Evaluation of helmets impact on the braking reaction time of college student e-bike riders

Li Yuan; Lufeng Huang; Rui Cheng; Zihan Qin

doi:10.1117/12.3034881

7 August 2024 Evaluation of helmets impact on the braking reaction time of college student e-bike riders

Li Yuan, Lufeng Huang, Rui Cheng, Zihan Qin

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132241L (2024) https://doi.org/10.1117/12.3034881
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

In order to study the influence of different helmets on the braking reaction times (BRTs) of college student electric bicycle(e-bike) riders, a real-vehicle experiment is conducted to test the BRTs when riders wore different helmets in a closed road section. In addition to helmet type, riders’ physiological characteristics, riding workload, riding expectations and speed were also explored by ANOVA and correlation analyses, on the BRTs s of college student e-bike riders. The results showed that (1) wearing full and three-quarters half helmets significantly increase the BRTs of e-bike riders but half helmets do not significantly increase BRTs; (2) the faster the speed, the longer the BRTs for the e-bike riders; (3) driving e-bike every day of the week leads to a significant reduction in BRTs; (4) the higher the safety expectation the shorter the BRTs. A linear regression model for the BRTs of e-bike riders was established with the speed and dummy variables of whether to wear a full helmet, whether to wear a three-quarters half helmet, whether to drive an e-bike every day of the week, whether to drive an e-bike 2-1 days of the week, whether to have a very high expectation of safety, and whether to have a low expectation of safety as the independent variables. The goodness of fit of the model was 0.693, which is better predict the college student rider BRTs. The goodness of fit is commonly used as a goodness of fit indicator for multiple regression analysis, the larger the goodness of fit, the better the model is fitted. Finally, some suggestions for the types of helmets to be worn by riders with different perceptions have been put forward, and it also provides a theoretical basis for e-bike stopping sight distance and time to collision calculation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Yuan, Lufeng Huang, Rui Cheng, and Zihan Qin "Evaluation of helmets impact on the braking reaction time of college student e-bike riders", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132241L (7 August 2024); https://doi.org/10.1117/12.3034881

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KEYWORDS

Safety

Statistical analysis

Error analysis

Linear regression

Roads

Visibility

Statistical modeling

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