Impacts of land use and amenities on public transport use, urban planning and design

Hu, Nan; Legara, Erika Fille; Lee, Kee Khoon; Hung, Gih Guang; Monterola, Christopher

doi:10.1016/j.landusepol.2016.06.004

Cited by 62 publications

(26 citation statements)

References 24 publications

(15 reference statements)

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“…The literature confirms that walking and public transportation ridership among older adults is positively correlated to mixed land-use, and supportive land-uses are more important A. Soltani et al Journal of Transport Geography 68 (2018) 109-117 than income in shifting people away from cars (Hu et al, 2016;Kim and Ulfarsson, 2004;Winters et al, 2015). Surprisingly, built environment features such as population density and road network design characteristics are not statistically significant in Shiraz.…”

Section: Discussionmentioning

confidence: 78%

Socio-demographic and built environment determinants of car use among older adults in Iran

Soltani

Pojani

Askari

et al. 2018

Journal of Transport Geography

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Internationally, an age-friendly built environment, including safe, affordable, and convenient transportation, has been identified as crucial in enhancing the quality of life for increasingly large numbers of elderly people. This study presents data on the urban travel and mobility patterns of older adults in Shiraz, Iran -a country where the population is ageing faster than the average of western societies. The relationship between car use and sociodemographic and built environment variables is investigated through Ordinary Least Squares and Geographically Weighted Regression models. These models show that the main determinants of car use among older adults are: employment status; household size; car ownership rate; trip purpose; property prices; and mixed land-uses. Also, the study reveals that car dependency is significant and more pronounced among wealthier individuals, who tend to be clustered in the northern section of the city. The population density and road network design characteristics do not affect car use among older adults, while a mix of land-uses tends to attenuate car dependency. Gender gaps in car use are much wider than in the West, and some level of spatial mismatch between travel needs and car access is observed in the southern section of the city. Clearly, Iranian cities must move away from the current car-centric development model.

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Section: Discussionmentioning

confidence: 78%

Socio-demographic and built environment determinants of car use among older adults in Iran

Soltani

Pojani

Askari

et al. 2018

Journal of Transport Geography

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“…Other methods have also been considered, such as multiplicative regression Zhao et al, 2014;Kepaptsoglou et al, 2017) [3,22,23], two-stage least square regression (2SLS) (Taylor et al, 2004;Estupiñán and Rodriguez, 2008) [24,25], Poisson regression [3,6], negative binomial regression [11], and structural equation modelling (SEM) (Sohn and Shim, 2010) [8]; geographical methods such as distance-decay weighted regression [1] and the network Kriging method (Zhang and Wang, 2014) [26]; machine learning methods such as the decision tree (DT) and support vector regression (SVR); and item-based collaborative filtering methods based on cosine similarity (CF) (Hu et al, 2016) [27], cluster analysis (Deng and Xu, 2015) [28], and back propagation neural networks (BPNN) (Li et al, 2016) [29]. However, understanding the results is a major challenge in terms of the interpretability of the function modeled by the machine learning algorithm.…”

Section: Models For Estimating Transit Ridershipmentioning

confidence: 99%

“…For example, Jun et al (2015) [21] first evaluated land use characteristics, including the proportions of residential, commercial and office, manufacturing, and mixed land use, within the PCAs of metro stations in the Seoul metropolitan area, and then explored the impact of these characteristics on metro station ridership. Hu et al (2016) [27] examined the association between land use characteristics at two levels of granularity and public transit ridership in Singapore in time and space. The main socio-economics variables used are population, employment, and automobile ownership ratio.…”

Section: Explanatory and Response Variablesmentioning

confidence: 99%

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Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro

Zhao

Tsui

2019

Applied Sciences

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Ridership analysis at the local level has a pivotal role in sustainable urban construction and transportation planning. In practice, urban rail transit (URT) ridership is affected by complex factors that vary across the urban area. The aim of this study is to model and explore the factors that impact metro station ridership in Shenzhen, China from a local perspective. The direct demand model, which uses ordinary least squares (OLS) estimation, is the most widely used method of ridership modeling. However, OLS estimation assumes parametric stability. This study investigates the use of a direct demand model on the basis of geographically weighted regression (GWR) to model the local relationships between metro station ridership and potential influencing factors. Real-world Shenzhen Metro smart card data are used to test and verify the applicability and performance of the model. The results show that GWR performs better than OLS estimation in terms of both model fitting and spatial interpretation. The GWR model demonstrates a high level of interpretability regarding the spatial distribution and variation of each coefficient, and thus can provide insights for decision-makers into URT ridership and its complex factors from a local perspective.

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“…Individual travel patterns have been used by several studies for inferring the respective trip purpose [9,10,[13][14][15][16]. For instance, Alexander et al [9] infer trip purpose from call detail records (CDRs) which are collected through the use of mobile phones that contain time-stamped geo-coordinates.…”

Section: Inference Based On Individual Travel Patterns and Additionalmentioning

confidence: 99%

Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data

et al. 2019

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Impacts of land use and amenities on public transport use, urban planning and design

Cited by 62 publications

References 24 publications

Socio-demographic and built environment determinants of car use among older adults in Iran

Socio-demographic and built environment determinants of car use among older adults in Iran

Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro

Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data

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