A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

Motorcycle-related crashes before and during the COVID-19 pandemic: A comparative retrospective observational study from the Middle East

BACKGROUND

During the COVID-19 pandemic, there was a boom in the delivery sector, with a significant increase in the demand and number of motorcycle delivery drivers in Qatar, which made them vulnerable to injury. We aimed to evaluate the incidence, pattern, and outcome of patients injured by motorcycle-related crashes (MCC) before and during the pandemic.

METHODS

A retrospective observational study included all adult patients admitted with motorcycle-related injuries before the pandemic (March 2018 to February 2020) and during the pandemic (March 2020 to March 2022). Comparative analyses were performed based on (work versus non-work related MCCs) and (pre- versus during the pandemic injuries).

RESULTS

510 patients with MCC were identified, of which 172 (33.7 %) were admitted in the pre-pandemic and 338 (66.3 %) during the pandemic. The mean age of patients was 29.2±7.8 years; 56 % were aged 20-29 years, and 99.4 % were males. Work-related MCCs were more frequent among the younger age group (60.9 % vs. 52.1 %; p=0.001) during the early evening, i.e., 6:00 to 9:00 p.m. (21.9 % vs. 13.9 %; p=0.004). However, non-work related MCC occurred more frequently between midnight and 3:00 am (20.2 % vs. 10.9 %; p=0.004), and such patients were more likely non-compliant for protective devices use (19.3 % vs. 6.1 %; p=0.001) and ride under the influence of alcohol (13.2 % vs. 7.4 %; p=0.03). During the pandemic, the proportion of alcohol consumers (13 % vs. 5.8 %; p=0.01) and work-related MCC (50.9 % vs. 22.7 %; p=0.001) increased significantly compared to the pre-pandemic period.

CONCLUSION

The overall burden of MCC increased during the pandemic, and the frequency of MCC involving commercial drivers surged significantly during the pandemic period as opposed to the non-work MCC, which predominated in the pre-pandemic period. Work-related MCCs were more frequent among younger age groups, mainly involving South Asians with frequent accidents in the evening time. However, recreation-related MCCs occurred more frequently at midnight, and victims were non-compliant with the protective gear. Furthermore, there is a need for prospective studies to examine the broader scope of risk factors that are associated with the work-related MCC, especially involving food deliveries, and for focused safety programs for motorcycle delivery drivers and recreational motorcyclists.

Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.