research-article

MyDrive: Drive Behavior Analytics Method And Platform

Authors:

Tanushree Banerjee,

Arijit Chowdhury,

Tapas ChakravartyAuthors Info & Claims

WPA '16: Proceedings of the 3rd International on Workshop on Physical Analytics

Pages 7 - 12

https://doi.org/10.1145/2935651.2935652

Published: 26 June 2016 Publication History

Abstract

In recent times, research on intelligent transportation and drive quality characterization has emerged to be an important area in the domain of intelligent vehicular telematics. The estimation of driving behavior quality and relative assessment of risky driving has always been a topic of interest for fleet managers, vehicle owners as well as the insurance providers. The most appealing use case that has come up is the analysis and reporting of the driving behavior, so that the drivers can get the feedback and change their driving pattern accordingly. Assessing driving style of an individual, relative categorization in a group of drivers, identifying his abnormal trips among all trips, demands continuous monitoring of the driver. In order to address these problems a statistical aggregate model is required. In this paper we propose an algorithm Skill- Aggression Quantifier (SAQ) which monitors, quantifies and classifies driving styles. The formulated idea has been implemented in an automated tool "MyDrive", which monitors and analyses the road-vehicle-driver interaction and models the driving styles of the individuals statistically.

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Digital Library

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Chakravarty, Tapas, Aditya Ghose, Chirabrata Bhaumik, and Abishi Chowdhury. "MobiDriveScore-- A system for mobile sensor based driving analysis: A risk assessment model for improving one's driving." In Sensing Technology (ICST), 2013 Seventh International Conference on, pp. 338--344. IEEE, 2013.

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Cited By

Cui DWang ZZhang ZLiu PWang SDorrell D(2022)Driving Event Recognition of Battery Electric Taxi Based on Big Data AnalysisIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.309275623:7(9200)Online publication date: 2022
https://doi.org/10.1109/TITS.2021.3092756
Banerjee TChowdhury AChakravarty TGhose A(2020)Driver authentication by quantifying driving style using GPS only2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PerComWorkshops48775.2020.9156080(1-6)Online publication date: Mar-2020
https://doi.org/10.1109/PerComWorkshops48775.2020.9156080
Yu RLong XLi J(2019)Driving Style Analyses for Car-sharing Users Utilizing Low-frequency Trajectory Data2019 5th International Conference on Transportation Information and Safety (ICTIS)10.1109/ICTIS.2019.8883597(927-933)Online publication date: Jul-2019
https://doi.org/10.1109/ICTIS.2019.8883597
Show More Cited By

Index Terms

MyDrive: Drive Behavior Analytics Method And Platform
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Ubiquitous computing
    2. Ubiquitous and mobile devices
      1. Mobile devices
      2. Smartphones

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Information

Published In

cover image ACM Conferences

WPA '16: Proceedings of the 3rd International on Workshop on Physical Analytics

June 2016

62 pages

ISBN:9781450343282

DOI:10.1145/2935651

Program Chairs:
Nicholas D. Lane
University College London and Bell Labs
,
Xia Zhou
Dartmouth College
,
Fahim Kawsar
Bell Labs

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

In-Cooperation

SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2016

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Author Tag

mobile application

Qualifiers

Research-article

Conference

MobiSys'16

Sponsor:

SIGMOBILE

MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services

June 26, 2016

Singapore, Singapore

Acceptance Rates

WPA '16 Paper Acceptance Rate 5 of 9 submissions, 56%;

Overall Acceptance Rate 11 of 17 submissions, 65%

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Cited By

Cui DWang ZZhang ZLiu PWang SDorrell D(2022)Driving Event Recognition of Battery Electric Taxi Based on Big Data AnalysisIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.309275623:7(9200)Online publication date: 2022
https://doi.org/10.1109/TITS.2021.3092756
Banerjee TChowdhury AChakravarty TGhose A(2020)Driver authentication by quantifying driving style using GPS only2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PerComWorkshops48775.2020.9156080(1-6)Online publication date: Mar-2020
https://doi.org/10.1109/PerComWorkshops48775.2020.9156080
Yu RLong XLi J(2019)Driving Style Analyses for Car-sharing Users Utilizing Low-frequency Trajectory Data2019 5th International Conference on Transportation Information and Safety (ICTIS)10.1109/ICTIS.2019.8883597(927-933)Online publication date: Jul-2019
https://doi.org/10.1109/ICTIS.2019.8883597
Gao ZLi LFeng JYu RWang XYin C(2018)Driver Identification Based on Stop-and-Go Events Using Naturalistic Driving Data2018 11th International Symposium on Computational Intelligence and Design (ISCID)10.1109/ISCID.2018.00076(306-310)Online publication date: Dec-2018
https://doi.org/10.1109/ISCID.2018.00076
Wahlstrom JSkog IHandel P(2017)Smartphone-Based Vehicle Telematics: A Ten-Year AnniversaryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2017.268046818:10(2802-2825)Online publication date: Oct-2017
https://doi.org/10.1109/TITS.2017.2680468
Zfnebi KSouissi NTikito K(2017)Driver behavior quantitative models: Identification and classification of variables2017 International Symposium on Networks, Computers and Communications (ISNCC)10.1109/ISNCC.2017.8071987(1-6)Online publication date: May-2017
https://doi.org/10.1109/ISNCC.2017.8071987
Nirmali BWickramasinghe SMunasinghe TAmalraj CBandara H(2017)Vehicular data acquisition and analytics system for real-time driver behavior monitoring and anomaly detection2017 IEEE International Conference on Industrial and Information Systems (ICIIS)10.1109/ICIINFS.2017.8300417(1-6)Online publication date: Dec-2017
https://doi.org/10.1109/ICIINFS.2017.8300417
Virojboonkiate NVateekul PRojviboonchai K(2017)Driver identification using histogram and neural network from acceleration data2017 IEEE 17th International Conference on Communication Technology (ICCT)10.1109/ICCT.2017.8359893(1560-1564)Online publication date: Oct-2017
https://doi.org/10.1109/ICCT.2017.8359893

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