Taxi Passenger Travel Spatial and Temporal Characteristics Analysis and Application Based on Ridesourcing Data

Abstract

Nowadays, with the development of humanistic consciousness, the residents' travel behavior is becoming more and more important to be considered in urban planning, and has become an important reference for urban traffic construction. The ridesourcing softwares like Uber and Didi have been widely accepted and used. As a model of travel, the ridesourcing has many features, like convenience and flexibility, and the origin and destination of every trip are completely determined by passengers, the running track of vehicles can directly reflect the travel behavior of urban residents. So this paper will study the passengers' travel behaviour by data mining based on the Didi order data, try to reveal the travel characteristics of urban residents from a macro perspective. This paper focuses on these issues: to analyze the order data from ridesourcing company Didi; to explore the temporal characteristics of passengers' travel from different angles like the temporal distribution of travel, the distribution of peak hour, time consumption; to analyze the spatial characteristics from the different aspects including the distribution of travel distance, travel hot spots; to propose an optimization model of the taxi stand location by analyzing travel characteristics, which can improve user's experience of residents by setting up reasonable site layout. Finally we found that the ridesourcing track data can well reveal travel characteristics of urban residents, which could be helpful for the urban planning and road network optimization.