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Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression

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  • Yanyong Guo

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Si Pai Lou No. 2, Nanjing 210096, China
    Department of Civil Engineering, The University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada)

  • Yao Wu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Si Pai Lou No. 2, Nanjing 210096, China)

  • Jian Lu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Si Pai Lou No. 2, Nanjing 210096, China)

  • Jibiao Zhou

    (School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China)

Abstract

Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

Suggested Citation

  • Yanyong Guo & Yao Wu & Jian Lu & Jibiao Zhou, 2019. "Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression," Sustainability, MDPI, vol. 11(7), pages 1-12, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2071-:d:220725
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    References listed on IDEAS

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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Zhanguo Song & Yanyong Guo & Yao Wu & Jing Ma, 2019. "Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-19, June.
    2. Changxi Ma & Dong Yang & Jibiao Zhou & Zhongxiang Feng & Quan Yuan, 2019. "Risk Riding Behaviors of Urban E-Bikes: A Literature Review," IJERPH, MDPI, vol. 16(13), pages 1-18, June.
    3. Jibiao Zhou & Tao Zheng & Sheng Dong & Xinhua Mao & Changxi Ma, 2022. "Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China," IJERPH, MDPI, vol. 19(5), pages 1-21, February.
    4. Chuanyun Fu & Hua Liu, 2020. "Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    5. Changxi Ma & Jibiao Zhou & Dong Yang & Yuanyuan Fan, 2020. "Research on the Relationship between the Individual Characteristics of Electric Bike Riders and Illegal Speeding Behavior: A Questionnaire-Based Study," Sustainability, MDPI, vol. 12(3), pages 1-12, January.
    6. Jibiao Zhou & Xinhua Mao & Yiting Wang & Minjie Zhang & Sheng Dong, 2019. "Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China," IJERPH, MDPI, vol. 16(16), pages 1-28, August.
    7. Zhenggan Cai & Fulu Wei & Zhenyu Wang & Yongqing Guo & Long Chen & Xin Li, 2021. "Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation," Sustainability, MDPI, vol. 13(13), pages 1-24, July.

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