Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity

Liu, Mengyang; Liu, Yuxuan; Ye, Yu

doi:10.1016/j.scs.2023.104613

Cited by 16 publications

(13 citation statements)

References 69 publications

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“…By considering variables such as distance to road, stream, and railway and terrain characteristics like elevation, aspect, and slope, the model can better simulate the spatial patterns of land use transitions and identify areas more prone to change. These variables provide valuable information about the landscape's physical and infrastructural characteristics, which can guide land use planning and management strategies [57,58]. The variables taken here are based on previous studies on this in other areas.…”

Section: Discussionmentioning

confidence: 99%

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Rani,

Gupta,

Singh

et al. 2023

Earth

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The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.

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Section: Discussionmentioning

confidence: 99%

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Rani,

Gupta,

Singh

et al. 2023

Earth

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“…These linear models have been utilized by numerous scholars to investigate how the built environment affects metro ridership. However, the linear model is to be established on the premise of assuming the stationarity of the impact, which will lead to certain deviations in the analysis results [40]. The nonlinear model is considered an effective approach to solving this problem.…”

Section: Modeling Methodsmentioning

confidence: 99%

“…Due to the fact that the scope of this analysis is typically determined using the "maximum" walking distance or the area most users walk to [18,37], the scope of the built environment analysis around metro stations is commonly referred to as the pedestrian catchment area (PCA). The existing PCAs mainly consist of three types: circular buffer [8,13,18,23,24,30,31,33,34,38], Tyson polygon [39], and Tyson polygon superimposed with circular buffer [9,24,25,40] (Figure 1). However, considering the dense area of the metro stations, residents in the overlapping areas of the two stations may choose either of the stations.…”

Section: Delineation Of Pca At Metro Stationsmentioning

confidence: 99%

“…In order to simplify the processing and better determine the scope of the city's TOD study [11], we have chosen circular buffers in this study. The radius of circular buffers selected by the existing studies is mostly 400 m [22], 500 m [13,31], 600 m [14,23,38], 800 m [8,10,18,24,33,40], and 1000 m [9,30]. The selection of buffer radius is mostly determined by pedestrian accessibility [17,23,33,40], experience [18], and reference to other research results [24].…”

Section: Delineation Of Pca At Metro Stationsmentioning

confidence: 99%

“…The radius of circular buffers selected by the existing studies is mostly 400 m [22], 500 m [13,31], 600 m [14,23,38], 800 m [8,10,18,24,33,40], and 1000 m [9,30]. The selection of buffer radius is mostly determined by pedestrian accessibility [17,23,33,40], experience [18], and reference to other research results [24]. In addition, in order to improve the accuracy of subway station PCA delineation, some scholars have proposed determining the PCA by goodness of fit.…”

Section: Delineation Of Pca At Metro Stationsmentioning

confidence: 99%

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Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing

Wang,

Li,

Zhang

et al. 2024

Sustainability

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Understanding the built environment’s impact on metro ridership is essential for developing targeted strategies for built environment renewal. Taking into consideration the limitations of existing studies, such as not proposing targeted strategies, using unified pedestrian catchment areas (PCA), and not determining the model’s accuracy, Beijing was divided into three zones from inside to outside by the distribution pattern of metro stations. Three PCAs were assumed for each zone and a total of 27 PCA combinations. The study compared the accuracy of the Ordinary Least Square (OLS) and several machine learning models under each PCA combination to determine the model to be used in this study and the recommended PCA combination for the three zones. Under the recommended PCA combinations for the three zones, the model with the highest accuracy was used to explore the built environment’s impact on metro ridership. Finally, prioritized stations for renewal were identified based on ridership and the built environment’s impact on metro ridership. The results are as follows: (1) The eXtreme Gradient Boosting (XGBoost) model has a higher accuracy and was appropriate for this study. The recommended PCA combination for the three zones in Beijing was 1000 m_1200 m_1800 m. (2) During the morning peak hours, the density of office and apartment facilities greatly influenced the ridership, with a strong threshold effect and spatial heterogeneity. Our research framework also provides a new way for other cities to determine the scope of Transit-Oriented Development (TOD) and proposes a new decision-making method for improving the vibrancy of metro stations.

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Implementation of Mutually Exclusive Pedestrian Accessibility Zones of the Metro on the Example of a Megalopolis

Markovskiy,

Churiakova,

Kosovan

et al. 2024

Lecture Notes in Computer Science

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Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity

Cited by 16 publications

References 69 publications

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing

Implementation of Mutually Exclusive Pedestrian Accessibility Zones of the Metro on the Example of a Megalopolis

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