poster

Mining spatio-temporal data for computing driver stress and observing its effects on driving behavior

Authors:

Gyanesha Prajjwal,

Sandip ChakrabortyAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 452 - 455

https://doi.org/10.1145/3274895.3274941

Published: 06 November 2018 Publication History

Abstract

With the increase in road fatalities due to various factors like aggressive driving and road rage, quantifying and monitoring the stress level of a driver is an important task for the preparation of driving rosters for the cab companies. Stress monitoring using physiological sensors is a costly and obstructive task, while stress factors impact differently for different individuals based on their personality traits. In this paper, we develop a learning-based model to predict the stress level of a driver and its effect on his driving behavior, solely based on spatio-temporal driving data collected through GPS and inertial sensors. We further establish a correlation between the stress level of a driver and his driving behavior; thus, we develop a complete system to infer stress profiling and its impact on driving behavior based on spatio-temporal driving data. The model has been tested over a publicly available dataset with 6 drivers for 500 minutes of driving data. We observe that the proposed model gives an average prediction accuracy of 79% with low false-positive rates.

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Cited By

Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614924
Vyas JBhumika Das DChaudhury S(2023)Federated learning based driver recommendation for next generation transportation systemExpert Systems with Applications10.1016/j.eswa.2023.119951225(119951)Online publication date: Oct-2023
https://doi.org/10.1016/j.eswa.2023.119951
Vyas JDas DChaudhury S(2022)DriveBFR: Driver Behavior and Fuel-Efficiency-Based Recommendation SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31120769:5(1446-1455)Online publication date: Oct-2022
https://doi.org/10.1109/TCSS.2021.3112076
Show More Cited By

Index Terms

Mining spatio-temporal data for computing driver stress and observing its effects on driving behavior
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614924
Vyas JBhumika Das DChaudhury S(2023)Federated learning based driver recommendation for next generation transportation systemExpert Systems with Applications10.1016/j.eswa.2023.119951225(119951)Online publication date: Oct-2023
https://doi.org/10.1016/j.eswa.2023.119951
Vyas JDas DChaudhury S(2022)DriveBFR: Driver Behavior and Fuel-Efficiency-Based Recommendation SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31120769:5(1446-1455)Online publication date: Oct-2022
https://doi.org/10.1109/TCSS.2021.3112076
Bhumika Vyas JDas D(2022)Stress-Aware Recommendation for Safe Driving using MTL-ConvLSTM2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC55140.2022.9922291(3490-3495)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1109/ITSC55140.2022.9922291
Abidi IKumar VSen R(2021)Practical Attestation for Edge Devices Running Compute Heavy Machine Learning ApplicationsProceedings of the 37th Annual Computer Security Applications Conference10.1145/3485832.3485909(323-336)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.1145/3485832.3485909

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