Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective of this research is to reveal existing driving patterns around harsh events at the driving ‘pulse’ level i.e. a few seconds before and after the event. For that purpose, a time-series clustering approach is applied on speed and heart rate metrics of individual drivers using data collected from a large naturalistic driving study. Results show that there are distinct speed patterns before harsh braking, harsh acceleration, and harsh cornering events. A deceleration is identified shortly before most harsh acceleration and cornering events, which possibly indicates reckless behavior, i.e. drivers not dedicating enough time to smoothly brake before cornering, or of a brief ‘decision-making’ moment before the harsh manoeuvre. On the contrary, speed seems to be steady before harsh braking events. Regarding heart rate, the analysis revealed certain patterns only after raw data were cleansed and filtered. These patterns may show increasing, decreasing or variable heart rate trends, which may correspond to different stress patterns of drivers around harsh events. Finally, we introduce the concept of driving pattern consistency, which can reveal the share of individual drivers that follow the same harsh event pattern. It is indicated that more than half of the drivers are not consistent, suggesting that driving patterns around harsh events may be more context-related than driver personality-related.

Driver behavior analytics is an important concept that plays a significant role in the understanding of road crashes. This paper investigates the optimal number of driver profiles to understand the most important characteristics that differentiate drivers and extract useful insights on the value of using different clustering approaches in profile recognition. To this end, two Machine Learning clustering algorithms, the K-Means and OPTICS algorithms, are applied on driving data from a large naturalistic experiment using almost 18 K trips recorded from 130 drivers. The results revealed 3 profiles, the less risky drivers, the modest drivers and the more aggressive drivers. Clustering was based on 3 important driving behavior characteristics, namely the number of speeding, headway and harsh events per 100 km. The less risky drivers profile was revealed by both algorithms, whereas drivers of higher aggressiveness are distinguished by K-Means based on the driving feature that dominates the rest. The OPTICS algorithm showed that many drivers, especially the aggressive ones, present unique behavior that cannot be grouped together with other drivers. The interpretability of driver profiles resulting from the application of these unsupervised learning techniques is worsened as the number of clusters increases. The association between driver profiles and individual characteristics leads to the conclusion that aggressiveness is mainly driven by personality traits and less by specific characteristics such as gender, age or past accident history. The results of this study can be potentially used to develop profile-specific applications that provide feedback to drivers and reduce their crash risk.

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.

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-theart. 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.

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of “successful” interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's “safety pyramid”. Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches.

This study analyzed road, traffic, and human factors of pedestrian crossing behavior through the development of integrated choice and latent variables models. The analysis used recent research as a starting point, in which a two-stage approach was successfully tested, including a separate estimation of human factors and choice models. Data from a dedicated field survey were used: pedestrian field observations of road crossing behavior in different road and traffic scenarios were combined with a questionnaire on pedestrian attitudes, perceptions, motivations, and declared behaviors. The integrated choice and latent variables models were developed for four road types: major urban arterials, main roads, secondary roads, and residential roads. Results suggest that the effect of traffic conditions on pedestrian crossing choices was more important on main and secondary urban roads, whereas on major urban arterials and on residential roads it was nonsignificant. In regard to the effects of human factors, a risk latent variable was found to enhance the explanatory power of most of the models. This variable was estimated on the basis of different indicators in each case, reflecting a clear risk-taking tendency on major and main roads and an optimization tendency on minor roads. Overall, it is indicated that the integration of human factors in pedestrian crossing models provides meaningful and insightful results, and they may be advantageous compared with the two-stage approach.