Pbe

driver behavior assessment beyond trajectory profiling

Bing He, Xiaolin Chen, Dian Zhang, Siyuan Liu, Dawei Han, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, the increasing car accidents ask for the better driver behavior analysis and risk assessment for travel safety, auto insurance pricing and smart city applications. Traditional approaches largely use GPS data to assess drivers. However, it is difficult to fine-grained assess the time-varying driving behaviors. In this paper, we employ the increasingly popular On-Board Diagnostic (OBD) equipment, which measures semantic-rich vehicle information, to extract detailed trajectory and behavior data for analysis. We propose PBE system, which consists of Trajectory Profiling Model (PM), Driver Behavior Model (BM) and Risk Evaluation Model (EM). PM profiles trajectories for reminding drivers of danger in real-time. The labeled trajectories can be utilized to boost the training of BM and EM for driver risk assessment when data is incomplete. BM evaluates the driving risk using fine-grained driving behaviors on a trajectory level. Its output incorporated with the time-varying pattern, is combined with the driver-level demographic information for the final driver risk assessment in EM. Meanwhile, the whole PBE system also considers the real-world cost-sensitive application scenarios. Extensive experiments on the real-world dataset demonstrate that the performance of PBE in risk assessment outperforms the traditional systems by at least 21%.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
EditorsUlf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
PublisherSpringer Verlag
Pages507-523
Number of pages17
ISBN (Print)9783030109967
DOIs
StatePublished - Jan 1 2019
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland
Duration: Sep 10 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11053 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
CountryIreland
CityDublin
Period9/10/189/14/18

Fingerprint

Profiling
Driver
Trajectories
Trajectory
Risk Assessment
Evaluation Model
Risk assessment
Time-varying
Risk Evaluation
Model Evaluation
Accidents
Insurance
Model
Pricing
Diagnostics
Global positioning system
Safety
Costs
Railroad cars
Real-time

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

He, B., Chen, X., Zhang, D., Liu, S., Han, D., & Ni, L. M. (2019). Pbe: driver behavior assessment beyond trajectory profiling. In U. Brefeld, A. Marascu, F. Pinelli, E. Curry, B. MacNamee, N. Hurley, E. Daly, ... M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings (pp. 507-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_31
He, Bing ; Chen, Xiaolin ; Zhang, Dian ; Liu, Siyuan ; Han, Dawei ; Ni, Lionel M. / Pbe : driver behavior assessment beyond trajectory profiling. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. editor / Ulf Brefeld ; Alice Marascu ; Fabio Pinelli ; Edward Curry ; Brian MacNamee ; Neil Hurley ; Elizabeth Daly ; Michele Berlingerio. Springer Verlag, 2019. pp. 507-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Nowadays, the increasing car accidents ask for the better driver behavior analysis and risk assessment for travel safety, auto insurance pricing and smart city applications. Traditional approaches largely use GPS data to assess drivers. However, it is difficult to fine-grained assess the time-varying driving behaviors. In this paper, we employ the increasingly popular On-Board Diagnostic (OBD) equipment, which measures semantic-rich vehicle information, to extract detailed trajectory and behavior data for analysis. We propose PBE system, which consists of Trajectory Profiling Model (PM), Driver Behavior Model (BM) and Risk Evaluation Model (EM). PM profiles trajectories for reminding drivers of danger in real-time. The labeled trajectories can be utilized to boost the training of BM and EM for driver risk assessment when data is incomplete. BM evaluates the driving risk using fine-grained driving behaviors on a trajectory level. Its output incorporated with the time-varying pattern, is combined with the driver-level demographic information for the final driver risk assessment in EM. Meanwhile, the whole PBE system also considers the real-world cost-sensitive application scenarios. Extensive experiments on the real-world dataset demonstrate that the performance of PBE in risk assessment outperforms the traditional systems by at least 21{\%}.",
author = "Bing He and Xiaolin Chen and Dian Zhang and Siyuan Liu and Dawei Han and Ni, {Lionel M.}",
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He, B, Chen, X, Zhang, D, Liu, S, Han, D & Ni, LM 2019, Pbe: driver behavior assessment beyond trajectory profiling. in U Brefeld, A Marascu, F Pinelli, E Curry, B MacNamee, N Hurley, E Daly & M Berlingerio (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11053 LNAI, Springer Verlag, pp. 507-523, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018, Dublin, Ireland, 9/10/18. https://doi.org/10.1007/978-3-030-10997-4_31

Pbe : driver behavior assessment beyond trajectory profiling. / He, Bing; Chen, Xiaolin; Zhang, Dian; Liu, Siyuan; Han, Dawei; Ni, Lionel M.

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. ed. / Ulf Brefeld; Alice Marascu; Fabio Pinelli; Edward Curry; Brian MacNamee; Neil Hurley; Elizabeth Daly; Michele Berlingerio. Springer Verlag, 2019. p. 507-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Pbe

T2 - driver behavior assessment beyond trajectory profiling

AU - He, Bing

AU - Chen, Xiaolin

AU - Zhang, Dian

AU - Liu, Siyuan

AU - Han, Dawei

AU - Ni, Lionel M.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Nowadays, the increasing car accidents ask for the better driver behavior analysis and risk assessment for travel safety, auto insurance pricing and smart city applications. Traditional approaches largely use GPS data to assess drivers. However, it is difficult to fine-grained assess the time-varying driving behaviors. In this paper, we employ the increasingly popular On-Board Diagnostic (OBD) equipment, which measures semantic-rich vehicle information, to extract detailed trajectory and behavior data for analysis. We propose PBE system, which consists of Trajectory Profiling Model (PM), Driver Behavior Model (BM) and Risk Evaluation Model (EM). PM profiles trajectories for reminding drivers of danger in real-time. The labeled trajectories can be utilized to boost the training of BM and EM for driver risk assessment when data is incomplete. BM evaluates the driving risk using fine-grained driving behaviors on a trajectory level. Its output incorporated with the time-varying pattern, is combined with the driver-level demographic information for the final driver risk assessment in EM. Meanwhile, the whole PBE system also considers the real-world cost-sensitive application scenarios. Extensive experiments on the real-world dataset demonstrate that the performance of PBE in risk assessment outperforms the traditional systems by at least 21%.

AB - Nowadays, the increasing car accidents ask for the better driver behavior analysis and risk assessment for travel safety, auto insurance pricing and smart city applications. Traditional approaches largely use GPS data to assess drivers. However, it is difficult to fine-grained assess the time-varying driving behaviors. In this paper, we employ the increasingly popular On-Board Diagnostic (OBD) equipment, which measures semantic-rich vehicle information, to extract detailed trajectory and behavior data for analysis. We propose PBE system, which consists of Trajectory Profiling Model (PM), Driver Behavior Model (BM) and Risk Evaluation Model (EM). PM profiles trajectories for reminding drivers of danger in real-time. The labeled trajectories can be utilized to boost the training of BM and EM for driver risk assessment when data is incomplete. BM evaluates the driving risk using fine-grained driving behaviors on a trajectory level. Its output incorporated with the time-varying pattern, is combined with the driver-level demographic information for the final driver risk assessment in EM. Meanwhile, the whole PBE system also considers the real-world cost-sensitive application scenarios. Extensive experiments on the real-world dataset demonstrate that the performance of PBE in risk assessment outperforms the traditional systems by at least 21%.

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U2 - 10.1007/978-3-030-10997-4_31

DO - 10.1007/978-3-030-10997-4_31

M3 - Conference contribution

SN - 9783030109967

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 507

EP - 523

BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings

A2 - Brefeld, Ulf

A2 - Marascu, Alice

A2 - Pinelli, Fabio

A2 - Curry, Edward

A2 - MacNamee, Brian

A2 - Hurley, Neil

A2 - Daly, Elizabeth

A2 - Berlingerio, Michele

PB - Springer Verlag

ER -

He B, Chen X, Zhang D, Liu S, Han D, Ni LM. Pbe: driver behavior assessment beyond trajectory profiling. In Brefeld U, Marascu A, Pinelli F, Curry E, MacNamee B, Hurley N, Daly E, Berlingerio M, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Springer Verlag. 2019. p. 507-523. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-10997-4_31