Although the enforcement of seatbelt use is considered to be an effective strategy in reducing road injuries and fatalities, lack of seatbelt use still accounts for a substantial proportion of fatal crashes in Tennessee, United States. This problem has raised the need to better understand factors influencing seatbelt use. These factors may arise from spatial/temporal characteristics of a driving location, type of vehicle, demographic and socioeconomic attributes of the vehicle occupants, driver behaviours, attitudes, and social norms. However, the above factors may not have the same effects on seatbelt use across different individuals. In addition, the behavioural factors are usually difficult to measure and may not always be readily available. Meanwhile, residential locations of vehicle occupants have been shown to be associated with their behavioural patterns and thus may serve as a proxy for behavioural factors. However, the suitability of geographic and residential locations of vehicle occupants to understand the seatbelt use behaviour is not known to date. This study aims to fill the above gaps by incorporating the residential location characteristics of vehicle occupants in addition to their demographics and crash characteristics into their seatbelt use while accounting for the varying effects of these factors on individual seatbelt use choices. To achieve this goal, empirical data are collected for vehicular crashes in Tennessee, United States, and the home addresses of vehicle occupants at the time of the crash are geocoded and linked with the census tract information. The resulting data is then used as explanatory variables in a latent class binary logit model to investigate the determinants of vehicle occupants’ seatbelt use at the time of the crash. The latent class specification is employed to capture the unobserved heterogeneity in data. Results show that Tennessean drivers belong to two general categories—conformist and eccentric—with gender, vehicle type, and income per capita determining the likelihood of these categories. Overall, male drivers, younger drivers, and drivers who have consumed drugs are less likely to wear a seatbelt, whereas drivers who come from areas with higher population density, travel time, and income per capita are more likely to wear a seatbelt. In addition, driving during the day and in rainy weather are associated with an increased likelihood of seatbelt use. The findings of this study will help developing effective policies to increase seatbelt use rate and improve safety.@en

Despite the proven effectiveness of seatbelt use in reducing traffic casualties, not wearing a seatbelt still contributes to a substantial proportion of fatal crashes worldwide. This problem has raised the need to better understand factors contributing to seatbelt use, particularly in multi-occupant vehicles. Among these factors, behavioural determinants of seatbelt use are difficult to measure and their data are not readily available. These behavioural factors may have shared influences on vehicle occupants, causing their seatbelt use choices to be correlated. These complexities have prevented a comprehensive understanding of seatbelt use choices in the literature. This study aims to fill this gap by developing an econometric model that explains seatbelt use choices in multi-occupant vehicles. A set of binary logit models are constructed for seatbelt use choices and their utilities are correlated across vehicle occupants. A new latent variable representing the unobserved factors or ‘atmosphere’ of the vehicle is then incorporated into the model. The model is empirically tested using seatbelt use data from Tennessee, United States. Results indicate that vehicle body type and time of the day are significantly associated with seatbelt use. In addition, the collective seatbelt use in a vehicle is influenced by the unobserved atmosphere in the vehicle. Age, alcohol and drug consumption, higher proportion of old population and white racial mix, higher income per capita, and higher education levels are factors contributing to this latent atmosphere.@en