Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration

Hatami, Faizeh; Thill, Jean-Claude

doi:10.3390/su14127179

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…Our independent variables measuring the built environment are land-use mix using an entropy index, bus route density, population density, street intersection density (as a proxy for urban block size), distance to the closest light rail station, and job accessibility. These variables are calculated with geospatial analysis software ArcGIS using shapefile data, as explained in an earlier study [10]. Since larger block groups may naturally include a larger variety of land uses, they would have higher land-use mix values.…”

Section: Study Methodsmentioning

confidence: 99%

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Non-Linear Associations Between the Urban Built Environment and Commuting Modal Split: A Random Forest Approach and SHAP Evaluation

et al. 2023

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The study of commuting mode choice is crucial since driving, with all its associated environmental and economic consequences, is the United States' most popular mode of transportation due to urban sprawl, priority to road construction and America's love affair with the automobile. More attention needs to be paid to sustainable modes such as public transit and walking. The built environment is expected to have an impact on commuting mode choice. Built environments with higher density, diversity, intentional design, destination accessibility, and shorter distance to transit (collectively known as the 5 Ds of the built environment) are hypothesized to lead to more sustainable mode choices, including public transit and walking. In this paper, we evaluate the impact of built environment variables on commuting modal split, including the four modes of public transit-bus, public transit-rail, walking, and driving. The study is conducted in Mecklenburg County, North Carolina, at the geographic level of census block groups in year 2015. Given the complexity of relationships in the built environment-travel behavior subject, the random forest method is used to predict aggregated commuting mode choice. Random forest is employed as it is capable of capturing nonlinear relationships and is not constrained by limitations in other widely used methods, such as multinomial logistic regression. After predicting the commuting mode shares, SHAP values (SHapley Additive exPlanations) are used to evaluate the impact of the built environment on commuting mode choices. As an advanced machine learning method, SHAP values adds explainability to the model. This method resolves the known limitation of machine learning methods as being ''black boxes'' and converts them to ''white boxes'' by providing interpretability. They provide insights into both the direction and magnitude of the relationships. Thanks to its rigorous ML-based design, our study helps to solidify the state of knowledge with strong evidence that block groups with higher degrees of the 5Ds lead to more choices of public transit and walking modes. We discuss urban policy implications of this study.

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Section: Study Methodsmentioning

confidence: 99%

“…To date, numerous studies have investigated the impacts of the built environment on different aspects of people's mobility and travel behaviors (e.g., [7], [9], [10], [11]). Most of these studies have used statistical and econometric models to gauge these effects [12], [13], [14].…”

Section: Introduction a Context And Objectivesmentioning

confidence: 99%

Non-Linear Associations Between the Urban Built Environment and Commuting Modal Split: A Random Forest Approach and SHAP Evaluation

et al. 2023

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“…Because of this, the earlier studies exploring the effect of land use on travel have come out with mixed findings. Travel related to overall trips or mandatory trips is extensively analysed (Hatami and Thill, 2022;Hu et al, 2022), but the non-mandatory trips are rarely verified. Mode choice for automobile, transit and walking trips is intensively verified (Liu et al, 2021;Ding et al, 2018), but studies pertaining to the length of travel by different modes are limited.…”

Section: Introductionmentioning

confidence: 99%

An Examination of the Land Use Determinants of Travel: A Case Study of Calicut City in India

Sreelekha¹

2023

European Transport/Trasporti Europei

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Several travel studies focus mainly on mode shifting. Again, most travel studies have focused on mandatory trips. But the influence of land use on travel is rarely verified in all these studies. This paper examines the influence of land use measures on travel in a medium-sized city, India. The travel characteristics like number of trips, person-km and person-hr of travel by various modes and for various purposes are analysed separately. Land use is characterised by population density, employment density, land use intensity, land use mix and accessibility. During the modelling process, the explanatory variables are subjected to transformation to get over the nonlinearity issue. Statistical analysis reveals that a balanced mix of various land use activities causes people to travel less by any mode and for any purpose. Such studies can help the city planners formulate effective transport policies to improve the quality of transport within the city.

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Non-business services performance forecasting for small urban areas using a spatiotemporal deep learning model

Hatami,

Poor,

Thill

2024

Cities

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Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration

Cited by 5 publications

References 49 publications

Non-Linear Associations Between the Urban Built Environment and Commuting Modal Split: A Random Forest Approach and SHAP Evaluation

Non-Linear Associations Between the Urban Built Environment and Commuting Modal Split: A Random Forest Approach and SHAP Evaluation

An Examination of the Land Use Determinants of Travel: A Case Study of Calicut City in India

Non-business services performance forecasting for small urban areas using a spatiotemporal deep learning model

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