Influences of LRT on travel behaviour: A retrospective study on movers in Minneapolis

Cao, X.(J), Ermagun, A., 2017, in Urban Studies

doi:10.1177/0042098016651569
Location Minneapolis-St. Paul metro area, United States
Population General
Sample size 597
Factor analysis type exploratory factor analysis, oblimin rotation
Stepwise regression no
Removal of insignificant variables yes
Reviewed by LCM

Abstract

Following scant evidence for the effects of proximity to rail transit on car use, we pinpoint the impacts of rail transit and neighbourhood characteristics on both transit and car use in the Minneapolis-St. Paul metropolitan area. We apply the structural equations modelling approach on 597 residents who moved into the Hiawatha Light Rail Transit (LRT) corridor after it opened. The analysis is based on a self-administrated survey where all attributes of the built environment and transit quality are perceived measures. Using a quasi-longitudinal design to compare the behaviour of movers into the Hiawatha and control corridors, we found that the Hiawatha LRT acts as both a catalyst and a magnet. Movers into the Hiawatha corridor experience transit improvement, which increases transit use and reduces car use. The LRT also enables transit-liking people who were unable to realise their preference previously to relocate near the LRT. However, the LRT has no significant effects on changes in car ownership. © 2016, © Urban Studies Journal Limited 2016.

Factors

Variable Pattern loading
Shopping areas within walking distance (Res preference/perception) 0.393
Easy access to a regional shopping mall (Res preference/perception) 0.72
Easy access to downtown (Res preference/perception) 0.375
Religious or civic buildings nearby (Res preference/perception) 0.507
Variable Pattern loading
High quality living unit (Res preference/perception) 0.605
Good investment potential (Res preference/perception) 0.391
Attractive appearance of neighborhood (Res preference/perception) 0.673
High level of upkeep in neighborhood (Res preference/perception) 0.609
Variable Pattern loading
Sidewalks throughout the neighborhood (Res preference/perception) 0.452
Good bicycle routes beyond the neighborhood (Res preference/perception) 0.839
Parks and open spaces nearby (Res preference/perception) 0.377
Variable Pattern loading
Low level of car traffic on neighborhood streets (Res preference/perception) 0.874
Quiet neighborhood (Res preference/perception) 0.735
Variable Pattern loading
Low crime rate within neighborhood (Safety) 0.426
Safe neighborhood for walking (Safety) 0.815
Safe neighborhood for kids to play in outdoors (Res preference/perception) 0.54
Variable Pattern loading
Lots of interaction among neighbors (Res preference/perception) 0.784
Lots of people out and about within the neighborhood (Res preference/perception) 0.752
Variable Pattern loading
Large back yards (Res preference/perception) 0.64
Lots of off-street parking (garages or driveways) (Res preference/perception) 0.481
Variable Pattern loading
Good public transit services (bus or rail) (Res preference/perception) 0.964
Easy to access transit stop/station (Res preference/perception) 0.921

Models

Source variable Target variable Effect p-value Effect type
Move into LRT corridor Change in transit use 0.23 <0.1 direct_effect
Move into LRT corridor Transit improvement 0.43 <0.05 direct_effect
Transit improvement Change in transit use 0.2 <0.05 direct_effect
Transit improvement Change in car use -0.23 <0.05 direct_effect
Move into suburban corridor Transit improvement -0.31 <0.05 direct_effect
Move into suburban corridor Change in transit use -0.35 <0.05 direct_effect
Transit Change in transit use 0.09 <0.1 direct_effect
Socializing (change in) Change in car use -0.08 <0.1 direct_effect
Change in HH size Change in car use 0.11 <0.05 direct_effect
Change in # of autos Change in car use 0.18 <0.05 direct_effect
Change in transit use Change in car use -0.19 <0.05 correlation
Source variable Target variable Effect p-value Effect type
Move into LRT corridor Change in transit use 0.27 <0.05 direct_effect
Move into LRT corridor Transit improvement 0.43 <0.05 direct_effect
Transit improvement Change in transit use 0.19 <0.05 direct_effect
Transit improvement Change in car use -0.22 <0.05 direct_effect
Move into suburban corridor Transit improvement -0.28 <0.1 direct_effect
Move into suburban corridor Change in transit use -0.42 <0.05 direct_effect
Change in # of autos Change in car use 0.24 <0.05 direct_effect
Change in transit use Change in car use -0.21 <0.05 correlation

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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