpooling and subway multimodal transit, and estimates the fuel saving benefits. The conclusion proves that the multimodal transit is a suitable way to satisfy commuting demands and can save 78.1% energy consumption on average compared with driving alone. This research will provide support for transport intelligent operation management.