TY  - JOUR
N2  - Several previous studies examined the variables of public-transit-related walking and privately owned vehicles (POVs) to go to work. However, most studies neglect the possible nonlinear relationships between these variables and other potential variables. Using the 2017 U.S. National Household Travel Survey, we employ the Bayesian Network algorithm to evaluate the non-linear and interaction impacts of health condition attributes, work trip attributes, work attributes, and individual and household attributes on walking and privately owned vehicles to reach public transit stations to go to work in California. The authors found that the trip time to public transit stations is the most important factor in individualsâ?? walking decision to reach public transit stations. Additionally, it was found that this factor was mediated by population density. For the POV model, the population density was identified as the most important factor and was mediated by travel time to work. These findings suggest that encouraging individuals to walk to public transit stations to go to work in California may be accomplished by adopting planning practices that support dense urban growth and, as a result, reduce trip times to transit stations. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
IS  - 7
N1  - cited By 4
ID  - scholars16913
TI  - How Sustainable Is Peopleâ??s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships
KW  - Bayesian analysis; machine learning; population density; sustainability; travel time; urban growth
KW  -  California; United States
AV  - none
JF  - Sustainability (Switzerland)
A1  - Tang, P.
A1  - Aghaabbasi, M.
A1  - Ali, M.
A1  - Jan, A.
A1  - Mohamed, A.M.
A1  - Mohamed, A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128193753&doi=10.3390%2fsu14073989&partnerID=40&md5=8a17623f1ff458ed7cbfdbc9cbde3bc4
VL  - 14
Y1  - 2022///
PB  - MDPI
SN  - 20711050
ER  -