A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences.

INTRODUCTION: Automated vehicle (AV) technology is a promising technology for improving the efficiency of traffic operations and reducing emissions. This technology has the potential to eliminate human error and significantly improve highway safety. However, little is known about AV safety issues due to limited crash data and relatively fewer AVs on the roadways. This study provides a comparative analysis between AVs and conventional vehicles on the factors leading to different types of collisions. METHOD: A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. Four years (2017-2020) of AV and conventional vehicle crash data on California roads were used. The AV crash dataset was acquired from the California Department of Motor Vehicles, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. A buffer of 50 feet was used to associate each AV crash and conventional vehicle crash; a total of 127 AV crashes and 865 conventional vehicle crashes were used for analysis. RESULTS: Our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. Further, AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles. The variables associated with the increased likelihood of rear-end collisions for AVs include signalized intersections and lanes with less than 45 mph speed limit. CONCLUSIONS: Although AVs are found to improve safety on the road in most types of collisions by limiting human error leading to vehicle crashes, the current state of the technology shows that safety aspects still need improvement.

Exploring pre-crash gate violations behaviors of drivers at highway-rail grade crossings using a mixed multinomial logit model.

The highway-rail grade crossings (HRGCs) across the United States have been experiencing about 2500 crashes each year. Previous studies analyzed crash frequencies and fatalities; however, factors pertaining to drivers' gate violation behaviors are little known. Also, applied methodologies for gate violation behaviors analysis did not consider their heterogeneity across regions. This study uses 20-year of crash data (1999-2018) to evaluate pre-crash drivers' behaviors at HRGCs. A mixed multinomial logit model was developed to associate such behaviors with demographic factors, vehicle characteristics, temporal and environmental factors, as well as crossing-related factors. The study results indicated a high intra-class correlation coefficient which signifies the importance of including the random-effect parameter in the model. Further, the study found that male drivers are more likely to drive around the gate, while older drivers are more likely to stop and proceed before a train has passed. Furthermore, compared to trucks, all other vehicle types are more likely to drive around the gate. The influence of train speed, vehicle occupancy, visibility, among others, on drivers' pre-crash behaviors, is also presented. Understanding the impact of these factors on pre-crash behaviors may assist in improving the motorist's safety at the highway-rail grade crossings across the United States.

Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach.

The COVID-19 outbreak has extremely impacted the globe due to travel restrictions and lockdowns. Geographically, COVID-19 has shown disproportional impacts; however, the research themes' distribution is yet to be explored. Thus, this study explored the geographical distribution of the research themes that relate to COVID-19 and the transportation sector. The study applied a text network approach on the bibliometric data of over 400 articles published between December 2019 and December 2020. It was found that the researches and the associated themes were geographically distributed based on the events that took place in the respective countries. Most of the articles were published by the authors from four countries, the USA, China, Japan, and the UK. The text network results revealed that the USA-based studies mainly focused on international travelers, monitoring, travel impacts of COVID-19, and social-distancing measures. The Japanese-based studies focused on the princess diamond cruise ship incident. On the other hand, Chinese authors published articles related to travel to Wuhan and China, passenger health, and public transportation. The UK-based studies had diverse topics of interest. Lastly, the remaining 62 countries' studies focused on returning travelers from China, public transportation, and the global spread of COVID-19. The findings are crucial to the transportation sector's researchers for various applications.