Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships

Yiming Xu, Xiang Yan, Xinyu Liu and Xilei Zhao

Transportation Research Part A: Policy and Practice, 2021, vol. 144, issue C, 170-188

Abstract: Ridesharing is critical for promoting transportation sustainability. As a new form of ridesharing services, ridesplitting has rarely been studied. Based on the Chicago ridesourcing trip data, this study explores how ridesplitting adoption rate (i.e., the proportion of ridesourcing trips with ridesharing authorization) varies across space and what factors are associated with these variations. We find large variations in ridesplitting adoption rates across neighborhoods (Census Tracts) and across origin–destination (Census-Tract-to-Census-Tract) pairs. Particularly, the ridesplitting adoption rate is low for airport rides. We further apply a random forest model to explore which factors are key predictors of ridesplitting adoption rate across O-D pairs and to explore their nonlinear associations. The results suggest that the socioeconomic and demographic variables collectively contribute to 68.60% of the predictive power of the model, but travel-cost variables and built-environment-related factors are also important. The most important variables associated with ridesplitting adoption are ethnic composition, median household income, education level, trip distance, and neighborhood density. We further examine the nonlinear association between neighborhood ridesplitting adoption rate and several key variables such as the percentage of white population, median household income, and neighborhood Walk Score. The revealed nonlinear patterns can help transportation professionals identify neighborhoods with the greatest potential to promote ridesplitting.

Keywords: Ridesplitting; Random forest; Nonlinearity; Variable importance; Interpretable machine learning; Ridesharing (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (36)

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DOI: 10.1016/j.tra.2020.12.005

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