Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.
Crashes are events that involve the interaction of different components: road, driver, vehicle and environment. Nevertheless, road is an essential component and improvements on road conditions are directly related to increased traffic safety. From 2008 to 2010 a road safety inspection project was developed, whose aim was to identify and collect information about hazardous points on the Complementary Road Network of Andalusia, Spain, and build a database with this information. These elements were technically called Susceptible Elements of Improvement (ESM), which are defined as elements on the road that show worse road conditions than the ideal road safety standards. The main objective of this paper is to study the relationship between ESMs, number of crashes and hazardous sections, by analysing the information gathered in this database with advanced data mining techniques. Economically, this project is rather beneficial, since the resources of governments are limited, and therefore, it is necessary to intervene in those sections that have a higher cost-effectiveness ratio. Therefore, these relationships between roads conditions and crashes will be identified by analysing the information available in this data set of the Government of Andalusia, which has not been previously used.
Autonomous vehicles (AVs) must interact with other road users including pedestrians. Unlike passive environments, pedestrians are active agents having their own utilities and decisions, which must be inferred and predicted by AVs in order to control interactions with them and navigation around them. In particular, when a pedestrian wishes to cross the road in front of the vehicle at an unmarked crossing, the pedestrian and AV must compete for the space, which may be considered as a game-theoretic interaction in which one agent must yield to the other. To inform AV controllers in this setting, this study collects and analyses data from real-world human road crossings to determine what features of crossing behaviours are predictive about the level of assertiveness of pedestrians and of the eventual winner of the interactions. It presents the largest and most detailed data set of its kind known to us, and new methods to analyze and predict pedestrian-vehicle interactions based upon it. Pedestrian-vehicle interactions are decomposed into sequences of independent discrete events. We use probabilistic methodslogistic regression and decision tree regression-and sequence analysis to analyze sets and sub-sequences of actions used by both pedestrians and human drivers while crossing at an intersection, to find common patterns of behaviour and to predict the winner of each interaction. We report on the particular features found to be predictive and which can thus be integrated into gametheoretic AV controllers to inform real-time interactions.
Recycled concrete aggregates and mixed recycled aggregates are specified as types of aggregates with lower densities, higher water absorption capacities, and lower mechanical strength than natural aggregates. In this paper, the mechanical behaviour and microstructural properties of natural aggregates, recycled concrete aggregates and mixed recycled aggregates were compared. Different specimens of unbound recycled mixtures demonstrated increased resistance properties. The formation of new cement hydrated particles was observed, and pozzolanic reactions were discovered by electronon microscopy in these novel materials. The properties of recycled concrete aggregates and mixed recycled aggregates suggest that these recycled materials can be used in unbound road layers to improve their mechanical behaviour in the long term.
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