Objectives: This study investigated factors correlated with the severity of injuries sustained by bicyclists in bicycle–motor vehicle crashes at unsignalized intersections to develop site-specific countermeasures and interventions to improve bicycle safety. Method: Crash data were extracted from accident reports entered into the Kentucky State Police's Kentucky Collision Database in 2002–2012. A partial proportional odds model was developed for data analysis. Results: According to our modeling results, stop-controlled intersections, one-lane approaches, helmet usage, and lower speed limits were associated with decreased injury severity, whereas uncontrolled intersections, older (age > 55) drivers and bicyclists, child (age < 16) bicyclists, foggy and rainy weather, inadequate use of lights in dark conditions, and wet road surfaces were linked with increased injury severity. Conclusions: Based on these results, we suggest the development of educational programs focused on the following groups: child bicyclists, older bicyclists, and older drivers. Investigating and modifying street lighting could improve bicycle safety. Implementing road diets/traffic calming methods could create a safer traffic environment. Certain traffic control strategies (e.g., stop control) could be considered for uncontrolled intersections with high bicycle exposure, and helmet campaigns should be launched to increase helmet awareness and use. The study also suggests some interesting future research directions, including examining driver/bicyclist behaviors at uncontrolled intersections and studying the riding behaviors of child bicyclists in Kentucky.

Crashes that occur on curved roadways are often more severe than straight road accidents. Previously, most studies focused on the associations between curved sections and roadway geometric characteristics. In this study, significant factors such as driver behavior, roadway features, vehicle factors, and environmental characteristics are identified and involved in analyzing traffic accident severity. Bayesian network analysis was conducted to deal with data, to explore the associations between variables, and to make predictions using these relationships. The results indicated that factors including point of impact, site of location, accident side of road, alcohol/drugs condition, etc., are relatively critical in crashes on horizontal curves. Accident severity increases when crashes occur on bridges. The sensitivity of accident severity to vehicle use, traffic control, point of impact, and alcohol/drugs condition is relatively high. Moreover, a combination of negative factors will aggravate accident severities. The results also proposed some suggestions regarding the design of vehicles, as well as the construction and improvement of curved roadways.

As well as offering tremendous opportunities for existing transportation systems, autonomous driving presents challenges for traffic management, particularly in mixed traffic flow. The design of driving strategies for autonomous vehicles (AVs) considers both safety and efficiency to promote smooth traffic flow with no collisions. However, the complex traffic conditions at freeway bottlenecks mean that the traffic impacts of AVs in such scenarios remain unclear. Therefore, this study employs several driving strategies utilizing deep reinforcement learning (RL) to form a mixed-autonomy traffic flow on a one-way two-lane freeway with on- and off-ramps and weaving sections in SUMO traffic microsimulations. The study also examines the overall traffic impacts in terms of congested patterns, safety, efficiency, and passenger comfort. Multiple experiments are conducted with different AV driving strategies, penetration rates, and key traffic conditions. The results show that AVs with a deep-RL driving strategy significantly mitigate traffic-congested patterns and prevent the propagation of shockwaves, but at the expense of speed at low penetration rates. Moreover, the proposed driving strategy is conducive to a safer capability in vehicle dynamics. However, AVs under a deep-RL strategy lead to poorer passenger comfort at bottlenecks. Therefore, it is crucial to design a driving strategy with high robustness and scalability so that deep RL-based AVs can adapt to more complex scenarios and traffic events. Overall, the present research improves the understandings of AV driving mechanisms and provides insights into corresponding future traffic-management measures.

As the dry bulk shipping market seems to have been stuck in a trough period for a long time, investors need to pay more attention to their investment strategies to survive during this period. This study aimed to find a suitable model to assess dry bulk ship investment decisions in the tough and peak periods based on real options theories. Two options, involving an abandonment option and a deferrable option, were used to define investors’ responses to the uncertainty in investment processes such as stopping or selling vessels. The option valuation was solved by using a binomial valuation model, due to data limitations. In accordance with shipping cycle theories, different volatility parameters for the tough and peak periods were calculated using a generalized autoregressive conditional heteroskedasticity (GARCH) model. The application of the real options model to a case study involving secondhand ship trading indicated its viability. According to the results of the case study, the new model has advantages over the traditional net present value (NPV) method in uncertain investment environments. Thus, the results demonstrate that the real options model is a more suitable method for use in the current dry bulk shipping market.