Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles
Dangerous driving behaviors are diverse and complex. Determining how to analyze the driving behavior of public drivers objectively and accurately has always been a research challenge. This research proposes a macroscopic and dynamic method for evaluating drivers’ dangerous driving degree...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9765502/ |
| _version_ | 1828791907780657152 |
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| author | David Chunhu Li Michael Yu-Ching Lin Li-Der Chou |
| author_facet | David Chunhu Li Michael Yu-Ching Lin Li-Der Chou |
| author_sort | David Chunhu Li |
| collection | DOAJ |
| description | Dangerous driving behaviors are diverse and complex. Determining how to analyze the driving behavior of public drivers objectively and accurately has always been a research challenge. This research proposes a macroscopic and dynamic method for evaluating drivers’ dangerous driving degree based on a fuzzy inference system. It also designs fuzzy-macro long short-term memory (LSTM), a variant of LSTM recurrent neural networks, which can predict drivers’ dangerous driving behaviors and risk degree. We elucidate how a macroscopic fuzzy inference dangerous driving behavior system is designed based on various driving behavior factors and the neuron architecture of the fuzzy-macro LSTM network. We collect real driving behavior data of drivers on the road and conduct a series of experimental analyses. Compared with five other commonly used time-series forecasting neural network models, our fuzzy-macro LSTM model performs best in terms of prediction error. Experimental results verify the effectiveness of the proposed method for macroanalysis and prediction of dangerous driving behavior. |
| first_indexed | 2024-12-12T02:59:11Z |
| format | Article |
| id | doaj.art-854dd3a3bbe54505b2de56ca72ded7b7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T02:59:11Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-854dd3a3bbe54505b2de56ca72ded7b72022-12-22T00:40:40ZengIEEEIEEE Access2169-35362022-01-0110478814789510.1109/ACCESS.2022.31712479765502Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of VehiclesDavid Chunhu Li0https://orcid.org/0000-0001-5250-310XMichael Yu-Ching Lin1https://orcid.org/0000-0001-5466-5831Li-Der Chou2https://orcid.org/0000-0003-2044-3119Information Technology and Management Program, Ming Chuan University, Taoyuan, TaiwanInformation Technology and Management Program, Ming Chuan University, Taoyuan, TaiwanDepartment of Computer Science and Information Engineering, National Central University, Taoyuan, TaiwanDangerous driving behaviors are diverse and complex. Determining how to analyze the driving behavior of public drivers objectively and accurately has always been a research challenge. This research proposes a macroscopic and dynamic method for evaluating drivers’ dangerous driving degree based on a fuzzy inference system. It also designs fuzzy-macro long short-term memory (LSTM), a variant of LSTM recurrent neural networks, which can predict drivers’ dangerous driving behaviors and risk degree. We elucidate how a macroscopic fuzzy inference dangerous driving behavior system is designed based on various driving behavior factors and the neuron architecture of the fuzzy-macro LSTM network. We collect real driving behavior data of drivers on the road and conduct a series of experimental analyses. Compared with five other commonly used time-series forecasting neural network models, our fuzzy-macro LSTM model performs best in terms of prediction error. Experimental results verify the effectiveness of the proposed method for macroanalysis and prediction of dangerous driving behavior.https://ieeexplore.ieee.org/document/9765502/Data analysistime seriesfuzzy rulesdriving behaviorpredictionfuzzy neural network |
| spellingShingle | David Chunhu Li Michael Yu-Ching Lin Li-Der Chou Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles IEEE Access Data analysis time series fuzzy rules driving behavior prediction fuzzy neural network |
| title | Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles |
| title_full | Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles |
| title_fullStr | Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles |
| title_full_unstemmed | Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles |
| title_short | Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles |
| title_sort | macroscopic big data analysis and prediction of driving behavior with an adaptive fuzzy recurrent neural network on the internet of vehicles |
| topic | Data analysis time series fuzzy rules driving behavior prediction fuzzy neural network |
| url | https://ieeexplore.ieee.org/document/9765502/ |
| work_keys_str_mv | AT davidchunhuli macroscopicbigdataanalysisandpredictionofdrivingbehaviorwithanadaptivefuzzyrecurrentneuralnetworkontheinternetofvehicles AT michaelyuchinglin macroscopicbigdataanalysisandpredictionofdrivingbehaviorwithanadaptivefuzzyrecurrentneuralnetworkontheinternetofvehicles AT liderchou macroscopicbigdataanalysisandpredictionofdrivingbehaviorwithanadaptivefuzzyrecurrentneuralnetworkontheinternetofvehicles |