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

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.

[1]  Yanfeng Ouyang,et al.  A Structured Flexible Transit System for Low-Demand Areas , 2011 .

[2]  Cláudio T. Silva,et al.  Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips , 2013, IEEE Transactions on Visualization and Computer Graphics.

[3]  Susan Shaheen,et al.  App-Based, On-Demand Ride Services: Comparing Taxi and Ridesourcing Trips and User Characteristics in San Francisco , 2014 .

[4]  Chun-Hsiao Chu,et al.  Taxi Vacancy Rate, Fare, and Subsidy with Maximum Social Willingness-to-Pay under Log-Linear Demand Function , 2009 .

[5]  Bruce Schaller,et al.  Entry controls in taxi regulation: Implications of US and Canadian experience for taxi regulation and deregulation , 2007 .

[6]  C. Daganzo.  On the design of public infrastructure systems with elastic demand , 2012 .

[7]  Ke Xu,et al.  Understanding ride-on-demand service: Demand and dynamic pricing , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[8]  Hai Yang,et al.  Pricing strategies for a taxi-hailing platform , 2016 .

[9]  Shi An,et al.  Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data , 2018 .

[10]  Abhishek Singhal,et al.  Analysis of taxi demand and supply in New York City: implications of recent taxi regulations , 2015 .

[11]  Y. Nie.  How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China , 2017 .

[12]  S. Ukkusuri,et al.  Taxi market equilibrium with third-party hailing service , 2017 .

[13]  R. Cervero,et al.  Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco , 2016 .

[14]  A. D. Vany.  Capacity Utilization under Alternative Regulatory Restraints: An Analysis of Taxi Markets , 1975 .

[15]  Luis González Abril,et al.  Trip destination prediction based on past GPS log using a Hidden Markov Model , 2010, Expert Syst. Appl..

[16]  Xiqun Chen,et al.  Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach , 2017 .

[17]  G. Douglas.  PRICE REGULATION AND OPTIMAL SERVICE STANDARDS , 2016 .

[18]  M. Kandlikar,et al.  Taxi apps, regulation, and the market for taxi journeys , 2016 .