Utilizing Spatial Big Data platform in evaluating correlations between rental housing car sharing and public transportation

Choi, Junyoung; Yoon, Jeakak

doi:10.1007/s41324-017-0122-6

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…The spatial travel characteristics of users were analyzed, and results showed that the users' demand for network vehicle travel was obviously different between the morning peak and the evening peak of working days. Choi and Yoon analyzed the correlation between the supply of public transportation and the carsharing demand using the travel data of transportation cards and the trajectory data of carsharing in Seoul, South Korea [11]. Zohra et al studied the purchase intention and product adjustment strategy of users with high price sensitivity by paying attention to users' participation in consumption [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Carsharing Demand Based on Data Mining Methods

Wang

Sai

et al. 2021

Algorithms

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With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.

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Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Carsharing Demand Based on Data Mining Methods

Wang

Sai

et al. 2021

Algorithms

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Exploring the Potentials of Open-Source Big Data and Machine Learning in Shared Mobility Fleet Utilization Prediction

2023

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The urban transportation landscape has been rapidly growing and dynamically changing in recent years, supported by the advancement of information and communication technologies (ICT). One of the new mobility trends supported by ICT is shared mobility, which has a positive potential to reduce car use externalities. These systems’ recent and sudden introduction was not adequately planned for, and their rapidly growing popularity was not expected, which resulted in the urgent need for different stakeholders’ intervention to ensure efficient services’ integration within the urban transportation networks and to grant an effective system operation. Several challenges face shared mobility, including fleet size management, vehicle distribution, demand balancing, and the definition of equitable prices. In this research, we developed a practical, straightforward methodology that utilizes big open-source data and different machine learning (ML) algorithms to predict the daily shared-e-scooter fleet utilization (the daily number of trips per vehicle) that could be used to drive the system’s operation policies. We used four ML algorithms with different levels of complexity, namely; Linear Regression, Support Vector Regression, Gradient Boosting Machine, and Long Short-Term Memory Neural Network, to predict the fleet utilization in Louisville, Kentucky, using the knowledge the models get from the training data in Austin, Texas. The Gradient Boosting Machine (LightGBM) was the model with the best performance prediction based on the different evaluation measures. The most critical factors impacting daily fleet utilization prediction were temporal time series features, sociodemographics, meteorological data, and the built environment.

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Big data in public transportation: a review of sources and methods

Welch

Widita

2019

Transport Reviews

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Utilizing Spatial Big Data platform in evaluating correlations between rental housing car sharing and public transportation

Cited by 5 publications

References 10 publications

Analysis and Prediction of Carsharing Demand Based on Data Mining Methods

Analysis and Prediction of Carsharing Demand Based on Data Mining Methods

Exploring the Potentials of Open-Source Big Data and Machine Learning in Shared Mobility Fleet Utilization Prediction

Big data in public transportation: a review of sources and methods

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