Rikke Ingebrigtsen

Bio: Rikke Ingebrigtsen is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 17 citations.

Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

Abstract: Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

Persistent traffic offenders: alcohol consumption and personality as predictors of driving disqualification

Abstract: espanolLa seguridad vial es un importante problema social. Muchos accidentes se deben al incumplimiento de las normas de trafico. Las infracciones graves o reiteradas se sancionan por la via administrativa o judicial y en ambos casos las sanciones pueden suponer la perdida del permiso de conducir. Este articulo explora la relevancia del alcohol y la personalidad como factores predictivos. El objetivo del estudio es determinar si el comportamiento delictivo de los infractores persistentes esta relacionado con sus caracteristicas de personalidad y patrones de consumo de alcohol. Se utilizo una muestra de 358 conductores: 232 infractores persistentes, a los que les habia sido retirado el carnet de conducir (127 por sentencia judicial y 105 por perdida total de puntos), y 126 conductores habituales no infractores. Se administro una bateria de pruebas que miden un conjunto de factores explicativos de personalidad y consumo de alcohol. Se utilizo un diseno transversal y se realizaron analisis estadisticos de varianza y regresion buscando diferencias entre los grupos. Los resultados revelan diferencias significativas en el tipo de infracciones y accidentes entre infractores de trafico y no infractores y entre ambas categorias de infractores. Ademas, ciertas variables, como el abuso de alcohol, altos niveles de actividad, activacion emocional, busqueda de sensaciones y la tendencia a la hostilidad durante la conduccion, pueden predecir con precision la pertenencia a uno u otro grupo. Los problemas con la bebida son el mejor predictor de la perdida del permiso de conducir, tanto por condena como por acumulacion de sanciones. EnglishTraffic safety is an important social problem. Many accidents are due to non-compliance with traffic regulations. Serious or repeated offenses are sanctioned with penalty points or court conviction, and sanctions can lead to disqualification from driving. This paper explores the relevance of alcohol consumption and personality factors as predictors of driving disqualification. The aim of the study is to determine whether the behaviors of persistent offenders and their propensity for law-breaking are related to their characteristics and patterns of drinking. A sample of 358 drivers participated in the study: 126 non-offender habitual drivers and 232 persistent traffic offenders disqualified from driving for serious or repeated traffic offenses, 127 of them after conviction, 105 without conviction (by accumulation of penalties). Participants were given a battery of tests measuring a set of explanatory personality and alcohol consumption factors. We used a cross-sectional study design and performed statistical analysis of variance and regression searching for differences among the groups. The results reveal group effects, with significant differences in a number of factors between traffic offenders and non-offenders, and between both categories of offenders in a number of variables, including traffic violations that lead to demerit points and/or loss of a driver’s license and crash involvement. Certain variables, including problem drinking, high levels of activity or excitement, penchant for thrill or sensation seeking, and propensity to hostility while driving, can accurately predict group membership. Alcohol disorders are the best predictors of disqualification from driving for serious or repeat traffic offenses, both penalized and convicted.

A Zero-Inflated Ordered Probit Model to Analyze Hazmat Truck Drivers’ Violation Behavior and Associated Risk Factors

Abstract: There are few studies on the violation of truck drivers, especially the hazmat truck driver, although truck driver's violation may cause serious casualties. This paper aims to investigate hazmat truck drivers' violation behavior and identify associated risk factors. Different data sources in intelligent transportation system (ITS) including hazmat transportation management system and traffic safety management system are extracted and emerged together. Three years (2016-2018) of violation data that comprised 11612 trip record in China are employed in this research. Based on Bayesian theory, this study proposes zero-inflated ordered probit (ZIOP) model and three alternative models to exploring the relationship between hazmat truck drivers' violation frequency and the key risk factors. The results show that ZIOP model can handle excessive zero observation problem of violation data properly and differentiate between `always-zero group' drivers and drivers who did not violate the traffic rules during research period but would do so in different surroundings. The results also indicate that the violation probability and the violation frequency level of hazmat truck drivers are influenced by driver characteristics, freight order attributes, and drivers' violation records. This research provides guidance for driving training and safety education of hazmat truck drivers, and will be helpful in building better driving simulation models.