An integrated urban-transport smart growth model around metro stations: A case of Qatar

Alkhereibi, Aya Hasan; AlSuwaidi, Maryam; Al-Mohammed, Reem; Pokharel, Shaligram; Ayari, Mohamed Arselene

doi:10.1016/j.trip.2021.100392

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Due to the availability of data, existing studies have drawn different conclusions on factors affecting ridership of subway stations. In the selection of the influencing factors of built environment, some scholars have selected from the aspects of land use, social and economic factors and traffic environment [18,[24][25][26][27]. In analyzing the factors that influence transportation, Cervero and Kockelman summarized the "3D" dimensions of the built environment, namely density, diversity and design.…”

Section: Introductionmentioning

confidence: 99%

“…To sum up, the literature review shows that the methods and built environment factors used by Chinese scholars in the research are similar to those in other countries, but the methods adopted in the delineation of pedestrian catchment area (PCA) for subway stations are slightly different. Some scholars [6,10,17,35] use Thiessen polygon and circular buffer overlay method in their research, while other scholars use zones of neighborhoods [16,26] or buffer method [9,13,15,18,25]. In terms of research on the ridership and built environment, more of them choose a single scale range and less of them analyze the impact of built environment factors on the subway ridership considering the MAUP's scale and zoning effects.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments

et al. 2022

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The accuracy of the regression model of ridership of subway stations depends on the scale range of the built environment around the subway stations. Previous studies have not considered the Modifiable Area Unit Problem (MAUP) to establish the regression model of subway station ridership. Taking Beijing as an example, this paper expanded the built environment variables from “5D” category to “7D” category, added indicators such as parking fee standard and population density factor, and proposed a Multi-Scale Geographical Weighted Regression (MGWR) model of outbound ridership of subway stations with standardized variables. The goodness of fit of regression models under 10 spatial scales or built environment around subway stations are compared, and the spatial heterogeneity of built environment factors under the optimal spatial scale of outbound ridership of subway stations during the morning peak on weekdays is discussed. The results show that: (1) the scale range overlapped by 1000 m radius circular buffer zone and Thiessen polygon has the highest explanatory power for the regression model, and is regarded as the optimal scale range of built environment; (2) the density of office facilities, sports and leisure facilities, medical service facilities, building density and floor area ratio (FAR) has a significant impact on the outbound ridership of all subway stations; (3) office facilities, catering facilities, FAR, number of parking lots, and whether subway stations are transfer stations have a positive impact on outbound ridership. The number of medical service facilities, sports and leisure facilities, bus stops and building density have a negative impact on outbound ridership; (4) the two added factors in this study: parking charge standard and population density, as the influencing factors of the built environment, have a significant impact on the outbound ridership of some subway stations; and (5) the different local coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity on the outbound ridership. The results can provide an important theoretical basis for the prediction and analysis of demand of ridership at subway stations and the integration of the built environment around the stations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments

et al. 2022

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“…It is well known that urban transport must play a central role in the development of today's urban structures that occupy huge areas and require excessive travel to meet basic needs, worker travel, etc. Qatar has very limited expertise with public transport and transit-oriented systems, resulting in additional resistance to changes from a vehicle-oriented society to a TOD [13]. Moreover, ref.…”

Section: Public Transportation Sustainabilitymentioning

confidence: 99%

Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development

et al. 2023

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The endeavors toward sustainable transportation systems are a key concern for planners and decision-makers where increasing public transport attractiveness is essential. In this paper, a machine-learning-based predictive modeling approach is proposed for metro ridership prediction, considering the built environment around the stations; it is in the best interest of sustainable transport planning to ultimately contribute to the achievement of Sustainable Development Goals (UN-SDGs). A total of twelve parameters are considered as input features including time of day, day of the week, station, and nine types of land use density. Hence, a time-series database is used for model development and testing. Several machine learning (ML) models were evaluated for their predictive performance: ridge regression, lasso regression, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and stacking ensemble learner. Bayesian optimization and grid search are combined with 10-fold cross-validation to tune the hyperparameters of each model. The performance of the developed models was validated based on the test dataset using five quantitative performance measures. The results demonstrated that, among the base learners, the decision tree showed the highest performance with an R2 of 87.4% on the test dataset. KNN and SVR were the second and third-best models among the base learners. Furthermore, the feature importance investigation explains the relative contribution of each type of land use density to the prediction of the metro ridership. The results showed that governmental land use density, educational facilities land use density, and mixed-use density are the three factors that play the most critical role in determining total ridership. The outcomes of this research could be of great help to the decision-making process for the best achievement of sustainable development goals in relation to sustainable transport and land use.

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Free interchange for better transit? Assessing the multi-dimensional impacts on metro to bus interchange behavior − insights from an explainable machine learning method

Gu,

Zhang,

et al. 2025

Travel Behaviour and Society

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An integrated urban-transport smart growth model around metro stations: A case of Qatar

Cited by 5 publications

References 24 publications

Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments

Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments

Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development

Free interchange for better transit? Assessing the multi-dimensional impacts on metro to bus interchange behavior − insights from an explainable machine learning method

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