A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Siami, Mohammad; Naderpour, Mohsen; Lu, Jie

doi:10.1109/fuzz-ieee.2019.8858812

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Insurance companies can take particular advantage of these models because underwriters are very interested in how many people are actually driving a car, especially when policies and premiums are linked to the age and number of drivers. A Mobile telematics data analytics framework based on supervised learning method has been proposed by Siami et al [37], they detected the gender of drivers from the driving styles using Choquet Fuzzy Integral Vertical Bagging Random Forest Classifier. In another study, a driver identification methodology was proposed by Moreira-Matias and Farah [38], using trip-based historical datasets collected by in-vehicle data recorders to identify the category of driver behind the wheel.…”

Section: B Driving Style Analyticsmentioning

confidence: 99%

A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction

Siami

Naderpour

2021

IEEE Trans. Intell. Transport. Syst.

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Mobile telematics is a relatively new innovation that involves collecting data on driving behavior using the internal sensors in a smartphone rather than from an in-vehicle data recorder. However, telematics data are usually not labeled, which makes extracting driving patterns from them very difficult. Therefore, unsupervised learning algorithms play an important role in this field. In addition, most current research is based on datasets developed in a laboratory or from site investigations and questionnaires, which are very different from real-world driving behaviors. To advance unsupervised learning techniques in this field, and to fill the gap in findings based on real-world data, we have developed an unsupervised pattern recognition framework for mobile telematics data. The framework comprises three main components: a self-organizing map, a nine-layers deep autoencoder, and partitive clustering algorithms. The SOM algorithm reduces the complexity of the data, the deep auto-encoder extracts the features, and the clustering algorithm groups driving events with similar patterns into behaviors. Further, given clustering with mobile telematics data is an under-researched area, we undertook an empirical comparison of five well-known clustering algorithms to determine the strengths and weaknesses of each method and which is best suited to categorizing driving styles. The study was conducted with a real-world insurance dataset containing 500,000 journeys by 2500 drivers, and the results were evaluated against three measures -Davis Boulding, Calinski Harabasz, and execution time. Overall, we find that k-means clustering and a self-organizing map were able to extract more accurate patterns than others. A statistical analysis of the 29 clusters produced by SOM and k-means, revealed 29 unique driving styles, all of which can be found in the transportation literature. The results from the study, with support from the corresponding literature review, demonstrate the efficacy of the presented framework in unsupervised settings. Additionally, the results provide a basis for developing a future risk analysis and automatic decision support system for usage-based insurance companies.

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Section: B Driving Style Analyticsmentioning

confidence: 99%

A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction

Siami

Naderpour

2021

IEEE Trans. Intell. Transport. Syst.

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“…TERKAIT Data telematik jika diolah dapat memberikan manfaat yang sangat besar oleh karena itu penelitian terkait dengan data telematik sudah dilakukan sebelumnya yaitu analisis data telematik dari aplikasi mobile yang diperoleh dari perusahaan asuransi untuk memprediksi jenis gender pengemudi menggunakan Choquet fuzzy yang dimodifikasi [9]. Pada penelitian tersebut feature yang digunakan diantaranya speed, acceleration(x,y), Yaw rate, Pitch rate, Roll rate dan GPS heading.…”

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“…Jika melihat dari penelitian serupa [9], pada penelitian tersebut kelas targetnya adalah apakah pengemudi bergender laki-laki atau perempuan, pada penelitian tersebut digunakan skor ROC-AUC sebagai salah satu penilaian algoritma yang digunakan. Hasilnya nilai AUC/skor ROC-AUC nya bernilai 71.67.…”

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Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine

Sitorus

Rizal

Jajuli

2020

JuTISI

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The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF kernel and modified C values. Every C value that is changed will be used K-Fold cross-validation first to separate the testing data and training data. The specified k value is 5. The results for each trial obtained accuracy, Receiver Operating Characteristic (ROC) and Area Under the Curves (AUC), for the best that is at C = 100 while the worst at C = 0.001.

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A Decision Model Based on an Optimized Choquet Integral: Multifactor Prediction and Intelligent Agriculture Application

Pollet

Dantan

Zghal

2023

Communications in Computer and Information Science

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Smart farming aims at improving agriculture production by using artificial intelligence and smart devices and, in continuity, farming optimization aims at supporting autonomous decision-making to maximize crop yield. In this context, the question of predicting the future days of growth stages transition of a plant is still a challenge, as existing automated predictions are not accurate nor reliable enough to be effectively used in the farming process. We propose here an approach based on Choquet integral, performing an aggregation of multiple imperfect predictions into a more accurate and reliable one, considering the specific relevance of various prediction sources as well as interactions, synergies, or redundancies between them. To identify the numerous parameter values defining the Choquet-based decision model, we propose a generic approach of optimization based on observed history, ensuring a reduced sensitivity to parameters, thanks to a principle of less specificity. Our proposal defines so an evaluation function assigning to any potential solution a predictive capability, quantifying the conformance of its outputs to evidence, as well as an associated optimization process based on the satisfaction degrees regarding a set of stated inequalities. The case study concerns an implemented prototype that enables, for a given culture and several input sources, to help farmers, providing them with better predictions of the growth stages. We also analyze the reliability of the process, enabling the assignment of an objective probabilistic criteria to any provided prediction. The experimental results are very encouraging, the predicted day remaining stable despite presence of noise and local errors.

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A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Cited by 8 publications

References 21 publications

A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction

A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction

Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine

A Decision Model Based on an Optimized Choquet Integral: Multifactor Prediction and Intelligent Agriculture Application

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