Will you ride the train? A combined home-work spatial segmentation approach
While the influence of land use and transport networks on travel behavior is known, few studies have jointly examined the effects of home and work location characteristics when modelling travel behavior. In this study, a two-step approach is proposed to investigate the combined effect of home and w...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Minnesota
2024-02-01
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| Series: | Journal of Transport and Land Use |
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| Online Access: | http://jtlu.org/index.php/jtlu/article/view/2278 |
| _version_ | 1797306685504618496 |
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| author | Vincent Obry-Legros Geneviève Boisjoly |
| author_facet | Vincent Obry-Legros Geneviève Boisjoly |
| author_sort | Vincent Obry-Legros |
| collection | DOAJ |
| description |
While the influence of land use and transport networks on travel behavior is known, few studies have jointly examined the effects of home and work location characteristics when modelling travel behavior. In this study, a two-step approach is proposed to investigate the combined effect of home and work location characteristics on the intent to use a new public transport service. Using data from the 2019 Montreal Mobility Survey (n=1698), this study examines the intent to use the Réseau Express Métropolitain (REM), a light rail under construction in Montreal, for commuting. A segmentation analysis is first conducted to characterize commuters based on their home and work location characteristics, resulting in six distinct home-work clusters. The clusters are then included in an ordered logistic regression modelling the intent to use the REM, along with socio-economic and attitudinal characteristics. Results from a dominance analysis reveal that the clusters are the third most important determinants of the intent to use the REM, even when controlling for individual characteristics. The addition of the clusters leads to a significant improvement of the model (likelihood of -2388.9 improved from -2400.7, p-value < 0,05). All other clusters have a significantly lower probability (between 32 and 51% less likely) of intent to use the REM than the typical commuters (who commute from the suburbs to downtown, often by transit), at a 95% confidence interval. These findings underscore the implications of pursuing radial public-transport networks, illustrating the ability of the proposed approach to identify which groups are likely to benefit from a public-transport project and to propose recommendations anchored in joint home and work location patterns.
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| first_indexed | 2024-03-08T00:45:28Z |
| format | Article |
| id | doaj.art-c0467678e8934a768c40d73bd32d560d |
| institution | Directory Open Access Journal |
| issn | 1938-7849 |
| language | English |
| last_indexed | 2024-03-08T00:45:28Z |
| publishDate | 2024-02-01 |
| publisher | University of Minnesota |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj.art-c0467678e8934a768c40d73bd32d560d2024-02-15T12:12:43ZengUniversity of MinnesotaJournal of Transport and Land Use1938-78492024-02-01171Will you ride the train? A combined home-work spatial segmentation approachVincent Obry-Legros0Geneviève Boisjoly1Polytechnique MontrealPolytechnique Montreal While the influence of land use and transport networks on travel behavior is known, few studies have jointly examined the effects of home and work location characteristics when modelling travel behavior. In this study, a two-step approach is proposed to investigate the combined effect of home and work location characteristics on the intent to use a new public transport service. Using data from the 2019 Montreal Mobility Survey (n=1698), this study examines the intent to use the Réseau Express Métropolitain (REM), a light rail under construction in Montreal, for commuting. A segmentation analysis is first conducted to characterize commuters based on their home and work location characteristics, resulting in six distinct home-work clusters. The clusters are then included in an ordered logistic regression modelling the intent to use the REM, along with socio-economic and attitudinal characteristics. Results from a dominance analysis reveal that the clusters are the third most important determinants of the intent to use the REM, even when controlling for individual characteristics. The addition of the clusters leads to a significant improvement of the model (likelihood of -2388.9 improved from -2400.7, p-value < 0,05). All other clusters have a significantly lower probability (between 32 and 51% less likely) of intent to use the REM than the typical commuters (who commute from the suburbs to downtown, often by transit), at a 95% confidence interval. These findings underscore the implications of pursuing radial public-transport networks, illustrating the ability of the proposed approach to identify which groups are likely to benefit from a public-transport project and to propose recommendations anchored in joint home and work location patterns. http://jtlu.org/index.php/jtlu/article/view/2278Travel behaviorSegmentation analysisPublic transportIntent to useSpatial analysisCommuting patterns |
| spellingShingle | Vincent Obry-Legros Geneviève Boisjoly Will you ride the train? A combined home-work spatial segmentation approach Journal of Transport and Land Use Travel behavior Segmentation analysis Public transport Intent to use Spatial analysis Commuting patterns |
| title | Will you ride the train? A combined home-work spatial segmentation approach |
| title_full | Will you ride the train? A combined home-work spatial segmentation approach |
| title_fullStr | Will you ride the train? A combined home-work spatial segmentation approach |
| title_full_unstemmed | Will you ride the train? A combined home-work spatial segmentation approach |
| title_short | Will you ride the train? A combined home-work spatial segmentation approach |
| title_sort | will you ride the train a combined home work spatial segmentation approach |
| topic | Travel behavior Segmentation analysis Public transport Intent to use Spatial analysis Commuting patterns |
| url | http://jtlu.org/index.php/jtlu/article/view/2278 |
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