No abstract

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

A truck platoon is a set of virtually linked trucks that travel in tandem with small intervehicle distances. Several studies have proved that traveling in platoons can significantly improve fuel economy due to the reduced aerodynamic drag. However, most literature only provides scattered pieces of information regarding fuel economy in truck platoons. Therefore, a literature survey is needed to understand what has been studied and what problems remain to be further addressed. This paper presents an overview of existing studies to illustrate the state of the art about fuel savings for truck platooning. Specifically, it summarized the methodologies, the contributing factors of fuel consumption, the coordination methods to improve the platooning rate, and the look-ahead control strategies to generate fuel-efficient speed profiles for each vehicle driving in a platoon over different road grades. After that, the autonomous truck platooning was introduced, and we raised and discussed a couple of outstanding questions to be addressed in future work.

No abstract

No abstract

Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

No abstract

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling.