Flow-based unit is better: exploring factors affecting mid-term OD demand of station-based one-way electric carsharing

Cheng, Jiaqi; Chen, Xiaohong; Ye, Jianhong; Shan, Xiaonian

doi:10.1016/j.trd.2021.102954

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…The solution to traffic problems (traffic congestion, emissions, noise, parking, etc.) in China's crowded cities has been addressed by researchers (Chen & Kockelman, 2016;Cheng et al, 2021;Jian et al, 2020;Lee et al, 2014). The United States has consolidated its leadership in car rental (global reach, advanced service network, etc).…”

Section: Discussionmentioning

confidence: 99%

Bibliometric analysis of carsharing and car rental research in the field of urban transportation and tourism transportation

Akay

2024

Journal of Multidisciplinary Academic Tourism

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This paper reviews recent carsharing and car rental research bibliometrically. The study examines the evolution, structure, and boundaries of Web of Science-reviewed carsharing and car rental research. VOSviewer and SPSS 22 evaluated 204 vehicle rental and 574 carsharing articles in the WOS core collection. A gradual rise in car rental and carsharing studies is shown. China and the US produce the most carsharing and rental publications, respectively. China is the most productive country for carsharing publications and the United States for car rental publications. While China and the United States cooperate on carsharing, the United States cooperates with other countries (Canada, Germany, England, France, Australia, Portugal, Taiwan, Israel) on car rental. Co-occurrence network analysis shows that carsharing has five main themes: sharing economy, electric vehicles, transportation, shared mobility, and mobility as a service, while car rental research has four main themes: revenue management, transportation, quality service, and e-commerce. Carsharing and car rental studies share transportation themes. Carsharing subjects include transportation, engineering, business economics, environmental science ecology, science technology, and computer science, while car rental subjects include management, operations research, economics, transportation, business, transportation science technology, business finance, engineering, tourism, and environmental science. Car rental concerns vary by management, tourism, and finance. Tourism literature neglects car hire. This study thoroughly reviews 26 years of automobile rental and 22 years of carsharing literature. Thus, it can help academics comprehend automobile rental and carsharing studies and direct future research.

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Section: Discussionmentioning

confidence: 99%

Bibliometric analysis of carsharing and car rental research in the field of urban transportation and tourism transportation

Akay

2024

Journal of Multidisciplinary Academic Tourism

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“…Zhang et al (2021) analyze the specific routes that are undertaken by e-scooter using a recursive logit route choice model. Cheng et al (2021) analyze spatial factors on car sharing demand combining machine learning and generalized linear models and explanations based on SHAP values. Wang et al (2020a) apply a long short-term memory (LSTM) recurrent neural network in the temporal modeling of car sharing demand of different stations.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Schimohr

Doebler

Scheiner

2022

Geographical Analysis

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Regression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike‐sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to account for spatial autocorrelation. Independent variables included in the models consist of land use types, elements of the transport system and sociodemographic characteristics. Out of several regression models with different underlying distributions, a Tweedie generalized additive model is chosen by its values for AIC, RMSE, and sMAPE to be compared to an XGBoost model. To consider spatial relationships, spatial splines are included in the Tweedie model, while the estimations of the XGBoost model are modified using a geographically weighted regression. Both methods entail certain advantages: while XGBoost leads to far better values regarding RMSE and sMAPE and therefore to a better model fit, the Tweedie model allows an easier interpretation of the influence of the independent variables including spatial effects.

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Travel Intention with Shared Electric Vehicles Based on Theory of Multiple Motivations for Urban Governance

Bao

Miao

Chen

et al. 2023

J. Shanghai Jiaotong Univ. (Sci.)

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Flow-based unit is better: exploring factors affecting mid-term OD demand of station-based one-way electric carsharing

Cited by 10 publications

References 27 publications

Bibliometric analysis of carsharing and car rental research in the field of urban transportation and tourism transportation

Bibliometric analysis of carsharing and car rental research in the field of urban transportation and tourism transportation

Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Travel Intention with Shared Electric Vehicles Based on Theory of Multiple Motivations for Urban Governance

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