2021
Gu, G.; Feng, T.; Yang, D.; Timmermans, H.
Modeling dynamics in household car ownership over life courses: a latent class competing risks model Journal Article
In: Transportation, vol. 48, iss. 2, 2021, ISSN: 15729435.
Abstract | Links | BibTeX | Tags: Car ownership, Heterogeneity, Latent class competing risks model, Life events
@article{Gu2021b,
title = {Modeling dynamics in household car ownership over life courses: a latent class competing risks model},
author = {G. Gu and T. Feng and D. Yang and H. Timmermans},
doi = {10.1007/s11116-019-10078-8},
issn = {15729435},
year = {2021},
date = {2021-01-01},
journal = {Transportation},
volume = {48},
issue = {2},
abstract = {This study presents a latent class competing risks model to examine the influence of socio-demographics and life course events on car transaction behaviour. The types of car transaction and interval times between car transactions events are incorporated in a competing risk model. To capture unobserved behavioural heterogeneity across the population, the model classifies households into different segments. Results estimated based on retrospective survey data show significant heterogeneity exist in household car ownership decisions. The covariates are found to have different effects on car ownership decisions between different classes. Households in the class labelled “Young households without a car” are more sensitive to life course events related to household composition. Households labelled as “middle-aged and aged households with car(s)” are more sensitive to life course events related to job and house locations.},
keywords = {Car ownership, Heterogeneity, Latent class competing risks model, Life events},
pubstate = {published},
tppubtype = {article}
}
This study presents a latent class competing risks model to examine the influence of socio-demographics and life course events on car transaction behaviour. The types of car transaction and interval times between car transactions events are incorporated in a competing risk model. To capture unobserved behavioural heterogeneity across the population, the model classifies households into different segments. Results estimated based on retrospective survey data show significant heterogeneity exist in household car ownership decisions. The covariates are found to have different effects on car ownership decisions between different classes. Households in the class labelled “Young households without a car” are more sensitive to life course events related to household composition. Households labelled as “middle-aged and aged households with car(s)” are more sensitive to life course events related to job and house locations.
2019
Guo, J.; Feng, T.; Timmermans, H. J. P.
Time-varying dependencies among mobility decisions and key life course events: An application of dynamic Bayesian decision networks Journal Article
In: Transportation Research Part A: Policy and Practice, vol. 130, 2019, ISSN: 09658564.
Abstract | Links | BibTeX | Tags: Concurrent, Dynamic Bayesian network, Lagged and lead effects, Life events
@article{Guo2019,
title = {Time-varying dependencies among mobility decisions and key life course events: An application of dynamic Bayesian decision networks},
author = {J. Guo and T. Feng and H. J. P. Timmermans},
doi = {10.1016/j.tra.2019.09.008},
issn = {09658564},
year = {2019},
date = {2019-01-01},
journal = {Transportation Research Part A: Policy and Practice},
volume = {130},
abstract = {People's long-term mobility decisions depend on their current situation, past history and/or future plans. Consequently, models of long-term mobility decisions should take lagged, concurrent and/or lead effects into account. Contributing to the literature on long-term mobility analysis, this study develops an integrated framework for modeling the temporally interdependent choices related to residential change, job change and car purchasing decisions. Using retrospective life trajectory data collected through a Web-based survey, a dynamic Bayesian network model is estimated. Results show that different life domains are highly interdependent. Concurrent, as well as lagged and lead effects are observed.},
keywords = {Concurrent, Dynamic Bayesian network, Lagged and lead effects, Life events},
pubstate = {published},
tppubtype = {article}
}
People’s long-term mobility decisions depend on their current situation, past history and/or future plans. Consequently, models of long-term mobility decisions should take lagged, concurrent and/or lead effects into account. Contributing to the literature on long-term mobility analysis, this study develops an integrated framework for modeling the temporally interdependent choices related to residential change, job change and car purchasing decisions. Using retrospective life trajectory data collected through a Web-based survey, a dynamic Bayesian network model is estimated. Results show that different life domains are highly interdependent. Concurrent, as well as lagged and lead effects are observed.