Use of multiple indicators multiple causes (MIMIC) method to investigate quantitative inference in socioeconomic determinants on motorcyclist stress

Research has shown that driving-related stress plays a significant role in causing traffic accidents, either directly or indirectly. Motorcyclists often engage in risky driving behaviors due to elevated stress levels. This study investigates the influence of socioeconomic factors on driving stress among motorcyclists. Data were gathered from 50 participants, with heart rate (HR) recorded using the Polar Vantage V2 device. Heart rate variability (HRV) was analyzed in both time and frequency domains using Kubios HRV software. The study employed the Multiple Indicators Multiple Causes (MIMIC) model to explore the associations between socioeconomic factors and driving stress. The results indicate that variables such as age, gender, education level, occupation, income, driving experience, and travel purpose significantly affect stress levels across both HRV domains. These findings highlight the importance of addressing motorcyclist stress through targeted interventions, including educational programs and policy measures that regulate driving duration. Such strategies are particularly vital in developing countries to reduce stress and improve road safety. This research provides a foundation for developing practical solutions aimed at minimizing driving stress and enhancing the well-being of motorcyclists in high-risk environments.•A MIMIC model was applied to analyze the relationship between stress variables in the time and frequency domains based on HRV data.•The model identified significant causal relationships, emphasizing the pivotal role of socioeconomic factors in influencing motorcyclists' driving stress.•The model demonstrated strong statistical performance with key indicators: chi-square = 38.749, GFI = 0.958, CFI = 0.982, AGFI = 0.893, TLI = 0.961, and RMSEA = 0.057, confirming its robustness and reliability.