Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.
{"title":"The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions","authors":"Yantang Zhang, Xiaowei Hu","doi":"10.5198/jtlu.2024.2434","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2434","url":null,"abstract":"Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.
{"title":"Non-linear effects of built environment factors on mode choice: A tour-based analysis","authors":"Jia Fang, Xiang Yan, Tao Tao, Changjie Chen","doi":"10.5198/jtlu.2024.2403","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2403","url":null,"abstract":"Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhongzhen Yang, Jionghao Li, Wenyuan Zhou, F. Lian
This study explores the optimal subsidy policy to maximize the benefits associated with the suburbanization of university campuses. A transport accessibility index is introduced, and a model is developed to analyze faculty housing relocation, incorporating factors such as transport accessibility, housing price, relocation subsidy, and the influence of children. The impact of housing relocation is assessed using a regional output model that considers both production and consumption aspects. Subsequently, a decision-making model is established to determine the optimal subsidy level and the number of faculty to relocate, with the overarching goal of maximizing total regional benefits. The findings reveal that an increase in subsidies correlates with a rise in the willingness of faculty to relocate, leading to heightened benefits for the region. However, the rate of benefit increase shows diminishing returns with each increment change in the subsidy. Notably, the study demonstrates that 70% of the additional benefits to the region emanate from the housing market, accurately reflecting the current financial landscape in China. This insight underscores why city governments frequently leverage land markets to actively promote suburbanization.
{"title":"Optimization of the subsidy for university faculty relocation in campus suburbanization","authors":"Zhongzhen Yang, Jionghao Li, Wenyuan Zhou, F. Lian","doi":"10.5198/jtlu.2024.2341","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2341","url":null,"abstract":"This study explores the optimal subsidy policy to maximize the benefits associated with the suburbanization of university campuses. A transport accessibility index is introduced, and a model is developed to analyze faculty housing relocation, incorporating factors such as transport accessibility, housing price, relocation subsidy, and the influence of children. The impact of housing relocation is assessed using a regional output model that considers both production and consumption aspects. Subsequently, a decision-making model is established to determine the optimal subsidy level and the number of faculty to relocate, with the overarching goal of maximizing total regional benefits. The findings reveal that an increase in subsidies correlates with a rise in the willingness of faculty to relocate, leading to heightened benefits for the region. However, the rate of benefit increase shows diminishing returns with each increment change in the subsidy. Notably, the study demonstrates that 70% of the additional benefits to the region emanate from the housing market, accurately reflecting the current financial landscape in China. This insight underscores why city governments frequently leverage land markets to actively promote suburbanization.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As cities across the world embrace the benefits of rapid transit technology and invest in the expansion of existing infrastructure or plan for the introduction of new lines, the differences in both benefits and externalities that bus rapid transit (BRT) and rail rapid transit (RRT) bring remain unclear. This study aimed to address that gap and understand whether there was a distinction in impacts on the residential migration of households in different income and residential tenure groups as the result of BRT and RRT projects. This was achieved by exploring the effects of both modes in the same metropolitan region—metro Vancouver. This study used three BRT and three RRT lines that were in service for all or part of the 20 years spanning 1996 through 2016 to assess the rates of in-movement of households by income in Census Tracts (CTs) within 800 meters (½-mile) of a given rapid line. Our analysis suggested that areas adjacent to the Expo-Millennium RRT Corridor saw fewer in-movers between the 2001 Census and the 2016 Census than the areas without rapid transit infrastructure, while the same was true for the CTs affected by BRT lines and that had a larger than average share of new housing while holding everything else (e.g., housing supply) constant. While we did not find evidence to state that the presence of rapid transit infrastructure disproportionately affected any one of the income groups, our analysis suggested that there were more affluent renters moving in along the RRT and BRT lines. At the same time, the share of low-income renters that moved into areas close to rapid transit lines remained relatively stable. This research added a unique perspective to the debate cities and transport agencies have been experiencing with respect to decisions around the investment into different transport technologies and contributed to the argument for the need to carefully plan and provide rapid transit infrastructure together with affordable and diverse housing options.
{"title":"If you build it, who will come? Exploring the effects of rapid transit on residential movements in Metro Vancouver","authors":"Bogdan Kapatsila, Jordan D. Rea, Emily Grisé","doi":"10.5198/jtlu.2024.2364","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2364","url":null,"abstract":"As cities across the world embrace the benefits of rapid transit technology and invest in the expansion of existing infrastructure or plan for the introduction of new lines, the differences in both benefits and externalities that bus rapid transit (BRT) and rail rapid transit (RRT) bring remain unclear. This study aimed to address that gap and understand whether there was a distinction in impacts on the residential migration of households in different income and residential tenure groups as the result of BRT and RRT projects. This was achieved by exploring the effects of both modes in the same metropolitan region—metro Vancouver. This study used three BRT and three RRT lines that were in service for all or part of the 20 years spanning 1996 through 2016 to assess the rates of in-movement of households by income in Census Tracts (CTs) within 800 meters (½-mile) of a given rapid line. Our analysis suggested that areas adjacent to the Expo-Millennium RRT Corridor saw fewer in-movers between the 2001 Census and the 2016 Census than the areas without rapid transit infrastructure, while the same was true for the CTs affected by BRT lines and that had a larger than average share of new housing while holding everything else (e.g., housing supply) constant. While we did not find evidence to state that the presence of rapid transit infrastructure disproportionately affected any one of the income groups, our analysis suggested that there were more affluent renters moving in along the RRT and BRT lines. At the same time, the share of low-income renters that moved into areas close to rapid transit lines remained relatively stable. This research added a unique perspective to the debate cities and transport agencies have been experiencing with respect to decisions around the investment into different transport technologies and contributed to the argument for the need to carefully plan and provide rapid transit infrastructure together with affordable and diverse housing options.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people's commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research.
{"title":"Exploring spatial association between residential and commercial urban spaces: A machine learning approach using taxi trajectory data","authors":"Lei Zhou, Weiye Xiao, Chen Wang, Haoran Wang","doi":"10.5198/jtlu.2024.1800","DOIUrl":"https://doi.org/10.5198/jtlu.2024.1800","url":null,"abstract":"Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people's commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140412058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.
{"title":"Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction","authors":"Jiahui Zhao, Zhibin Li, Pan-xue Liu, Mingye Zhang","doi":"10.5198/jtlu.2024.2348","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2348","url":null,"abstract":"Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.\u0000","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study identifies built environmental factors that influence the determination of fault in urban pedestrian crashes in the United States, with implications for both safety and equity. Using data from Columbus, Ohio, we apply regression modeling, spatial analysis, and case studies, and find pedestrians are more likely to be found at fault on fast, high-volume arterial roads with bus stops. We also observe that better provision of crossings leads to more marked intersection crashes, which are less likely to be blamed on pedestrians. In addition, large differences in both the provision of crossings and fault exist between neighborhoods. We interpret findings through the lenses of the systems-oriented safety approaches Safe Systems and Vision Zero. The conclusion argues that the designation of individual responsibility for crashes preempts collective responsibility, preventing wider adoption of design interventions as well as systemic changes to the processes that determine the built environment of US roadways.
{"title":"The built environment and the determination of fault in urban pedestrian crashes: Toward a systems-oriented crash investigation","authors":"Jonathan Stiles, Harvey Miller","doi":"10.5198/jtlu.2024.2335","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2335","url":null,"abstract":"This study identifies built environmental factors that influence the determination of fault in urban pedestrian crashes in the United States, with implications for both safety and equity. Using data from Columbus, Ohio, we apply regression modeling, spatial analysis, and case studies, and find pedestrians are more likely to be found at fault on fast, high-volume arterial roads with bus stops. We also observe that better provision of crossings leads to more marked intersection crashes, which are less likely to be blamed on pedestrians. In addition, large differences in both the provision of crossings and fault exist between neighborhoods. We interpret findings through the lenses of the systems-oriented safety approaches Safe Systems and Vision Zero. The conclusion argues that the designation of individual responsibility for crashes preempts collective responsibility, preventing wider adoption of design interventions as well as systemic changes to the processes that determine the built environment of US roadways.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study identifies built environmental factors that influence the determination of fault in urban pedestrian crashes in the United States, with implications for both safety and equity. Using data from Columbus, Ohio, we apply regression modeling, spatial analysis, and case studies, and find pedestrians are more likely to be found at fault on fast, high-volume arterial roads with bus stops. We also observe that better provision of crossings leads to more marked intersection crashes, which are less likely to be blamed on pedestrians. In addition, large differences in both the provision of crossings and fault exist between neighborhoods. We interpret findings through the lenses of the systems-oriented safety approaches Safe Systems and Vision Zero. The conclusion argues that the designation of individual responsibility for crashes preempts collective responsibility, preventing wider adoption of design interventions as well as systemic changes to the processes that determine the built environment of US roadways.
{"title":"The built environment and the determination of fault in urban pedestrian crashes: Toward a systems-oriented crash investigation","authors":"Jonathan Stiles, Harvey Miller","doi":"10.5198/jtlu.2024.2335","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2335","url":null,"abstract":"This study identifies built environmental factors that influence the determination of fault in urban pedestrian crashes in the United States, with implications for both safety and equity. Using data from Columbus, Ohio, we apply regression modeling, spatial analysis, and case studies, and find pedestrians are more likely to be found at fault on fast, high-volume arterial roads with bus stops. We also observe that better provision of crossings leads to more marked intersection crashes, which are less likely to be blamed on pedestrians. In addition, large differences in both the provision of crossings and fault exist between neighborhoods. We interpret findings through the lenses of the systems-oriented safety approaches Safe Systems and Vision Zero. The conclusion argues that the designation of individual responsibility for crashes preempts collective responsibility, preventing wider adoption of design interventions as well as systemic changes to the processes that determine the built environment of US roadways.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
尽管土地使用和交通网络对出行行为的影响众所周知，但很少有研究在模拟出行行为时联合考察家庭和工作地点特征的影响。在本研究中，我们提出了一种分两步走的方法，来研究家庭和工作地点特征对使用新公共交通服务意愿的综合影响。本研究利用 2019 年蒙特利尔流动性调查（n=1698）的数据，考察了使用蒙特利尔在建轻轨 Réseau Express Métropolitain (REM) 的通勤意愿。首先根据通勤者的家庭和工作地点特征对其进行了细分分析，得出了六个不同的家庭工作群组。然后将这些群组与社会经济和态度特征一起纳入有序逻辑回归模型，以模拟使用 REM 的意图。支配分析的结果显示，即使在控制了个人特征的情况下，群组也是使用 REM 的意向的第三大重要决定因素。加入集群后，模型得到了显著改善（可能性从 -2400.7 改善为 -2388.9，p 值小于 0.05）。在 95% 的置信区间内，所有其他聚类使用 REM 的意向概率（在 32% 到 51% 之间）都明显低于典型通勤者（他们通常乘坐公交车从郊区通勤到市中心）。这些发现强调了建立放射状公共交通网络的意义，说明所提出的方法能够确定哪些群体可能从公共交通项目中受益，并根据家庭和工作地点的共同模式提出建议。
{"title":"Will you ride the train? A combined home-work spatial segmentation approach","authors":"Vincent Obry-Legros, G. Boisjoly","doi":"10.5198/jtlu.2024.2278","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2278","url":null,"abstract":"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.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
尽管土地使用和交通网络对出行行为的影响众所周知，但很少有研究在模拟出行行为时联合考察家庭和工作地点特征的影响。在本研究中，我们提出了一种分两步走的方法，来研究家庭和工作地点特征对使用新公共交通服务意愿的综合影响。本研究利用 2019 年蒙特利尔流动性调查（n=1698）的数据，考察了使用蒙特利尔在建轻轨 Réseau Express Métropolitain (REM) 的通勤意愿。首先根据通勤者的家庭和工作地点特征对其进行了细分分析，得出了六个不同的家庭工作群组。然后将这些群组与社会经济和态度特征一起纳入有序逻辑回归模型，以模拟使用 REM 的意图。支配分析的结果显示，即使在控制了个人特征的情况下，群组也是使用 REM 的意向的第三大重要决定因素。加入集群后，模型得到了显著改善（可能性从 -2400.7 改善为 -2388.9，p 值小于 0.05）。在 95% 的置信区间内，所有其他聚类使用 REM 的意向概率（在 32% 到 51% 之间）都明显低于典型通勤者（他们通常乘坐公交车从郊区通勤到市中心）。这些发现强调了建立放射状公共交通网络的意义，说明所提出的方法能够确定哪些群体可能从公共交通项目中受益，并根据家庭和工作地点的共同模式提出建议。
{"title":"Will you ride the train? A combined home-work spatial segmentation approach","authors":"Vincent Obry-Legros, G. Boisjoly","doi":"10.5198/jtlu.2024.2278","DOIUrl":"https://doi.org/10.5198/jtlu.2024.2278","url":null,"abstract":"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.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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