Apply Scikit-Learn in Python to Analyze Driver Behavior Based on OBD Data

Hwang, Chi-Pan; Chen, Mu-Song; Shih, Chih‐Min; Chen, Hsing-Yu; Liu, Wen Kai

doi:10.1109/waina.2018.00159

Cited by 15 publications

(11 citation statements)

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“…Multiple aggressiveness categories were presented. Another classification study on driving style was conducted in China [72]. The study utilized an OBE of GPS to observe the speed limit.…”

Section: 1) Speedmentioning

confidence: 99%

“…Furthermore, the increase of vehicle fuel efficiency has become a key research field due to its substantial impact on the usage of fossil fuel and global carbon repercussions [86]. In the evaluation of driver behavior, various control systems, such as controller area network (CAN bus) of public transports or LSTM deep learning predictor controller, cloud computing platform, and sensors have been developed to categorize and evaluate the eco-driving performance of bus drivers and to characterize driver behavior by engine speed, deceleration, pedaling, corner turn, and lane change endeavors [72,73,103].…”

Section: ) Benefits Related To Increasing Energy Conservation Awareness Of Drivermentioning

confidence: 99%

“…Meanwhile, multiple aggressiveness is another prevailing characteristic of driving style [66][67][68][69]. Apart from the multiple aggressiveness attributes of driver behavior, defensive, weak defensive, weak aggressive, and aggressive are significant features of driving style as well [72].…”

Section: ) Driver Classificationmentioning

confidence: 99%

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A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

et al. 2021

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Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends.

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“…Multiple aggressiveness categories were presented. Another classification study on driving style was conducted in China [72]. The study utilized an OBE of GPS to observe the speed limit.…”

Section: 1) Speedmentioning

confidence: 99%

Section: ) Benefits Related To Increasing Energy Conservation Awareness Of Drivermentioning

confidence: 99%

See 1 more Smart Citation

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

et al. 2021

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“…The 'get similarity' function was used to compare each data frame by applying a cross-correlation technique. The correlation technique was used for the observational relationship of the different sets in the data, based on their characteristics [54]. Various correlations could also be used to do quantitative correlation.…”

Section: Proposed Frameworkmentioning

confidence: 99%

An OpenCV-Based Approach for Automated Cardiac Rhythm Measurement in Zebrafish from Video Datasets

et al. 2021

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Cardiac arrhythmia has been defined as one of the abnormal heart rhythm symptoms, which is a common problem dealt with by cardiologists. Zebrafish were established as a powerful animal model with a transparent body that enables optical observation to analyze cardiac morphology and cardiac rhythm regularity. Currently, research has observed heart-related parameters in zebrafish, which used different approaches, such as starting from the use of fluorescent transgenic zebrafish, different software, and different observation methods. In this study, we developed an innovative approach by using the OpenCV library to measure zebrafish larvae heart rate and rhythm. The program is designed in Python, with the feature of multiprocessing for simultaneous region-of-interest (ROI) detection, covering both the atrium and ventricle regions in the video, and was designed to be simple and user-friendly, having utility even for users who are unfamiliar with Python. Results were validated with our previously published method using ImageJ, which observes pixel changes. In summary, the results showed good consistency in heart rate-related parameters. In addition, the established method in this study also can be widely applied to other invertebrates (like Daphnia) for cardiac rhythm measurement.

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“…Nirmali [17] and Hwang [18] argued that driving behaviors such as sudden acceleration and slamming on the emergency brakes affected energy consumption. The evaluation indicators related to such behaviors included but were not limited to vehicle speed, motor speed, acceleration, and deceleration.…”

Section: Data Transformationmentioning

confidence: 99%

Construction of Analytical Models for Driving Energy Consumption of Electric Buses through Machine Learning

Lin

Ying

2020

Applied Sciences

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In recent years, the Taiwan government has been calling for the use of public transportation and has been popularizing pollution-reducing green vehicles. Passenger transport operators are being encouraged to replace traditional buses with electric buses, to increase their use in urban transportation. Reduced energy consumption and operating costs are important operational benefits for passenger transport operators, and driving behavior has a significant impact on fuel consumption. Although many literatures or real-world systems have addressed the issues related to reducing energy consumption with electric buses, these works do not involve the records collected from an on-vehicle battery management system (BMS). Accordingly, the results of analyses of existing works lack in-depth discussions, and therefore the applicability of existing works is insignificant. Therefore, in this study, driving data were collected using a battery management system (BMS), and vehicular power consumption was classified according to energy efficiency. Then, decision trees and random forest were applied to construct energy consumption analytical models. Finally, the driving behaviors that influence energy consumption were investigated. A case study was conducted in which a Taichung passenger transport operator’s electric bus driving data on urban routes were collected to construct energy consumption analytical models. The data consisted of two parts, i.e., vehicle records and route records. On the basis of these records, we considered the practicability and applicability of the analytical models by transforming the unstructured records into raw data. Passenger transport operators and drivers can leverage the obtained eco-driving indicators for different bus routes for energy savings and carbon reduction.

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Apply Scikit-Learn in Python to Analyze Driver Behavior Based on OBD Data

Cited by 15 publications

References 1 publication

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

An OpenCV-Based Approach for Automated Cardiac Rhythm Measurement in Zebrafish from Video Datasets

Construction of Analytical Models for Driving Energy Consumption of Electric Buses through Machine Learning

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