Investigating the effects of travel lane configuration and lane width on traffic safety where powered-two-wheelers (PTWs) share with larger vehicles: A micro perspective.

The design of travel lane configuration and lane width is crucial to traffic safety, especially in an urban mixed traffic environment where Powered-Two-Wheelers (PTWs) are prevalent and share the same roads with larger vehicles such as cars, buses, and trucks. However, there have been limited studies on the effects of the design of travel lane configuration and lane width on safety in such a mixed traffic environment. It's true the above-mentioned research question can be evaluated simply in terms of the number of crashes. However, doing so not only requires a few years of crash and traffic data, but limited insight can be gained in terms of how driver and rider behaviours are affected, and this has implications for further improvement in road safety. This study examines the changes in driving/riding behaviours and surrogate events before and after the adjustments of travel lane configuration and lane width by proposing a micro perspective approach as a complement to conventional studies. A before-and-after site-based investigation was conducted at two study sites which had opposite adjustments for travel lane configuration and lane widths: at one site the number of lanes was reduced, thereby widening the lane width in the outer lane on one road section, and at the second site the number of lanes was increased, thereby narrowing lane width in the outer lane on the other road section. The results showed that an increase in lane width resulted in a considerable increase in the number of speeding events as well as unsafe driving/riding behaviours and surrogate events related to lane splitting, lane sharing, and overtaking.

Exploring the combined effects of driving situations on freeway rear-end crash risk using naturalistic driving study data.

The causes and the crash-generating processes of freeway rear-end (FRE) crashes are complicated. Previous studies have highlighted the many contributing factors to crash occurrences on freeways, such as traffic flow conditions, driver-following behavior, driver attention allocation, driver characteristics, the driving environment, and drivers' interactions with surrounding vehicles, etc. Nevertheless, few studies have looked into the combined effects of these factors on FRE crash risk as a whole. This study focuses on characterizing the sequential crash generating process of the interactions between traffic flow conditions, roadway attributes, driver behavior, event attributes, and precipitating events in FRE crashes. A sequential modeling framework for modeling the sequential and combined effects on FRE crash risk was constructed by applying structural equation modeling (SEM). The Second Highway Strategic Research Program (SHRP2) Naturalistic Driving Study (NDS) data was utilized for this purpose as this data provides extensive information concerning what happened before crashes and near-crashes. A total of 17 and 433 FRE crashes and near-crashes, respectively, were included in this study. It was found that (1) FRE crashes were associated with the sequential and combined effects of those factors above; (2) certain types of speed oscillations were identified as precursors to sudden braking when vehicles ahead decelerated or stopped-and-went; and (3) many factors were identified as being associated with driver perception time and crash occurrence.

A multivariate spatial crash frequency model for identifying sites with promise based on crash types.

Many studies have proposed the use of a systemic approach to identify sites with promise (SWiPs). Proponents of the systemic approach to road safety management suggest that it is more effective in reducing crash frequency than the traditional hot spot approach. The systemic approach aims to identify SWiPs by crash type(s) and, therefore, effectively connects crashes to their corresponding countermeasures. Nevertheless, a major challenge to implementing this approach is the low precision of crash frequency models, which results from the systemic approach considering subsets (crash types) of total crashes leading to higher variability in modeling outcomes. This study responds to the need for more precise statistical output and proposes a multivariate spatial model for simultaneously modeling crash frequencies for different crash types. The multivariate spatial model not only induces a multivariate correlation structure between crash types at the same site, but also spatial correlation among adjacent sites to enhance model precision. This study utilized crash, traffic, and roadway inventory data on rural two-lane highways in Pennsylvania to construct and test the multivariate spatial model. Four models with and without the multivariate and spatial correlations were tested and compared. The results show that the model that considers both multivariate and spatial correlation has the best fit. Moreover, it was found that the multivariate correlation plays a stronger role than the spatial correlation when modeling crash frequencies in terms of different crash types.