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Driving behavior monitoring by smartphone sensors is one of the most investigated approaches to ameliorate road safety. Various methods are adopted in the literature; however, to the best of our knowledge, their robustness to the prediction of new unseen data from different drivers and road conditions is not explored. In this paper, a two-phase Mac...
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Context 1
... record the drivers' behavior, accelerometer and gyroscope sensors embedded in different smartphones are utilized. These two sensors record motion data of smartphones in three different dimensions (x, y, and z indicated in Figure 1(a)). The physical and gravitational acceleration (m/s 2 ) of the devices is collected by the accelerometer and the angular velocity (rad/s) relative to the three axes of the smartphones is stored by the gyroscope. ...
Context 2
... the coordination of the smartphone should be aligned with the vehicle direction, every device must be affixed to the car during the data collection. In our experiment, smartphones are connected to the vehicles in a way that the z-axis is toward the sky and the y axis is aligned with the direction of the car movement (Figure 1(b)). To make sure that the smartphones are not affected by unwanted movements inside the vehicles, we used some equipment to strictly connect the smartphones to the vehicles such as mobile holders or adhesive tapes. ...
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... L E is the label time series recorded for the event type E (brake or turn) and l E ti is always 0 or 1, denoting whether r S, A ð Þ ti is part of a specific event or not. Additionally, the total number of the recorded data points in a whole trip is N. Based on Figure 1, the raw input for the phase one in the brake detection part is R Acc y and in the turn detection part are R Gyr z and R Acc x : The raw inputs, in this phase, are first modified by different filters as shown. In fact, by noise elimination, R S A becomes FR S A , where F demonstrate the type of filter (L for low-pass, H for highpass, W 1 for level one and W 2 for level two of wavelet filter). ...
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Automatic vehicle detection using machine learning techniques have been well studied in the last decade. Most of the previous work on vehicle recognition has been performed on datasets like GTI which mostly involve well-structured road scenarios. Such datasets are not representative of road scenes pertaining to less developed road infrastructure. T...
Citations
... Machine learning algorithms have been used to detect various forms of distracted driving, including phone use. For instance, they used machine learning to detect phone use while driving by analyzing accelerometer and gyroscope data from a smartphone [23][24][25][26][27][28]. Another study used machine learning to detect phone use while driving by analyzing front-facing camera images [29,30]. ...
This paper begins with the assumption that a mobile phone is placed in the right trouser pocket. Its primary objective is to ascertain whether the mobile phone belongs to the driver or the passenger. To achieve this goal, the paper conducts an analysis of raw data collected from phone sensors, specifically the accelerometer, gyroscope, orientation, and GPS, in order to identify and detect movement in the right leg. Based on this analysis, the application makes the decision to disconnect the signal and Internet access on the driver's phone if the car's speed exceeds 60 miles per hour, while maintaining these services for passengers. Subsequently, relevant features are extracted from the data, and a variety of machine learning algorithms are evaluated to determine the most suitable model for the task. The results demonstrate promising performance, suggesting that the proposed method could serve as an effective tool for detecting distracted driving.
... The main reason is that the investigation of phase 2 needs an exclusive and extensive experiment design. Interested readers are referred to Zarei Yazd et al. (2022), Eftekhari and Ghatee (2019), and Ferreira et al. (2017) for studying some of the current event extraction methods and their challenges. ...
Classification of driving events is a crucial stage in driving behavior monitoring using smartphone sensory data. It has not been previously explored that to what extent classification performance depends on the classifier type and input data characteristics. To fill this gap, a real-world experiment is designed for supervised data collection. Then the effects of different machine learning (ML) classifiers, data sampling rates, and sensor combinations on the final classification accuracy are demonstrated. A considerable number of labeled events (4114) containing 11 types of driving maneuvers are collected using base sensors (accelerometer and gyroscope) and composite sensors (linear accelerometer and rotation vector) available in smartphones. Several models using 23 ML algorithms are trained. The sensitivity of these models is analyzed by changing the characteristics of the input data concerning the type of ML classifier, data sampling rate, and the bundle of mobile sensors. It is demonstrated that: (1) F1 scores vary from 70 to 96% for different ML classifiers, (2) F1 scores drop 30–40% depending on the classifier type when reducing the data sampling rate, and (3) using all four sensors as a bundle for classifying driving events is not reasonable since an approximate equal F1 score is achievable by a three-sensor bundle which includes an accelerometer and a linear accelerometer.
This work presents a novel transportation mode detection algorithm that handles the recognition of kick-scooters. In 2015, 10 minutes of data from a kick-scooter were considered in a transportation mode detection study, yielding a 56% F1-score. Since then, kick-scooters were not given much attention. Yet, kick-scooters are now very present in the urban transportation ecosystem, and their consideration in transportation studies has become a must. To fill this gap, 4 hours of kick-scooter signals were collected by 18 participants, with a set of 6 different kick-scooters, using 3 body-worn inertial measurement units. Obviously, kick-scooter patterns are classified in contrast with other modes of transportation. Two classification scenarios are considered in order to gradually increase the classification model complexity. The first scenario includes walking, biking, and kick-scooter, while the second considers public transport (tramway and bus) in addition to the former transportation modes. Results show that kick-scooters can be detected with an F1-score of 80% in the first scenario. Walking and public transport samples were still accurately classified in the second scenario, with an F1-score above 80% for both classes. However, bike and kick-scooter samples were both classified with lower F1-scores, equal to 59% and 64% respectively. Therefore, the main focus of future works should be directed toward the separability of kick-scooters and bikes when public transport is considered. The findings also suggest to place preferably the sensors in the trouser’s pocket, allowing for leg motion to be finely captured.
The way in which public transport buses are driven has an influence in users’perception and satisfaction with the service. Bus driver’s behavior is usually obtained surveying passengers and/or using the mystery passenger method, not necessarily allowing for an objective and continuous evaluation. In this work, we introduce a novel methodology to automatically classify drivers’ behavior in a more consistent and objective manner, based on data from inertial measurement units, and machine learning techniques. By substituting human evaluators with automatic data collection and classification algorithms, we are able to reduce the subjectivity and cost of the current methodology, while increasing sample size. Our approach is based on three components: i) data capture using inertial measurement units (e.g. mobile devices), ii) carefully tuned classifiers that deal with sample imbalance problems, and iii) an interpretable scoring system. Results show that collected data captures several types of undesirable maneuvers, providing a rich information to the classification process. In terms of categorization performance, the evaluated classifiers, namely support vector machines, decision trees and k-NN, deliver high and consistent accuracy after the tuning process, even in the presence of a highly imbalanced sample. Finally, the proposed driver’s behavior score shows high discriminative power, effectively characterizing differences between drivers, and providing driver-tailored driving recommendations, that can be generated in specific spots, in order to improve passengers’ experience. The resulting methodology can be cost-effectively deployed at a large scale with good performance.
