Hello, everyone. My name is Guang Wang.
I'm a Ph.D. student from Rutgers University. Today, it's my honor to show our recent work with you
The title of the paper is understanding user behavior in car sharing services through the lens of mobility
This is a joint work between
Rutgers University, Beijing Jiaotong University, and Southeast University
I will introduce our work following this outline.
let's go into the introduction first
In recent years, we have been witnessing a rapid increase of sharing economy
in which users play a critical role, so understanding user behavior is very important to improve sharing economy services
In this paper, we are focused on one of the innovative shared mobility, the car sharing
The reason is that during the past decade, the number of car sharing users has significantly
increased from about 1 million to over 20 million
Understanding user behavior in car sharing is important for new findings discovery, and system enhancement and better user experiences
Now we are fortunate to have access to both the attitude-free and attitude-aware data
which provides us a new way to understand user behavior through the lens of mobility
Hence in this work, we aim to uncover comprehensive user behavior in car sharing
from different aspects, and we try to answer three research questions
The first research question is people's attitude to car sharing service
and the second research question is who are more likely to use car sharing services and when where why how long how regulars they will use the
car sharing services and the last question is what are the similarities and differences
for the car sharing usage in different cities
To answer the three questions, in this paper, we adopt a mixed-methods approach that combines an online survey and logged order data analysis
Based on our online survey, we report some results.
The first one is most people are willing to use car sharing services, and the second is most people use car sharing
for traveling instead of commuting. And we also study the reasons that may prevent people
from using car sharing, and we found that the inconvenience would be the most important reason
and we also found that over half of the participants expressed optimistic
attitude towards car sharing, which means car sharing may be a promising mobility mode in the future
We then report some results based on our data-driven analysis, and we found the
pickup and return patterns are highly dynamic in different hours of different days, and the usage on
weekend are also different from weekdays, and we also found that about 50% of the car sharing usage are within five hours
and we then investigate where users use car sharing
we found that the most visited stations are all
located in the CBD area and the high-tech IT industry bases.
And specifically, during 9to 10 a.m, we found the top five stations with the most vehicle net inflows are in the IT industry bases basis and the CBD area.
And we introduced the entropy and conditional entropy to quantify the regularity of car sharing users' user behavior
We found that when users original pickup station is known, the destination conditional entropy is very low which means their destination stations
are more stable and may be predicted. And we found that about seventy percent of the users visited
no more than two service stations, which means that they have stable usage and revisitation behavior
We then investigated the age-based analysis, we found about 90% of the
users are between 18 years old to 38 years old, and we found the most usage
are from 27 years old to 29 years old people. And for young and elder people, they have different usage
in weekends and weekdays. And we also report some findings that relate to the
user gender, and we found that female users have more stable destinations than male users, and they
have different popular locations for the female and male users
and we also compare user behavior in different cities. We found that even though there are some similar usage patterns but there
are also some different usage patterns in different cities
and finally based on our findings, we extracted five categories of features that are highly related to the user behavior
Then we develop a behavior-aware XGBoost method to predict the usages
We compare our method with other methods and we found our behavior aware XGBoost
method achieves the best performance
finally, we summarize some key findings and implications. In this work, we report many new findings based on our qualitative and quantitative studies
and these findings have the potential to
provide guidance for service station deployment
vehicle balancing and pricing mechanism
and we also report some limitations and future work
For more data and work, please refer to our website, thank you very much!