Evaluation of bicyclist physiological response and visual attention in commercial vehicle loading zones.

INTRODUCTION: With growing freight operations throughout the world, there is a push for transportation systems to accommodate trucks during loading and unloading operations. Currently, many urban locations do not provide loading and unloading zones, which results in trucks parking in places that obstruct bicyclist's roadway infrastructure (e.g., bicycle lanes). METHOD: To understand the implications of these truck operations, a bicycle simulation experiment was designed to evaluate the impact of commercial vehicle loading and unloading activities on safe and efficient bicycle operations in a shared urban roadway environment. A fully counterbalanced, partially randomized, factorial design was chosen to explore three independent variables: commercial vehicle loading zone (CVLZ) sizes with three levels (i.e., no CVLZ, Min CVLZ, and Max CVLZ), courier position with three levels (i.e., no courier, behind the truck, beside the truck), and with and without loading accessories. Bicyclist's physiological response and eye tracking were used as performance measures. Data were obtained from 48 participants, resulting in 864 observations in 18 experimental scenarios using linear mixed-effects models (LMM). RESULTS: Results from the LMMs suggest that loading zone size and courier position had the greatest effect on bicyclist's physiological responses. Bicyclists had approximately two peaks-per-minute higher when riding in the condition that included no CVLZ and courier on the side compared to the base conditions (i.e., Max CVLZ and no courier). Additionally, when the courier was beside the truck, bicyclist's eye fixation durations (sec) were one (s) greater than when the courier was located behind the truck, indicating that bicyclists were more alert as they passed by the courier. The presence of accessories had the lowest influence on both bicyclists' physiological response and eye tracking measures. PRACTICAL APPLICATIONS: These findings could support better roadway and CVLZ design guidelines, which will allow our urban street system to operate more efficiently, safely, and reliable for all users.

Categorizing datasets of road traffic accidents in Oman spanning from 2012 to 2022.

Road traffic accidents constitute the primary cause of fatalities associated with injuries and engender substantial economic ramifications for affected individuals, their families, and entire nations. The Sultanate of Oman, like other countries, suffers from traffic accident injuries and traffic congestion. The accident rate for the period 2021 was recorded as one accident every six hours. Despite a 70% increase in total number of vehicles and an 81% rise in licensed drivers between 2012 and 2019, data on traffic accidents demonstrate an improving trend with a notable 55% decline in crash fatalities. However, it is important to recognize that road traffic accidents in Oman encompass not only social issues but also pose a significant economic burden, resulting in substantial financial costs for the nation. Notwithstanding, it was discovered that more than 50% of fatal crashes in Oman were primarily caused by excessive speeding. The main goal of this research is to analysis the causes and trends of traffic accidents at the national level in the Sultanate of Oman. Data analysis reveals speed as the primary cause of traffic accidents in Oman, with Muscat and Dhofar registering the highest accident rates. In addition, the distribution of deaths and injuries resulting from accidents varies according to Gender and nationality. According to the road accident scenario analysis in the state, more traffic accidents occurred in males than females. Traffic accidents have witnessed a notable decline over the past decade, attributable to the diligent efforts and interventions implemented by the Royal Oman Police.

Examining Impacts of COVID-19-Related Stay-At-Home Orders through a Two-Way Random Effects Model.

The COVID-19 pandemic has had far-reaching impacts on public health and safety, economics, and the transportation system. To reduce the spread of this disease, federal and local governments around the world have introduced stay-at-home orders and other restrictions on travel to "non-essential" businesses to implement social distancing. Preliminary evidence suggests substantial variability in the impacts of these orders in the United States, both across states and over time. This study examines this issue using daily county-level vehicle miles traveled (VMT) data for the 48 continental U.S. states and the District of Columbia. A two-way random effects model is estimated to assess changes in VMT from March 1 to June 30, 2020 as compared with baseline January travel levels. The implementation of stay-at-home orders was associated with a 56.4 percent reduction in VMT on average. However, this effect was shown to dissipate over time, which may be attributable to "quarantine fatigue." In the absence of full shelter-in-place orders, travel was also reduced where restrictions on select businesses were introduced. For example, restrictions on entertainment, indoor dining, and indoor recreational activities corresponded to reductions in VMT of 3 to 4 percent while restrictions on retail and personal care facilities showed 13 percent lower traffic levels. VMT was also shown to vary based on the number of COVID case reports, as well as with respect to other characteristics, including median household income, political leanings, and how rural the county was in nature.

How do the type and duration of distraction affect speed selection and crash risk? An evaluation using naturalistic driving data.

Distracted driving is among the leading causes of roadway crashes worldwide. However, due to limitations of police-reported crash data, it is often challenging to understand the nature and magnitude of this problem. Distraction has also been shown to affect driver speed selection, which is important as both mean speed and speed variance have substantive impacts on crash risk. This study utilizes naturalistic driving data to investigate the relationship between the engagement in various secondary (non-driving) tasks and driver speed selection under different driving contexts. Separate analyses were conducted for low-speed and high-speed driving environments. Two-way random effects linear regression models were estimated for both speed regimes, while controlling for driver, roadway, and traffic characteristics. The differences were assessed based upon ten types of secondary tasks. In general, engagement in all tasks was found to decrease speeds in high-speed environments while the effects were mixed in low-speed settings. The changes in speeds were much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, an average episode of a driver talking on a handheld cellphone was associated with a 6-mph speed reduction in high-speed environments, but a 3.5-mph increase in low-speed settings. In addition to examining impacts on speed selection, the risk of involvement in crash and near-crash events was also evaluated in consideration of the type and duration of distraction. Interestingly, distractions tended to show similar relationships, in both direction and magnitude, with the risk of involvement in both crash and near-crash events. From a policy standpoint, this study provides further motivation for legislation and other programs aimed at curbing distracted driving.