2020
Guo, J.; Feng, T.; Timmermans, H. J. P.
Modeling co-dependent choice of workplace, residence and commuting mode using an error component mixed logit model Journal Article
In: Transportation, vol. 47, iss. 2, 2020, ISSN: 15729435.
Abstract | Links | BibTeX | Tags: Co-dependent location, Error component logit model, Heterogeneity, Multidimensional choice, Panel data, Pivoted stated choice experiment
@article{Guo2020,
title = {Modeling co-dependent choice of workplace, residence and commuting mode using an error component mixed logit model},
author = {J. Guo and T. Feng and H. J. P. Timmermans},
doi = {10.1007/s11116-018-9927-y},
issn = {15729435},
year = {2020},
date = {2020-01-01},
journal = {Transportation},
volume = {47},
issue = {2},
abstract = {This paper develops an error component mixed logit model to analyze the multi-dimensional residential, work and transportation mode choice. It expanse previous studies based on life-trajectory theory which predominantly only considered two life domains. In contributing to this emerging field of research, we design an integrated pivoted stated choice experiment considering the multi-dimensional choice of job, residence and transportation mode for the journey to work. The results of the estimated error component mixed logit model with panel effects indicate that most selected attributes of the residential environment, job profile and transportation mode are significantly related to individual differences in multidimensional choices. Moreover, the estimation of various sources of unobserved heterogeneity signals significant unobserved heterogeneity in selected taste parameters, and choice dependent heteroscedasticity in error component variance.},
keywords = {Co-dependent location, Error component logit model, Heterogeneity, Multidimensional choice, Panel data, Pivoted stated choice experiment},
pubstate = {published},
tppubtype = {article}
}
This paper develops an error component mixed logit model to analyze the multi-dimensional residential, work and transportation mode choice. It expanse previous studies based on life-trajectory theory which predominantly only considered two life domains. In contributing to this emerging field of research, we design an integrated pivoted stated choice experiment considering the multi-dimensional choice of job, residence and transportation mode for the journey to work. The results of the estimated error component mixed logit model with panel effects indicate that most selected attributes of the residential environment, job profile and transportation mode are significantly related to individual differences in multidimensional choices. Moreover, the estimation of various sources of unobserved heterogeneity signals significant unobserved heterogeneity in selected taste parameters, and choice dependent heteroscedasticity in error component variance.
Sun, Q.; Feng, T.; Kemperman, A.; Spahn, A.
Modal shift implications of e-bike use in the Netherlands: Moving towards sustainability? Journal Article
In: Transportation Research Part D: Transport and Environment, vol. 78, 2020, ISSN: 13619209.
Abstract | Links | BibTeX | Tags: Cycling, E-bike, Modal shift, Panel data
@article{Sun2020,
title = {Modal shift implications of e-bike use in the Netherlands: Moving towards sustainability?},
author = {Q. Sun and T. Feng and A. Kemperman and A. Spahn},
doi = {10.1016/j.trd.2019.102202},
issn = {13619209},
year = {2020},
date = {2020-01-01},
journal = {Transportation Research Part D: Transport and Environment},
volume = {78},
abstract = {This paper investigates the modal shift patterns of e-bike users in the Dutch context. We focus on the change in e-bikers’ travel behavior to assess whether this change benefits sustainability. Our study provides direct ecologically valid evidence on modal shift by using a longitudinal dataset from the Netherlands Mobility Panel survey. We examine e-bikers’ modal shift patterns before and after acquiring an e-bike. The findings indicate that after e-bike adoptions, conventional bike use reduces significantly, while car use reduces less strongly. Nonetheless, the share of car kilometers is much larger than that of conventional bikes at the baseline. Besides, the emission rate per passenger kilometer of an e-bike is several times lower than that of a car. These imply a net environmental gain after e-bike adoptions. The present study also sheds light on modal shifts at a disaggregated level by investigating those e-bikers who are more likely to drive less after e-bike adoption. The findings suggest that e-bikers younger than 50 and those around retirement age (60–69) seem more likely to step out of their cars. Additionally, people living in rural areas tend to be more likely to reduce their car use than their counterparts in highly urbanized areas. Based on our findings, we present policy recommendations for achieving a greener shift in mobility systems.},
keywords = {Cycling, E-bike, Modal shift, Panel data},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the modal shift patterns of e-bike users in the Dutch context. We focus on the change in e-bikers’ travel behavior to assess whether this change benefits sustainability. Our study provides direct ecologically valid evidence on modal shift by using a longitudinal dataset from the Netherlands Mobility Panel survey. We examine e-bikers’ modal shift patterns before and after acquiring an e-bike. The findings indicate that after e-bike adoptions, conventional bike use reduces significantly, while car use reduces less strongly. Nonetheless, the share of car kilometers is much larger than that of conventional bikes at the baseline. Besides, the emission rate per passenger kilometer of an e-bike is several times lower than that of a car. These imply a net environmental gain after e-bike adoptions. The present study also sheds light on modal shifts at a disaggregated level by investigating those e-bikers who are more likely to drive less after e-bike adoption. The findings suggest that e-bikers younger than 50 and those around retirement age (60–69) seem more likely to step out of their cars. Additionally, people living in rural areas tend to be more likely to reduce their car use than their counterparts in highly urbanized areas. Based on our findings, we present policy recommendations for achieving a greener shift in mobility systems.