TY - JOUR
T1 - Multimodal travel groups and attitudes
T2 - A latent class cluster analysis of Dutch travelers
AU - Molin, Eric
AU - Mokhtarian, Patricia
AU - Kroesen, Maarten
N1 - Harvest
Available online 10-12-2016
PY - 2016
Y1 - 2016
N2 - For developing sustainable travel policies, it may be helpful to identify multimodal travelers, that is, travelers who make use of more than one mode of transport within a given period of time. Of special interest is identifying car drivers who also use public transport and/or bicycle, as this group is more likely to respond to policies that stimulate the use of those modes. It is suggested in the literature that this group may have less biased perceptions and different attitudes towards those modes. This supposition is examined in this paper by conducting a latent class cluster analysis, which identifies (multi)modal travel groups based on the self-reported frequency of mode use. Simultaneously, a membership function is estimated to predict the probability of belonging to each of the five identified (multi)modal travel groups, as a function of attitudinal variables in addition to structural variables. The results indicate that the (near) solo car drivers indeed have more negative attitudes towards public transport and bicycle, while frequent car drivers who also use public transport have less negative public transport attitudes. Although the results suggest that in four of the five identified travel groups, attitudes are congruent with travel mode use, this is not the case for the group who uses public transport most often. This group has relatively favorable car attitudes, and given that many young, low-income travelers belong to this group, it may be expected that at least part of this group will start using car more often once they can afford it. Based on the results, challenges for sustainable policies are formulated for each of the identified (multi)modal travel groups.
AB - For developing sustainable travel policies, it may be helpful to identify multimodal travelers, that is, travelers who make use of more than one mode of transport within a given period of time. Of special interest is identifying car drivers who also use public transport and/or bicycle, as this group is more likely to respond to policies that stimulate the use of those modes. It is suggested in the literature that this group may have less biased perceptions and different attitudes towards those modes. This supposition is examined in this paper by conducting a latent class cluster analysis, which identifies (multi)modal travel groups based on the self-reported frequency of mode use. Simultaneously, a membership function is estimated to predict the probability of belonging to each of the five identified (multi)modal travel groups, as a function of attitudinal variables in addition to structural variables. The results indicate that the (near) solo car drivers indeed have more negative attitudes towards public transport and bicycle, while frequent car drivers who also use public transport have less negative public transport attitudes. Although the results suggest that in four of the five identified travel groups, attitudes are congruent with travel mode use, this is not the case for the group who uses public transport most often. This group has relatively favorable car attitudes, and given that many young, low-income travelers belong to this group, it may be expected that at least part of this group will start using car more often once they can afford it. Based on the results, challenges for sustainable policies are formulated for each of the identified (multi)modal travel groups.
KW - Attitude
KW - Factor analysis
KW - Latent class cluster analysis
KW - Mode choice
KW - Mode frequency
KW - Multimodality
UR - http://www.scopus.com/inward/record.url?scp=84949255899&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:255aff0e-f0c1-4db3-b3ff-b902261d9ab7
U2 - 10.1016/j.tra.2015.11.001
DO - 10.1016/j.tra.2015.11.001
M3 - Article
SN - 0965-8564
VL - 83
SP - 14
EP - 29
JO - Transportation Research. Part A: Policy & Practice
JF - Transportation Research. Part A: Policy & Practice
ER -