This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-the-art. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of “optimal” human driving patterns that Automated Vehicles (AV) could ‘mimic’ to become safer.

The objective of this paper is to provide a critical review of the most popular and often implemented methodologies related to Usage-based motor insurance (UBI). UBI schemes, like Pay-as-you-drive (PAUD) and Pay-how-you-drive (PHUD), are a new innovative concept that has recently started to be commercialized around the world. The main idea is that instead of a fixed price, drivers have to pay a premium based on their driving behaviour and degree of exposure. Despite the fact that it has been implemented only for a few years, it is proven to be a very promising practice with a significant potential impact on traffic safety. This is achieved by the financial incentive given to drivers in order to improve their driving behaviour such as reducing the number of harsh braking and acceleration events taking place or reducing their degree of exposure such as their annual mileage, the time of the day travelling etc. and therefore reduces traffic risk. It can also be beneficial towards other social objectives such as traffic congestion and pollution emissions reduction.To this end, the existing literature on UBI schemes is critically reviewed and research gaps are identified. Findings show that there is a multiplicity and diversity of several research studies accumulated in modern literature examining the correlation between PAUD (based on driver's exposure) and PHUD (based on driving behaviour) schemes and traffic risk in order to determine accident risk. Moreover, it seems that UBI implementation would eliminate the cross-subsidies phenomenon, which implies less insurance costs for goods and less exposed drivers. Moreover, it would also provide a strong motivation for drivers to improve their driving behaviour and reduce their degree of exposure by receiving feedback and monitoring their driving performance and exposure which would result in traffic risk reduction both totally and individually

Recent research in transport safety focuses on the processing of large amounts of available data by means of intelligent systems, in order to decrease the number of accidents for transportation users. Several Machine Learning (ML) and Artificial Intelligence (AI) applications have been developed to address safety problems and improve efficiency of transportation systems. However exchange of knowledge between transport modes has been limited. This paper reviews the ML and AI methods used in different transport modes (road, rail, maritime and aviation) to address safety problems, in order to identify good practices and experiences that can be transferable between transport modes. The methods examined include statistical and econometric methods, algorithmic approaches, classification and clustering methods, artificial neural networks (ANN) as well as optimization and dimension reduction techniques. Our research reveals the increasing interest of transportation researchers and practitioners in AI applications for crash prediction, incident/failure detection, pattern identification, driver/operator or route assistance, as well as optimization problems. The most popular and efficient methods used in all transport modes are ANN, SVM, Hidden Markov Models and Bayesian models. The type of the analytical technique is mainly driven by the purpose of the safety analysis performed. Finally, a wider variety of AI and ML methodologies is observed in road transport mode, which also appears to concentrate a higher, and constantly increasing, number of studies compared to the other modes.