Analyzing driver injury severity in two-vehicle rear-end crashes considering leading-following configurations based on passenger car and light truck involvement.

Rear-end crash is a major type of traffic crashes leading to a large number of injuries and fatalities each year, and passenger cars and light trucks are two main vehicle types in rear-end crashes on US roadways. Passenger cars and light trucks are different in size, vehicle mass and driver's vision. It is necessary to investigate the driver injury outcome patterns in rear-end crashes between passenger cars and light trucks considering crash configurations regarding the leading and following vehicle types. This study employs latent class multinomial logit (MNL) model to examine the risk factors on driver injury severity along with heterogeneity in variable effects presented by the cluster pattern in two-vehicle rear-end crashes involving passenger cars and light trucks, considering four crash configuration types, i.e., a passenger car struck by a passenger car, a light truck struck by a light truck, a passenger car struck by a light truck, and a light truck struck by a passenger car as exploratory variables. A model with two latent classes, which indicates the heterogeneity in variable effects among all the observations, is found to best fit the 7-year crash dataset from Washington State. The pseudo-elasticities are calculated to quantify the marginal effects of the contributing factors. The risk factors curve and sloping road condition, driver without seatbelt, and driver age of 65 and above increase driver fatality and serious injury risk greatly, and these three factors contribute from different latent classes. The crash configuration of a passenger car struck by a light truck is found to be one of class characteristics factors, which indicates that the heterogeneity exists between these two vehicle types. This factor is also a risk factor of injury. Furthermore, the leading vehicle is found to be much more vulnerable and closely related to injury, especially when it is in the crash of a passenger car struck by a light truck. The latent classes discovered give theoretical evidence of how to appropriately select subset data for further model construction for practical interest of serious injury prevention. The risk factors and their influence on injury severity provide beneficial insights on developing relevant countermeasures and strategies for injury severity mitigation on rear-end crashes involving passenger cars and light trucks.

Exploring the impacts of travel-implied policy factors on COVID-19 spread within communities based on multi-source data interpretations.

The global Coronavirus Disease 2019 (COVID-19) pandemic has led to the implementation of social distancing measures such as work-from-home orders that have drastically changed people's travel-related behavior. As countries are easing up these measures and people are resuming their pre-pandemic activities, the second wave of COVID-19 is observed in many countries. This study proposes a Community Activity Score (CAS) based on inter-community traffic characteristics (in and out of community traffic volume and travel distance) to capture the current travel-related activity level compared to the pre-pandemic baseline and study its relationship with confirmed COVID-19 cases. Fourteen other travel-related factors belonging to five categories (Social Distancing Index, residents staying at home, travel frequency and distance, mobility trend, and out-of-county visitors) and three social distancing measures (stay-at-home order, face-covering order, and self-quarantine for out-of-county travels) are also considered to reflect the likelihood of exposure to the COVID-19. Considering that it usually takes days from exposure to confirming the infection, the exposure-to-confirm temporal delay between the time-varying travel-related factors and their impacts on the number of confirmed COVID-19 cases is considered in this study. Honolulu County in the State of Hawaii is used as a case study to evaluate the proposed CAS and other factors on confirmed COVID-19 cases with various temporal delays at a county-level. Negative Binomial models were chosen to study the impacts of travel-related factors and social distancing measures on COVID-19 cases. The case study results show that CAS and other factors are correlated with COVID-19 spread, and models that factor in the exposure-to-confirm temporal delay perform better in forecasting COVID-19 cases later. Policymakers can use the study's various findings and insights to evaluate the impacts of social distancing policies on travel and effectively allocate resources for the possible increase in confirmed COVID-19 cases.

Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models.

Due to limited visibility and low skid resistance on road surface, single-vehicle crashes under rain conditions, especially those occurred in rural areas, are more likely to result in driver incapacitating injuries and fatalities. A three-year crash dataset including all rural single-vehicle crashes under rain conditions from 2012 to 2014 in four South Central states, i.e., Texas, Arkansas, Oklahoma, and Louisiana, are selected in this paper to analyze the impact factors on driver injury severity. The mixed logit model (MLM) and the latent class model (LCM) are developed on the same dataset. Several parsimony indices, e.g., AIC and BIC, and as well as McFadden pseudo r-squared, are calculated for all the models to evaluate their respective performance. Results show that choosing the uniform distribution as the prior for random parameters could better improve the goodness-of-fit of the MLM than using normal and lognormal distributions. In addition, the two-class LCM also shows superiority when compared to three- and four-class LCMs. Finally, a careful comparison between these two models is conducted, and the results indicate that the LCM has a slightly better performance in analyzing the aforementioned dataset in this study. Model estimation results show that curve, on grade, signal control, multiple lanes, pickup, straight, drug/alcohol impaired, and seat belt not used can significantly increase the probability of incapacitating injuries and fatalities for drivers in the two models. On the other hand, wet, male, semi-trailer, and young can significantly decrease the probability of incapacitating injuries and fatalities for drivers. This study provides an insightful understanding of the effects of these attributes on rural single-vehicle crashes under rain conditions and beneficial references for developing effective countermeasures for severe injury prevention.