Identify impacting factor for urban rail ridership from built environment spatial heterogeneity

Li, Xiang Yu; Sinniah, Gobi Krishna; Li, Ruiwei

doi:10.1016/j.cstp.2022.04.003

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…The increased usage of private vehicles not only contributes to road congestion but carbon gas emissions and accident rates as well. While issues with accessibility of public transportation have long been reported in previous studies, it is unclear how the built environment (BE) affects ridership, especially in Malaysia [4]. A blanket (one size fits all) approach guideline such as Malaysia's TOD policies may not be sufficient to improve and promote sustainable urban transport, as reported in similar study by Kamruzzaman, et al [5].…”

Section: Introductionmentioning

confidence: 97%

“…There are very few local studies that have analyzed ridership based on smart card data about spatial heterogeneity of built environment for each transit station. There are local studies [4,18,19] who examined ridership with surrounding catchment area and land use, but the measurement of ridership data is limited to the annual or monthly ridership summary only. In addition, the parcels of land use might not represent specifically to the respective train stations in study.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, parcel usage might be too generalized as compared to the statistical granularities of POI data which are much smaller [20]. Yu Li, et al [4] identified factors impacting ridership in Klang Valley but the temporal IOP Publishing doi:10.1088/1755-1315/1240/1/012010 3 measurement used are considered very wide. This will be difficult to identify the inconsistency of different temporal scales for users' ridership more clearly.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, using spatial and temporal variability data that can be observed using user generated instruments, it is possible to find out whether there are significant changes in feeder service ridership as we get closer to rail transit stations. The temporal scale needs to be seen in a smaller hourly scale as opposed to monthly in local study by Yu Li, et al [4]. Within this environment, the contributing ridership pattern throughout the day can be identified and built environment factors that impact to a presumably high or low ridership may be examined.…”

Section: Introductionmentioning

confidence: 99%

“…There are various spatial regressions done previously, among them are Geographically Weighted Regression (GWR), Ordinary Least Squares (OLS) and many more as done in previous studies [28][29][30] for modelling spatially varying relationship. In Malaysia, spatial regressions have been done extensively and among them are to analyze house prices [31,32] and to identify factors impacting ridership in Yu Li, et al [4]. Analyzing space time energy use in residential buildings [33] using geographically and temporally weighted regression (GTWR) model was done in Seattle Washington where the time factor was also taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Conceptual Framework for Maximizing Service Catchment Area Between Rail Transit and Feeder Service Using Spatial Temporal Regression Technique

Halim,

Rosni,

Tsong

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The move towards a more sustainable transport mode is crucial in the Klang Valley by incorporating feeder services to rail network stations. Feeder services will complement the expanding rail transits network and deliver connectivity for areas not covered within rail stations. Past literatures found that feeder services were ineffective during rail breakdown as feeder service was not going or stopping to where it was intended to. Feeder service can be allocated more effectively if we have better idea of targeted service area. Urban planners now have access to a variety of urban analytics because of technological advancements and the rise of smart cities. Data generated are produced across numerous locations and at diverse time scales, providing a significant possibility for data mining to uncover insightful information when both space and time are considered. This study will attempt to assess the community’s accessibility to rail transit through feeder service in Klang Valley. In achieving this a conceptual framework is developed within the objectives of this study which are firstly, to identify built environment factors that influence ridership around transit stations. Secondly, to determine the spatial -temporal relationship between built environment and transit ridership of feeder service and rail transits. Next, the station-level spatial and temporal variability of ridership data between feeder service to rail network is examined. Finally, as the main finding of this study, a framework in maximizing a service catchment transportation service for feeder service and rail transit will be developed. In doing that, the conceptual framework approach is by addressing numerical analytical technique in geographic data, by using spatial temporal regression. This study aspires to provide planning departments and transit agencies with useful guidance to execute targeted policies and develop a sustainable and efficient feeder service coverage area to support rail transits by looking at both spatial and temporal scales.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%