Understanding Driving Behavior: Measurement, Modeling and Analysis

Elassad, Zouhair Elamrani Abou; Mousannif, Hajar

doi:10.1007/978-3-030-11928-7_41

Cited by 12 publications

(2 citation statements)

References 31 publications

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“…The study was conducted using a fixed-based driving simulator located at the Cadi Ayyad University (UCA) facility. Simulator driving experiments hold the significant advantage of simulating the driving experience in a safe environment with complete experimental control over driving conditions including all types of climate, terrain, and traffic ( 18 ). Certainly, it would be very risky to carry out trials on real road settings.…”

Section: Experiments Design and Data Collectionmentioning

confidence: 99%

“…The use of simulation experiments in the field of transportation safety research has been increasing in recent years as they imitate the driving conduct in a safe environment, with the major advantage of maintaining entire empirical control over all contexts and the ability to examine numerous designs. Moreover, it would be very dangerous to carry out crash analysis simulations in a real driving environment ( 13 , 18 , 19 ). The adopted simulation scenario incorporated multiple weather conditions, namely clear, fog, rain, and snow patterns.…”

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confidence: 99%

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Efficient Fusion Decision System for Predicting Road Crash Events: A Comparative Simulator Study for Imbalance Class Handling

Elamrani Abou Elassad,

Ameksa,

Elamrani Abou Elassad

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road crash events are a fact of life. Although significant progress have been made in adopting machine learning techniques for analyzing road crashes, there has been limited emphasis on evaluating crash events within data fusion systems. The primary purpose of this study is to outline and validate a comparative safety analysis of an ensemble fusion system founded on the use of different base classifiers and a meta-classifier to procure more efficient crash prediction. Three categories of features namely vehicle-telemetry, driver-inputs and environmental-conditions have been collected using a driving-simulator in order to identify the crash strongest precursors through feature extraction technique. Furthermore, optimized strategies using AdaBoost, XGBoost, RF, GBM, LightGBM, CatBoost and KNN techniques were implemented to establish effective predictions within a fusion-based system. To ensure that the proposed system provide superior decisions given the infrequent nature of crash events, an imbalance-learning approach was conducted based on three resampling strategies: over-sampling, under-sampling and SMOTE-Tomek-Links. The findings depict that the superior performance has been attained when adopting LightGBM, CatBoost and KNN as base classifiers along with SMOTE-TL as balancing technique and XGBoost as meta-classifier with 89.19% precision, 96.77% recall and 92.83% f1-score. To our knowledge, there has been a limited interest, if not at all, at endorsing a fusion-based system examining the impact of real-time features' combinations on the prediction of road crashes while providing a critical analysis of class-imbalance. Overall, the findings emphasized the relevance of the explanatory features and can be endorsed in designing efficient intelligent transportation systems.

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Section: Experiments Design and Data Collectionmentioning

confidence: 99%

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confidence: 99%