Injury-severity analysis of intercity bus crashes in Ghana: A random parameters multinomial logit with heterogeneity in means and variances approach.

Travel by bus is an efficient, cost-effective, safe and preferred means of intercity transport in many advanced countries. On the contrary, there is huge public sentiment about the safety records of intercity buses in low- and middle-income countries given the increasing bus-involved road traffic crashes and high fatality rates. This study sought to model the injury severity of intercity bus transport in Ghana using the random parameters multinomial logit with heterogeneity in means and variances modelling technique to account for unobserved heterogeneity in the dataset.

A comparative analysis of freeway crash incident clearance time using random parameter and latent class hazard-based duration model.

The effects of freeway incident clearance times on the flow of traffic have recently increased interests in understanding what factors influence incident durations. This has particularly become topical due to the financial and economic implications of traffic gridlocks caused by freeway incidents on industries and personal mobility. This paper presents two advanced econometric modeling methods, random parameters duration modeling and latent class duration modeling in understanding the factors that impact freeway incident clearance times in the State of Alabama.

Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities.

Introduction: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes.

Method: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including "bicyclist failed to yield," "motorist failed to yield," "bicyclist overtaking motorist," and "motorist overtaking bicyclist.

Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China.

The novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province.

COVID-19 effects on shared-biking in New York, Boston, and Chicago.

Coronavirus has had a large-scale impact on transportation. This study attempts to assess the effects of COVID-19 on biking. Bikeshare data was used to understand the impacts of COVID-19 during the initial wave of the disease on biking in New York City, Boston, and Chicago.

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways.

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders.

Analytical approximation for macroscopic fundamental diagram of urban corridor with mixed human and connected and autonomous traffic.

Advances in connected and autonomous vehicles have the promise to reshape the future of the transportation system. How and when the benefits associated with automation and connectivity technology will start to impact the performance of an urban corridor is an issue of interest for traffic operators. This paper proposes an analytical capacity model for urban corridors with mixed traffic based on the concept of macroscopic fundamental diagram.

A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania.

Motorcyclists face higher risks of severe injuries in crashes compared to motor vehicle drivers who are often protected by seatbelts and airbags during collisions. A report by the National Highway Traffic Safety Administration reveals that motorcyclists have 27 times the risk of fatality in traffic crashes as much as motor vehicle drivers. Previous studies have identified a list of risk factors associated with motorcyclist injury severity and generated valuable insights for countermeasures to protect motorcyclists in crashes.

Factors associated with driver injury severity of lane changing crashes involving younger and older drivers.

Lane change maneuvers can be a difficult driving task for many drivers, particularly, younger and inexperienced drivers and older drivers. Frequent and improper lane changing has a substantial effect on both traffic operations and safety. This study investigates the factors that contribute to the injury severity of crashes resulting from improper lane change maneuvers involving younger and older drivers in Alabama.

An integrated spatio-temporal approach to examine the consequences of driving under the influence (DUI) in crashes.

Driving under the influence (DUI) is illegal in the United States because a driver's mental and motor skills can be seriously impaired by alcohol or drugs. Consequently, DUI violators' involvement in severe crashes is high. Motivated by the spatial and temporal nature of traffic crashes, this study introduces an integrated spatio-temporal approach to analyzing highway safety data.

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates.

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes.

Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes.

Do NHTSA vehicle safety ratings affect side impact crash outcomes?

Introduction: Side impact crash injuries tend to be severe, mainly due to the effects of the mechanism of such crashes. This study addresses the relationship between side impact crash injury severities and side impact safety ratings of the passenger cars involved in such crashes. It is motivated by the lack of research on side impact safety ratings in relation to the real-world crash outcomes.

An analysis of risk factors associated with road crash severities in Namibia.

Road crash is a leading cause of death and disabilities in Namibia and other developing countries. Based on recent trends, the World Health Organization indicated that progress to realize Sustainable Development Goal (SDG) target 3.6 - which calls for a 50% reduction in the number of road traffic deaths by 2020 - remains far from sufficient.

Injury Severity and Contributing Driver Actions in Passenger Vehicle-Truck Collisions.

Large-scale truck-involved crashes attract great attention due to their increasingly severe injuries. The majority of those crashes are passenger vehicle-truck collisions. This study intends to investigate the critical relationship between truck/passenger vehicle driver's intentional or unintentional actions and the associated injury severity in passenger vehicle-truck crashes.

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach.

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes.

Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes.

This paper examines and compares the effect of selected variables on driver injury severity of, both, at-fault and not at-fault drivers. Data from the Highway Safety Information System (HSIS) for the state of North Carolina was used for analysis and modeling. A partial proportional odds model was developed to examine the effect of each variable on injury severity of at-fault driver and not at-fault driver, and, to examine how each variable affects these two drivers' injury severity differently.

Correlates of front-seat passengers' non-use of seatbelts at night.

When properly worn, seatbelts can save lives. They are designed to prevent occupants from hitting objects inside their vehicle and from being ejected out of their vehicle in the event of a crash. Despite their proven effectiveness in reducing the severity of injuries, seatbelt non-use among passengers still remains a problem, especially at night.

Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities.

This paper investigates factors that influence the severity of single-vehicle crashes that happen on weekdays and weekends. Crash data from 2012 to 2016 for the State of Alabama was used for this study. Latent class logit models were developed as alternative to the frequently used random parameters models to account for unobserved heterogeneity across crash-severity observations.