Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

Tu, Meiting; Li, Wenxiang; Orfila, Olivier; Li, Ye; Gruyer, Dominique

doi:10.1016/j.trd.2021.102776

Cited by 78 publications

(42 citation statements)

References 32 publications

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“…This study used the “xgboost” package in Python to help develop the entire model [ 55 , 78 , 80 ]. We provided the relative importance ranking of all predicting variables to identify the significant correlates.…”

Section: Resultsmentioning

confidence: 99%

“…Favorable public policy can promote the development of public transport [ 88 ]. The above-mentioned findings could facilitate the understandings on how the built and social environment variables affect bus use among older adults [ 55 ]. It is informative for policymakers and planners to adopt reasonable and targeted interventions to improve bus use among older adults.…”

Section: Discussionmentioning

confidence: 99%

“…The “urban vitality” there can be understood as the movement of people across urban spaces and is measured by the Baidu Heat Index [ 28 ].They found that the associations change remarkably beyond some thresholds of the built environment and that there exist non-linear synergies [ 28 ]. Tu et al [ 55 ] used GBDT to study the non-linear effects and the thresholds of the built environment on ride-splitting based on partial dependence plots in the context of Chengdu. By combining with practical examples, Ding, Cao, and Naess [ 26 ] expounded the advantages of GBDT more vividly and concretely.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model

Wang

Zhao

Liu

et al. 2021

IJERPH

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Global aging has raised increasing concerns on the health and well-being of older adults. Public transport is a viable option to improve the mobility and quality of life among older adults. However, policies that promote the public transport use among older adults are rare. This study utilizes the eXtreme Gradient Boosting (XGBoost) decision tree to explore the non-linear associations of the built and social environment with bus use among older adults in China. The bus use of older adults was obtained from the Zhongshan Household Travel Survey (ZHTS) in 2012. Results show that non-linear relationships exist among all built environment and social environment characteristics. Within certain thresholds, the percentage of green space land use, land use mixture, bus-stop density, and dwelling unit density are positively related to bus use among older adults. Likewise, one social environment variable, the proportion of older adults in a neighborhood, is the key social environment variable. Furthermore, the dwelling unit density and proportion of older adults appear to have an inverse U-shaped relationship. Additionally, age, ownership of motorcycles, and distance from home to the nearest bus stop also show non-linearity. The findings presented in this paper facilitate effective planning interventions to promote bus use among older adults.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model

Wang

Zhao

Liu

et al. 2021

IJERPH

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“…Based on real trip data provided by the Hangzhou Didi platform, an ensemble learning approach was presented to predict users' ridesplitting choices, and the ReliefF algorithm was used to rank and select a variety of features that may impact ridesplitting behaviour [13]. Additionally, the studies byLi et al [11], Tu et al [14] and Tu et al [35] were all based on Chengdu ridesourcing data provided by DiDi Chuxing. Li et al [11] developed a ridesplitting trip identification algorithm and studied the characteristics of ridesplitting trips in Chengdu.…”

Section: Existing Studies On Ridesplittingmentioning

confidence: 99%

“…Wang et al [11] and Li et al [12] analysed the characteristics of ridesplitting. Chen et al [13] quantified the impact of ridesplitting on multi-modal urban mobility, and Chen et al [14] presented an ensemble learning approach to predicting users' ridesplitting choices. Xu et al [6] and Tu et al [15] explored the influencing factors affecting ridesplitting.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Factors of Intercity Ridesplitting Based on Observed and GIS Data: A Case Study in China

Jin-cheng

Chen

et al. 2021

IJGI

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Ridesplitting, a form of ridesourcing in which riders with similar origins and destinations are matched, is an effective mode of sustainable transportation. In recently years, ridesplitting has spread rapidly worldwide and plays an increasingly important role in intercity travel. However, intercity ridesplitting has rarely been studied. In this paper, we use observe intercity ridesplitting data between Yinchuan and Shizuishan in China and building environment data based on a geographic information system (GIS) to analyse temporal, spatial and other characteristics. Then, we divide the study area into grids and explore the contributing factors that affect the intercity ridesplitting matching success rate. Based on these significant factors, we develop a binary logistic regression (BLR) model and predict the intercity ridesplitting matching success rate. The results indicate that morning peak, evening peak, weekends and weekdays, precipitation and snowfall, population density, some types of points of interest (POI), travel time and the advance appointment time are significant factors. In addition, the prediction accuracy of the model is more than 78%, which shows that the factors studied in this paper have good explanatory power. The results of this study can help in understanding the characteristics of intercity ridesplitting and provide a reference for improving the intercity ridesplitting matching success rate.

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Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis

Wang

Weng

et al. 2022

Transportation

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Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

Cited by 78 publications

References 32 publications

Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model

Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model

Exploring the Factors of Intercity Ridesplitting Based on Observed and GIS Data: A Case Study in China

Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis

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