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Research has consistently demonstrated that seatbelt use is critically important in reducing the likelihood of fatal and serious injuries resulting from traffic crashes. However, after years of nationwide increases in seatbelt use, these rates have largely plateaued, motivating the need for research to better understand those circumstances under which seatbelt use remains relatively low. At an aggregate level, research has shown that occupants in the same vehicle tend to exhibit correlation in seatbelt use or non-use. This suggests that social dynamics may play a role in occupants’ decisions as to whether or not to wear a seatbelt. To that end, this study examines trends in seatbelt use among pairs of drivers and front-seat passengers using data from direct observation roadside surveys. Bivariate probit models are estimated to examine the relationship between seatbelt use and various demographic, vehicle, and site-specific factors. The bivariate framework is also able to account for correlation among important unobserved factors associated with seatbelt use. The results show significantly better fit as compared with independent univariate probit models. The results also suggest both direct and indirect relationships between seatbelt use and various demographic, vehicle, and site characteristics. Seatbelt use rates are found to vary based on occupants’ age, gender, and race. Furthermore, seatbelt use by both the driver and front-seat passenger is also shown to vary based on the other occupant’s age. Heterogeneity is also shown across various geographic regions and roadway functional classes.

Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

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