How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

Ding, Chuan; Liu, Chao

doi:10.1016/j.jtrangeo.2019.04.011

Cited by 209 publications

(118 citation statements)

References 37 publications

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“…Land use mix has a small influence on ridership at values below 0.6, above which ridership increases up to about 1000 boardings. Our finding affirms that the land use mix of a region should reach at least 0.6 to ensure maximum efficiency of the public transport system [30].…”

Section: Results Analysis and Discussionsupporting

confidence: 80%

“…Hospital density and scenic spot density also have considerable roles in promoting the use of public transport, contributing 5.25 and 2.89%, respectively. Land use mix contributes 4.96% to ridership, which is similar to the findings of Vergel-Tovar and Rodriguez [26] and Ding et al [30]. Public transport accessibility also influences ridership substantially [12,13].…”

Section: Relative Importance Of Independent Variablessupporting

confidence: 85%

“…Ma et al [13] applied a geographically and temporally weighted regression model to explore the temporal and spatial impact of the built environment on transit ridership at the TAZ level in Beijing. Ding et al [30] innovatively employed gradient boosting decision tree (GBDT) to explore the non-linear effects of built environment variables on subway ridership. Their results showed that built environment variables contributed to 34% of the ridership fluctuation with non-linear relationships and threshold effects.…”

Section: Impact Of the Built Environmentmentioning

confidence: 99%

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Analysing the relationship between weather, built environment, and public transport ridership

Lin

Weng

Brands

et al. 2020

IET intell. transp. syst

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For a sustainable public transport system, it is important to unveil the spatiotemporal characteristics of ridership and identify the influence mechanisms. Some studies analysed the effects of weather and built environment separately, however, their effects when incorporated remains to be determined. Using smart card data, weather information, and point of interest data from Beijing, the Light Gradient Boosted Machine was employed to investigate the relative importance of weather and built environment variables contributing to daily ridership at the traffic analysis zone level, and investigate the non-linear relationship and interaction effects between them. Weather conditions and built environment contribute 30.22 and 55.83% to ridership fluctuations, respectively. Most variables show complex non-linear and threshold effects on ridership. The interaction effects of weather and weekend/public holiday have a more substantial influence on ridership than weekdays, indicating weather conditions have less impact on regular commuting trips than discretionary trips. The ridership fluctuations in response to changing weather conditions vary with spatial locations. Adverse weather, such as strong wind, high humidity, or heavy rainfall, has a more disruptive impact on leisure-related areas than on residence and office areas. This study can benefit stakeholders in making decisions about optimising public transport networks and scheduling service frequency. 2 Literature review 2.1 Impact of weather conditions Many previous studies have addressed the effects of rain, temperature, and wind on public transport ridership. Generally, extreme weather conditions, such as very high and low temperatures, strong wind, and heavy precipitation, reduce public transport ridership [14-17]. However, the effect of weather

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Section: Results Analysis and Discussionsupporting

confidence: 80%

Section: Relative Importance Of Independent Variablessupporting

confidence: 85%

Section: Impact Of the Built Environmentmentioning

confidence: 99%

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Analysing the relationship between weather, built environment, and public transport ridership

Lin

Weng

Brands

et al. 2020

IET intell. transp. syst

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“…Parameter setting is one of the key steps in model training. The key parameters of the GBDT model are the number of iterations, the learning rate, and the complexity of the tree [63], which can effectively avoid over fitting phenomenon [14]. The number of iterations is also called the number of regression trees.…”

Section: Parameter Settingmentioning

confidence: 99%

“…The prediction effect will gradually decline with the extension of the prediction period [12], which limits the application of the model to a certain extent. Some recent studies have shown that nontime series deep learning methods [13], such as GBDT [14], [15], SVM, random forest [16] and neural network [17], [18], exhibit good forecasting effects in some forecasting fields, but the prediction of water accumulation processes is a continuous process that changes over time. These nontime series models can only predict a certain feature of the water accumulation process and cannot realize the prediction of the water accumulation process.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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Smart Governance Models to Optimise Urban Planning Under Uncertainty by Decision Trees

Garau

Desogus

Annunziata

et al. 2021

Lecture Notes in Computer Science

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How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

Cited by 209 publications

References 37 publications

Analysing the relationship between weather, built environment, and public transport ridership

Analysing the relationship between weather, built environment, and public transport ridership

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Smart Governance Models to Optimise Urban Planning Under Uncertainty by Decision Trees

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