TY - JOUR
T1 - Predicting Travel Behavior by Analyzing Mobility Transactions
AU - Slik, Jesper
AU - Bhulai, Sandjai
PY - 2020/10
Y1 - 2020/10
N2 - Urban planning can benefit tremendously from a better understanding of where, when, why, and how people travel. Through advances in technology, detailed data on the travel behavior of individuals has become available. This data can be leveraged to understand why one prefers one mode of transportation over another one. In this paper, we analyze a unique dataset through which we can address this question. We show that the travel behavior in our dataset is highly predictable, with an accuracy of 97%. The main predictors are reachability features, more so than specific travel times. Moreover, the travel type (commute or personal) has a considerable influence on travel mode choice.
AB - Urban planning can benefit tremendously from a better understanding of where, when, why, and how people travel. Through advances in technology, detailed data on the travel behavior of individuals has become available. This data can be leveraged to understand why one prefers one mode of transportation over another one. In this paper, we analyze a unique dataset through which we can address this question. We show that the travel behavior in our dataset is highly predictable, with an accuracy of 97%. The main predictors are reachability features, more so than specific travel times. Moreover, the travel type (commute or personal) has a considerable influence on travel mode choice.
UR - https://iasks.org/jttm/volume-02/issue-2-2020
U2 - 10.5383/JTTM.02.02.004
DO - 10.5383/JTTM.02.02.004
M3 - Article
SN - 2371-5782
VL - 2
SP - 25
EP - 33
JO - Journal of Traffic and Transportation Management
JF - Journal of Traffic and Transportation Management
IS - 2
ER -