Modeling travel mode choice of young people with differentiated E-hailing ride services in Nanjing China

Abstract E-hailing ride service (ERS) has become increasingly popular globally and is changing the urban mobility landscape. There is insufficient research effort in understanding the impact of ERS on travel behavior, in particular among young people. This paper aims to start filling that research gap by first collecting mode choice preference data through a stated preference survey in City of Nanjing, China and then applying nested logit (NL) models and a series of post-estimation analysis to address a number of key research questions of mode choice behavior without and with ERS. Three ERS modes are considered in the Chinese context: DiDi Taxi (D-Taxi), DiDi Express (D-Express), and DiDi Premier (D-Premier), all provided by DiDi Chuxing, the dominant ERS service provider in China. The study finds that age makes little difference in mode choice preference when ERS is introduced between the two age groups considered (18–30 and 31–45). The study results also suggest that young travelers are naturally drawn to ERS for what it represents (a technology innovation) and its business (pricing) model. ERS appears to be a competitive alternative to the conventional modes especially when they are under performed. The study also finds that ERS will likely increase vehicle kilometers traveled (VKT) considerably, which will lead to increase in on-road vehicular emissions, unless some mechanism to switch users to ridesharing is in place.

[1]  P. Mokhtarian,et al.  What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California , 2018, Travel Behaviour and Society.

[2]  K. Train Discrete Choice Methods with Simulation , 2003 .

[3]  Naphtali Rishe,et al.  The Nash Equilibrium Among Taxi Ridesharing Partners , 2017, SIGSPATIAL/GIS.

[4]  Qian Yu,et al.  Improving urban bus emission and fuel consumption modeling by incorporating passenger load factor for real world driving , 2016 .

[5]  Bin Wang,et al.  Possible Emission Reductions From Ride-Sourcing Travel in a Global Megacity: The Case of Beijing , 2018 .

[6]  Lorentz Jäntschi,et al.  Design of Experiments: Useful Orthogonal Arrays for Number of Experiments from 4 to 16 , 2007, Entropy.

[7]  Biying Yu,et al.  Spatial Heterogeneous Characteristics of Ridesharing in Beijing–Tianjin–Hebei Region of China , 2018 .

[8]  P. Mokhtarian,et al.  Exploring the latent constructs behind the use of ridehailing in California , 2018, Journal of Choice Modelling.

[9]  Jonathan D. Hall,et al.  Is Uber a substitute or complement for public transit? , 2018, Journal of Urban Economics.

[10]  B. Taylor,et al.  Who knows about kids these days? Analyzing the determinants of youth and adult mobility in the U.S. between 1990 and 2009 , 2016 .

[11]  Toshiyuki Yamamoto,et al.  Men Shape a Downward Trend in Car Use among Young Adults—Evidence from Six Industrialized Countries , 2012 .

[12]  Alejandro Henao,et al.  Travel and energy implications of ridesourcing service in Austin, Texas , 2019, Transportation Research Part D: Transport and Environment.

[14]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[15]  Yi-Ming Wei,et al.  Environmental benefits from ridesharing: A case of Beijing , 2017 .

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

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

[18]  Weiwei Jiang,et al.  The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data , 2018, IEEE Access.

[19]  Ouri Wolfson,et al.  A Model of Multimodal Ridesharing and Its Analysis , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).