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• Accident modification functions for traffic law enforcement are compared. • All functions show that more enforcement improves safety. • Differences-in-differences estimates capture both increases and decreases in enforcement. • A decrease in enforcement worsens road safety. Traffic law enforcement is a road safety measure whose effects on accidents or injuries is best described by means of a function rather than a point estimate. An informative function should comprise both increases and decreases in enforcement. Currently available accident modification functions cannot serve this need. A fruitful approach to developing accident modification functions covering both increases and decreases in enforcement is differences-in-differences estimates based on multivariate accident prediction models. The paper explains how to develop such estimates and illustrates them. The interpretation of the results of empirical studies can be informed by a game-theoretic model of the effects of enforcement, previously published in Accident Analysis and Prevention (Bjørnskau and Elvik 1992, 507–520). [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-method traffic system simulation model of rear-end collisions with a powered two-wheeler (PTW) is developed for the purpose of testing different collision warning algorithms. • A combined Emergency Stop Signal (ESS) & Rear-End Collision Alert Signal (RECAS) system, named Motorcycle Emergency Braking Warning System (MEBWS), reduced total number of rear-end collisions with leading PTW by approximately 30%. • In unavoidable collisions, MEBWS reduced severity of injuries by over 10%. • The estimated annual reduction in economic crash costs of PTW rear-end collisions in the EU is estimated to over 43 million EUR (45 million USD). Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU "Vision Zero" goal: "reduce road deaths to almost zero by 2050". MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive , Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision. [ABSTRACT FROM AUTHOR]

The emergence of Intelligent Connected Vehicles (ICVs) is expected to drastically change various fields in the transportation system—especially traffic safety of road users. Therefore, this special issue aims to facilitate a forum for transportation researchers fostering an exchange of research ideas and experience in traffic safety with a specific focus on operations, planning and management of ICVs. The issue contains thirty-six papers from seven different countries. Topics are classified into seven categories: (1) Driving behavior in the ICV environment; (2) Safety evaluation of ICVs; (3) ICV driving/management strategies; (4) New framework for ICV safety analysis; (5) ICV safety for vulnerable road users; (6) Perception towards ICVs; and (7) Security issues relating to ICVs. The papers are concisely introduced in this editorial. All the papers were invited to present at the International Symposium on Accident Analysis & Prevention in 2021 (ISAAP 2021) and the symposium was successfully held. The research conducted in these articles reveal challenges and future directions in the area of ICVs that include further developing novel methodologies and algorithms for collision-free trajectories of ICVs, testing diverse scenarios in complex environments with mixed traffic, and addressing inherent safety risks of specific vulnerable road users (e.g., older road users, bicyclists, riders of micro-mobility vehicles). [ABSTRACT FROM AUTHOR]

• A semi-automatic model is developed for hazard and accident cause identification. • We explore and analyze the semantic relationship between the hazards of marine accidents. • The importance of both hazards and accident causes is calculated and ranked. • We gain accident causes distribution in 40 different accident scenarios by using topic model. In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions. [ABSTRACT FROM AUTHOR]

Safety is the precondition and guarantee of railway development. However, due to equipment failure, poor management, misoperation and so on, accidents on the railway occur from time to time. China and Japan, as leading countries in Railway Technology in the world, have done a lot of work in accident prevention and safety. Through the collection and comparison of transport data and typical casualty cases in China and Japan, this paper summarizes the main causes of accidents and the experience of accident prevention in the two countries. And further, puts forward suggestions for preventing accidents and large-scale casualty. [ABSTRACT FROM AUTHOR]