Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network

Girma, Abenezer; Yan, Xuyang; Homaifar, Abdollah

doi:10.1109/ictai.2019.00127

Cited by 38 publications

(13 citation statements)

References 17 publications

(17 reference statements)

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“…Compared to our paper, [6], [7], [8], [12], [13] and [14] do not look for frequency. Also, LSTM in [9] obtained lower scores in comparison with a Decision Tree (DT) algorithm ( [12], [13] and [14]) on the same dataset. As shown by [14], certain features discriminate better than others for some drivers.…”

Section: State Of the Artmentioning

confidence: 96%

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Secure Routine: A Routine-Based Algorithm for Drivers Identification

Micale,

Costantino,

Matteucci

et al. 2021

Preprint

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The introduction of Information and Communication Technology (ICT) in transportation systems leads to several advantages (efficiency of transport, mobility, traffic management). However, it may bring some drawbacks in terms of increasing security challenges, also related to human behaviour. As an example, in the last decades attempts to characterize drivers' behaviour have been mostly targeted. This paper presents Secure Routine, a paradigm that uses driver's habits to driver identification and, in particular, to distinguish the vehicle's owner from other drivers. We evaluate Secure Routine in combination with other three existing research works based on machine learning techniques. Results are measured using well-known metrics and show that Secure Routine outperforms the compared works.

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Section: State Of the Artmentioning

confidence: 96%

“…Girma et al in [9] used the Long Short-Term Memory (LSTM) algorithm with sliding windows and tested their model on [10] and [11] datasets with Precision and Recall of 98%.…”

Section: State Of the Artmentioning

confidence: 99%

Secure Routine: A Routine-Based Algorithm for Drivers Identification

Micale,

Costantino,

Matteucci

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…For route choice analysis, methods of data collecting mainly include Stated Preference (SP)/ Revealed Preference (RP) survey, experiment, simulation, approaches based on data science and data-driven (for example, GPS trajectory data [13], vehicle test data [14], vehicle Telematics Data [15]). Comparing with traffic survey, experiment has stronger controllability.…”

Section: Literature Review a Route Switching Behavior Studymentioning

confidence: 99%

Drivers Route Switching Behavior Based on Group Classification

et al. 2020

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Drivers' route switching behavior shows obvious difference when they face various traffic conditions. The article studies the drivers' route switching behavior based on group classification. Questionnaire combining SP survey and RP survey is carried out to collect the drivers' route choice behavior under the influence of individual attributes, daily travel characteristic and traffic conditions. Latent Class Model (LCM) is used to analyze the behavior characteristic. According to the goodness of models, drivers are divided into three categories. Drivers of sensitive pattern will switch route easily which is represented by young people with shorter driver-age. In contrast, drivers of unresponsive pattern will not switch routes easily and the pattern is represented by elder people with longer driver-age. Based on the drivers' classification results, ordinal logistic model is established. According to the odds ratio of each variable, we find that, age, driver-age, nature of drivers, and travel frequency all affect drivers' route switching behavior.

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“…(2020), Girma et al . (2019) and Carvalho et al . (2017) employ recurrent neural networks to identify drivers or to learn different driving behaviors such as normal, moderate, and aggressive.…”

Section: Introductionmentioning

confidence: 95%

“…The fusion or selection of those measurements makes the very first (preliminary) step of our analysis. • Savelonas et al (2020), Girma et al (2019) and Carvalho et al (2017) employ recurrent neural networks to identify drivers or to learn different driving behaviors such as normal, moderate, and aggressive. The merit of those recurrent neural networks is their automated feature engineering, whereas the difficulties associated are interpretation and how to label trips therein.…”

Section: Introductionmentioning

confidence: 99%

Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data

et al. 2021

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Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

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Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network

Cited by 38 publications

References 17 publications

Secure Routine: A Routine-Based Algorithm for Drivers Identification

Secure Routine: A Routine-Based Algorithm for Drivers Identification

Drivers Route Switching Behavior Based on Group Classification

Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data

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