Exploring the Nonlinear Relationship between the Built Environment and Active Travel in the Twin Cities

Tao, Tao; Wu, Xiaolan; Cao, Jason; Fan, Yingling

doi:10.1177/0739456x20915765

Cited by 63 publications

(39 citation statements)

References 50 publications

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“…Recent studies have attempted to describe the possible nonlinear relationships between the built environment and travel behavior with machine learning [43][44][45][46][47]. Machine learning contains various methods, including sigmoid regression, gradient boosting decision tree (GBDT), the generalized additive mixed model (GAMM), semiparametric model, random forest, and the artificial neural network (ANN).…”

Section: Nonlinear Relationship Of Travel and The Built Environmentmentioning

confidence: 99%

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Wang

Zhang

Zhao

et al. 2021

IJERPH

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The health and welfare of older adults have raised increasing attention due to global aging. Cycling is a physical activity and mode of transportation to enhance the mobility and quality of life among older adults. Nevertheless, the planning strategies to promote cycling among older adults are underutilized. Therefore, this paper describes the nonlinear associations of the built environment with cycling frequency among older adults. The data were collected from the Zhongshan Household Travel Survey (ZHTS) in 2012. The modeling approach was the eXtreme Gradient Boosting (XGBoost) model. The findings demonstrated that nonlinear relationships exist among all the selected built environment attributes. Within specific intervals, the population density, the land-use mixture, the distance from home to the nearest bus stop, and the distance from home to CBD are positively correlated to the cycling among older adults. Additionally, an inverse “U”-shaped relationship appears in the percentage of green space land use among all land uses. Moreover, the intersection density is inversely related to the cycling frequency among older adults. These findings provide nuanced and appropriate guidance for establishing age-friendly neighborhoods.

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Section: Nonlinear Relationship Of Travel and The Built Environmentmentioning

confidence: 99%

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Wang

Zhang

Zhao

et al. 2021

IJERPH

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“…Traditionally, transportation and urban planning has favoured motorized transport, resulting in significant impacts to environment, economy, society and public health (Tao et al, 2019). Travelers' dependence on private vehicles leads to congestion, severe air pollution and high noise levels especially in city centers.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Effects of Active Travel in Health and Quality of Life

Adamos

Nathanail

Theodoridou

et al. 2020

Transport and Telecommunication Journal

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AbstractThe ever-increasing tendency of people to travel by motorized vehicles contributes significantly to air pollution and traffic problems. Active travel, namely walking and cycling, seems to be a feasible solution to the current situation in urban mobility. The present paper aims at investigating the effects of active travel in health and quality of life and determine those factors that affect travel behavior. A structured literature review was carried out, which revealed the strong association of walking/cycling with the containment of noncommunicable diseases and the invigoration of wellbeing and self-confidence. In addition, a questionnaire survey was conducted in Greece, addressing the attitudes and perceptions of 507 people towards active travel, health and quality of life. Results showed that participants have a more positive attitude about walking compared to cycling, while characteristics such as age, gender, body mass index, physical and health conditions determine active commuting and consequently life satisfaction.

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“…We obtained pedestrian count data from Melbourne’s automated Pedestrian Counting System [ 62 ]. The descriptive statistics of the 32 sensors that were consistently active from 2014–2018 during AM, lunch, and PM periods, illustrated in Table 2 , suggest that overall foot-traffic in the city has been growing year on year, with the biggest increase between 2014–15.…”

Section: Methodsmentioning

confidence: 99%

“…However, since collinearity between different types of pedestrian flows is unavoidable (violating regression assumptions), a more flexible approach was needed. We therefore also calibrated the model on five different machine learning specifications, which are designed to account for collinearity as well as non-linear relationships between variables that some previous studies have emphasized [ 62 , 63 ]. These include Stochastic Gradient Descent (SGD), Support Vector Regression (SVR), Random Forest (RF), Bootstrap Aggregation (BAG), Gradient Boosting (GB), and Gaussian Process (GP).…”

Section: Methodsmentioning

confidence: 99%

We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in Melbourne

2021

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Cities are increasingly promoting walkability to tackle climate change, improve urban quality of life, and address socioeconomic inequities that auto-oriented development tends to exacerbate, prompting a need for predictive pedestrian flow models. This paper implements a novel network-based pedestrian flow model at a property-level resolution in the City of Melbourne. Data on Melbourne’s urban form, land-uses, amenities, and pedestrian walkways as well as weather conditions are used to predict pedestrian flows between different land-use pairs, which are subsequently calibrated against hourly observed pedestrian counts from automated sensors. Calibration allows the model extrapolate pedestrian flows on all streets throughout the city center based on reliable baseline observations, and to forecast how new development projects will change existing pedestrian flows. Longitudinal data availability also allows us to validate how accurate such predictions are by comparing model results to actual pedestrian counts observed in following years. Updating the built-environment data annually, we (1) test the accuracy of different calibration techniques for predicting foot-traffic on the city’s streets in subsequent years; (2) assess how changes in the built environment affect changes in foot-traffic; (3) analyze which pedestrian origin-destination flows explain observed foot-traffic during three peak weekday periods; and (4) assess the stability of model predictions over time. We find that annual changes in the built environment have a significant and measurable impact on the spatial distribution of Melbourne’s pedestrian flows. We hope this novel framework can be used by planners to implement “pedestrian impact assessments” for newly planned developments, which can complement traditional vehicular “traffic impact assessments”.

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Exploring the Nonlinear Relationship between the Built Environment and Active Travel in the Twin Cities

Cited by 63 publications

References 50 publications

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Investigating the Effects of Active Travel in Health and Quality of Life

We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in Melbourne

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