Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership

Kim, Dohyung; Ahn, Yongjin; Choi, Seungho; Kim, Kwang-Koo

doi:10.3390/su8101016

Cited by 21 publications

(16 citation statements)

References 36 publications

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“…Bao et al divided bikesharing stations into five types based on their surrounding points of interest (POIs) before exploring bikesharing travel patterns and trip purposes; it was found that relatively little travel behavior information can be extracted from LDA model without classification compared with after classification ( 20 ). Subway and taxi data were analyzed simultaneously to uncover factors on human mobility depending on the means of transportation in Seoul, with stations divided into several subsets with similar ridership patterns ( 21 ). Compared with modeling for the entire data directly without further processing, two-step modeling, clustering first and then modeling, has greatly improved performance in relation to rationality and veracity of result unscrambling ( 12 ).…”

Section: Related Workmentioning

confidence: 99%

“…The existing methods have been widely applied to investigate spatial variations in the nexus between transit ridership and ambient built environment. The ordinary least squares (OLS) regression model was the most common method in previous studies ( 21 ). Moreover, other traditional statistical models like the structural equation model (SEM), zero-inflated negative binomial model, hierarchical Bayesian models, and Poisson regression model were also frequently used in the field of transit ridership analysis ( 2 , 31 , 35 , 36 ).…”

Section: Related Workmentioning

confidence: 99%

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How Built Environment Impacts Online Car-Hailing Ridership

Wang

Chen

et al. 2020

Transportation Research Record

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Extensive research has shown that unilateral optimization of transit systems is not effective enough to significantly increase its transport efficiency. Considering that urban land-use characteristics, including residential, work, consumption, transit, and so forth, are significantly interrelated with travel demands and travel behaviors, this paper provides a way to optimize transit system by raising awareness of the relation between ridership and built environment. This paper adopted point of interest (POI) data to investigate the effect of physical built environment on online car-hailing ridership in Chengdu, China. The study area was tessellated with several Voronoi cells; these cells were further clustered into three ridership patterns based on the time-varying characteristic of ridership. Given that some differences existed in the three ridership patterns, a separate spatial ridership model was developed to understand the factors that influence ridership patterns using geographic weighted regression (GWR) analysis. The data and results verified that the built environment had various influences on online car-hailing alighting ridership in spatial and temporal dimensions, of which the significant POI factors for determining the ridership pattern during different periods were detected. Remarkably, this study took the ridership dataset from the online car-hailing transit system, mainly because the pick-up (PU) and drop-off (DO) locations generated by this service are closest to the origin and destination of the trip, except that it is more popular recently. Therefore, the analysis of the impact of built environment on travel based on the online car-hailing dataset can be captured in greater detail, with a higher degree of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

How Built Environment Impacts Online Car-Hailing Ridership

Wang

Chen

et al. 2020

Transportation Research Record

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“…Although rapid transit ridership involves count data, the preferable negative binomial regression and Poisson regression have been rarely used for station-level ridership analysis (Choi et al, 2012). The main reason is that count data models are perceived not to have any advantages for ridership analysis (Choi et al, 2012;Dill et al, 2013;Kim et al, 2016). Another violation of the assumptions underlying OLS regression analysis is that there is no correlation between the independent variables and the disturbance items.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

et al. 2019

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This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level. Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies. Four models (three global models-ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM) and one local model-geographically weighted regression (GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity. Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China. Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model. The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership. Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance. It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.

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“…In the Beijing case study developed by Wang et al [37], the intense population growth in suburban regions quadrupled individual transport emissions between 2000-2009 due to trip increases. The case study on land use near subway stations in Los Angeles conducted by Kim et al [38] demonstrated the link between the location of offices and shopping centers and the generation of travel. According to Dhakap and Schipper [17], urban mobility planning fails because it does not predict a decrease in vehicle inventory and the demand for travel; instead, it only provides infrastructure improvement directed to the most emitting mode, encouraging it.…”

Section: Data Transparency and Integrated Planningmentioning

confidence: 99%

Urban Mobility and Greenhouse Gas Emissions: Status, Public Policies, and Scenarios in a Developing Economy City, Natal, Brazil

Toledo

Rovere

2018

Sustainability

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This study aims to deepen the understanding of the role of the urban mobility sector in the current and future greenhouse gas (GHG) emissions of a middle-sized city of Brazil, which is also a developing economy. With the cross-reference between road and rail mobility data, governmental mobility planning, the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) of the Intergovernmental Panel on Climate Change (IPCC) emission quantification methodology, and the creation of scenarios for up to 10 subsequent years, it is possible to verify that individual motorized transport accounts for 60% of the total emissions from the urban transportation sector, with the largest amount of carbon dioxide equivalent (CO 2eq ) emissions per passenger among all of the forms of mobility. However, in the case of this study, government mobility planning, by not encouraging more energy-efficient transport and non-motorized modes, ends up aggravating GHG emissions in the scenarios considered for 2020 and 2025. In turn, the mitigation scenarios proposed herein integrate public transport and non-motorized transport solutions that would reduce the total of equivalent carbon dioxide (tCO 2eq ) by at least 45,000 tCO 2eq per year by 2025. This cross-referencing of the environmental impact of government mobility policies can be replicated in other cities in developing countries that do not yet present municipal inventories or emission monitoring.

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Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership

Cited by 21 publications

References 36 publications

How Built Environment Impacts Online Car-Hailing Ridership

How Built Environment Impacts Online Car-Hailing Ridership

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

Urban Mobility and Greenhouse Gas Emissions: Status, Public Policies, and Scenarios in a Developing Economy City, Natal, Brazil

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