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Micro-Mobility Sharing System Accident Case Analysis by Statistical Machine Learning Algorithms

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  • Hakan İnaç

    (Directorate for Strategy Development, Head of Investment Management & Control Department, Ministry of Transport and Infrastructure, Ankara 06338, Turkey)

Abstract

This study aims to analyze the variables that affect the accidents experienced by e-scooter users and to estimate the probability of an accident during travel with an e-scooter vehicle. The data of e-scooter drivers, offered for use via rental application in 15 different cities of Turkey, were run in this study. The methodology of this study consists of testing the effects of the input parameters with the statistical analysis of the data, estimating the probability of an e-scooter accident with machine learning, and calculating the optimum values of the input parameters to minimize e-scooter accidents. By running SVM, RF, AB, k NN, and NN algorithms, four statuses (completed, injured, material damage, and nonapplicable) likely to be encountered by shared e-scooter drivers during the journey are estimated in this study. The F1 score values of the SVM, RF, k NN, AB, and NN algorithms were calculated as 0.821, 0.907, 0.839, 0.928, and 0.821, respectively. The AB algorithm showed the best performance with high accuracy. In addition, the highest consistency ratio in the ML algorithms belongs to the AB algorithm, which has a mean value of 0.930 and a standard deviation value of 0.178. As a result, the rental experience, distance, driving time, and driving speed for a female driver were calculated as 100, 10.44 km, 48.33 min, and 13.38 km/h, respectively, so that shared e-scooter drivers can complete their journey without any problems. The optimum values of the independent variables of the rental experience, distance, driving time, and driving speed for male drivers were computed as 120, 11.49 km, 52.20 min, and 17.28 km/h, respectively. Finally, this study generally provides a guide to authorized institutions so that customers who use shared and rentable micro-mobility e-scooter vehicles do not have problems during the travel process.

Suggested Citation

  • Hakan İnaç, 2023. "Micro-Mobility Sharing System Accident Case Analysis by Statistical Machine Learning Algorithms," Sustainability, MDPI, vol. 15(3), pages 1-31, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2097-:d:1044047
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    References listed on IDEAS

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    1. Margherita Pazzini & Leonardo Cameli & Claudio Lantieri & Valeria Vignali & Giulio Dondi & Thomas Jonsson, 2022. "New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim," IJERPH, MDPI, vol. 19(12), pages 1-17, June.
    2. Button, Kenneth & Frye, Hailey & Reaves, David, 2020. "Economic regulation and E-scooter networks in the USA," Research in Transportation Economics, Elsevier, vol. 84(C).
    3. Abdulkadir Atalan, 2023. "Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 214-241, January.
    4. Sujae Kim & Sangho Choo & Gyeongjae Lee & Sanghun Kim, 2022. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method," Sustainability, MDPI, vol. 14(5), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

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