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Like many countries in sub-Saharan Africa, Ghana has witnessed an increase in the use of motorcycles for both commercial transport and private transport of people and goods. The rapid rise in commercial motorcycle activities has been attributed to the problem of urban traffic congestion and the general lack of reliable and affordable public transport in rural areas. This study investigates and compares factors that are associated with motorcycle crash injury outcomes in rural and urban areas of Ghana. This comparison is particularly important because the commercial use of motorcycles and their rapid growth in urban areas are a new phenomenon, in contrast to rural areas where people have long relied on motorcycles for their transportation needs. Preliminary analysis of the crash data revealed that more of the rural area crashes occurred under dark and unlit roadway conditions, while urban areas recorded more intersection-related crashes. Additionally, it was found that more pedestrian collisions happened in urban areas, while head-on collisions happened more in rural areas. The model estimation results show that collisions with a pedestrian, run-off-road, and collisions that occur under dark and unlit roadway conditions were more likely to result in fatal injury. Findings from this study are expected to help in crafting and targeting appropriate countermeasures to effectively reduce the occurrence and severity of motorcycle crashes throughout the country and, indeed, sub-Saharan Africa.

Crashes involving three-wheeler motorized rickshaws (3-WMR) and motorcycles are becoming a rising public health and socioeconomic problem in developing countries. While earlier studies have investigated safety-related issues for two, and four-wheelers, there exists a notable research gap to understanding the factors that contribute to the severity of injuries in involving 3-WMR collisions with motorcyclists. The current study aims to fill this gap by investigating the risk factors contributing to injury severity in such crashes, employing a random parameters multinomial logit model with heterogeneity in means and variance. The study conducted an empirical analysis using traffic crash data spanning three years (2019–2021) from RESCUE 1122 in Rawalpindi, Pakistan. The model outcomes demonstrate that major injuries (severe and fatal) in 3-WMR and motorcycle collisions are affected by numerous factors, including road features, driver characteristics, temporal factors and environmental factors. The study provides beneficial findings, emphasizing the significance of accounting for unobserved heterogeneity in the variables contributing to injury severity in 3-WMR and motorcycle collisions. Based on the findings of the study, policy recommendations are provided to help safety practitioners build more effective strategies to address the safety concerns regarding 3-WMR crashes with motorcycles in Pakistan and other regions with similar safety issues.

Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.

Traffic crashes involving three-wheeled motorized rickshaws (3-WMRs) pose considerable public health risk in developing countries. Although prior research has thoroughly investigated the effects of risk factors such as weather, road conditions, and driver characteristics on 3-WMR collisions, the effect of the day of the week (weekday versus weekend) has been relatively underexplored. The study uniquely identifies the temporal heterogeneity in 3-WMR injury severity risk factors between weekday and weekend crashes. By integrating psychological and sociological theories, it also aims to understand how risk perception and sensation seeking behaviors contribute to various injury outcomes in 3-WMR crashes. The current study aims to fill this gap by investigating the risk factors contributing to injury severity in such crashes, employing a random parameters multinomial logit model with heterogeneity in means and variance. The study conducted an empirical analysis using traffic crash data spanning six years (2017–2022) from RESCUE 1122 in Rawalpindi, Pakistan. The findings identify several statistically significant factors that influence major injuries, including single-vehicle crashes, teenage drivers, rainy weather, peak traffic hours, wrong U-turns, speeding, and driver distraction. The study’s findings demonstrated the importance of considering temporal heterogeneity, thereby offering differentiated policy recommendations for weekends and weekdays, ensuring more tailored and effective safety interventions for 3-WMR users.

The COVID-19 travel restriction orders have significantly reduced travel and generally lowered the risk of road traffic collisions, but many accounts suggest an increase in risky driving behaviors and consequent fatal crashes during the shelter-in-place period. Risky driving behaviors including failure to wear a seatbelt, speeding, and drunk driving were observed to be the leading contributing factors of the fatalities. Whereas the fatal crashes that characterized the shelter-in-place period has become a topical issue, the high number of crashes that occurred as a result of the panic shopping and increased travel activities in the weeks before the shelter-in-place order have not received much attention. In this study, we investigated the differences and similarities in the effects of the factors that were associated with crash injury severity before, during, and after the shelter-in-place order. The study used crash data from the state of Alabama for the 2020 calendar year. Preliminary data analysis revealed interesting variations in crash trends across the three periods. It was found that the highest weekly crash frequency occurred in the immediate week before the shelter-in-place order, and a higher proportion of crashes that occurred between 6 p.m. and 6 a.m. and those that occurred in residential areas happened during the shelter-in-place period while shopping area crashes, manufacturing/industrial area crashes, rear-end collisions, and crashes involving female drivers occurred mostly before the shelter-in-place period. Three injury severity models were developed using random parameters logit with heterogeneity in means and variances approach. The results showed that major injury crashes occurred mainly in rural areas and occurred due to speeding, fatigue driving, and failure to use a seatbelt. The effects of these factors on crash outcome did not vary across the year, indicating that the shelter-in-place order did not impact the driving behaviors of the driver population that got into major injury crashes. The results further revealed that the effects of some crash factors, such as road type and manner of collision, varied across the periods. The findings of the study provide a deeper, data-driven understanding of how driving behaviors and associated crash outcomes may be affected by extreme events such as the COVID-19 shelter-in-place.

Annually, side impact crashes contribute to a significant proportion of road fatalities. These crashes typically occur as a result of traffic violations at intersections. This study contributes to efforts in addressing side impact crashes at unsignalized intersections by performing a path analysis to unravel some behavioral trajectories through which these crashes occur. The study further investigated how these behavioral pathways influence the severity of the crashes. Crashes that occurred at unsignalized four-way intersections and T-junctions in Alabama were used for model estimations. Three precrash actions, failed to yield right-of-way at the stop sign, failed to yield right-of-way at a turn, and running stop sign, were considered. The model estimation results reveal that some of the crash factors were more associated with certain precrash factors but not others at either four-way intersections or T-junctions or both. It was observed that side impact crashes that occurred under daylight conditions at four-way intersections, for instance, were less likely to involve running a stop sign but more likely to involve failure to yield at the stop sign and failure to yield right-of-way at a turn, but under dark and unlit roadway conditions, the at-fault drivers were more likely to run a stop sign or fail to yield at a stop sign but less likely to be involved in failure to yield right-of-way at a turn. This approach to injury severity analysis uncovers complex underlying relationships between precrash actions, other contributing factors, and crash outcomes.

The rise in population density, vehicle ownership, and urban development has significantly influenced travel demand and altered travel patterns, leading to traffic congestion in rapidly growing urban centers such as Accra, Ghana. The traditional approach of expanding roadways to accommodate rising traffic is no longer environmentally sustainable. Instead, the emphasis has shifted toward travel demand management (TDM) strategies as a more sustainable solution. This study aimed to investigate a range of TDM strategies that can be adopted in Accra to improve traffic flow through the lenses of everyday road users. The study employed a questionnaire survey and a stratified sampling technique to gather data from 615 respondents for relative importance index (RII) ranking and Chi-square statistical analysis. The findings revealed that the topmost preferred strategies were mass transit improvements, walking and cycling improvements, and alternative work schedules. Notably, mass transit improvements emerged as the most preferred strategy. Conversely, strategies like efficient parking pricing, congestion pricing, and higher fuel and road taxes for private vehicles ranked lowest, garnering minimal preference. The study also revealed a statistically significant correlation between variables such as age, education level, marital status, income level, and mode of transportation and all the selected TDM preferences. However, no significant relationship was found between gender or car ownership and any of the selected TDM preferences. The study provides valuable insights into road users’ preferences for TDM strategies that can aid in planning future urban mobility systems to proactively manage travel demand, alleviate congestion, and promote sustainable transportation options for the city’s residents.

Drivers with physical and/or mental impairments face many driving challenges. However, not many studies have been carried out to understand the factors that contribute to crashes involving these drivers and how these factors influence their crash outcomes. This study aims to address this gap in the road safety literature. The study uses historical crash data from the State of Alabama for at-fault physically impaired drivers and utilizes a random parameter with heterogeneity in a mean modeling approach to account for unobserved heterogeneity. The model estimation results reveal that in rural areas, driving over the speed limit, the time of crash being between 6.00 p.m. and 11.59 p.m., younger drivers, employed and distracted drivers were associated with severe injuries. Minor injury crashes are found to be associated with female drivers, state roads and residential areas. Finally, property-damage-only crashes are more associated with weekdays, driving under the influence of alcohol or drugs, a road with left curvature, driving too fast for the road conditions and intersections. The results obtained provide a foundation for the adoption of targeted countermeasures to improve highway safety for physically impaired drivers and all road users in general.

Although the school bus is considered a safe form of transportation, school bus crashes are a major safety concern. School bus crashes are a result of driver error of either the at-fault school bus or another at-fault vehicle (where the school bus is not at fault). To examine the risk factors associated with school bus related crashes and crash outcomes, this study segments and develops two binary logit models for each school bus crash type. A total of 1702 school bus related crashes recorded between 2009 and 2016 were used to estimate the model. According to the model results, sideswipe collisions were less likely to result in injury outcomes for at-fault school buses than rear-end and side-impact collisions. Speeding, driving impaired or under the influence, and negotiating a curve all have significant positive associations with injury outcomes in not-at-fault school bus crashes. This study’s practical implications include enforcing the school bus safe driving guidelines, training programs for school bus drivers that include elements of nonroutine trips, training for crossing guards, and awareness programs for drivers of other vehicles to instill safe driving practices around school buses.

Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types.