How safe is your (taxi) driver?

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

1 Citation (Scopus)

Abstract

For an auto insurer, understanding the risk of individual drivers is a critical factor in building a healthy and profitable portfolio. For decades, assessing the risk of drivers has relied on demographic information which allows the insurer to segment the market in several risk groups priced with an appropriate premium. In the recent years, however, some insurers started experimenting with so called Usage-Based Insurance (UBI) in which the insurer monitors a number of additional variables (mostly related to the location) and uses them to better assess the risk of the drivers. While several studies have reported results on the UBI trials these studies keep the studied data confidential (for obvious privacy and business concerns) which inevitably limits their reproducibility and interest by the data-mining community. In this paper we discuss a methodology for studying driver risk assessment using a public dataset of 173M taxi rides in NYC with over 40K drivers. Our approach for risk assessment utilizes not only the location data (which is significantly sparser than what is normally exploited in UBI) but also the revenue, tips and overall activity of the drivers (as proxies of their behavioral traits) and obtain risk scoring accuracy on par with the reported results on non-professional driver cohorts in spite of sparser location data and no demographic information about the drivers.

Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2319-2322
Number of pages4
VolumePart F131841
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Other

Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017
CountrySingapore
CitySingapore
Period6/11/1710/11/17

Fingerprint

Insurer
Insurance
Demographics
Risk assessment
Revenue
Data mining
Methodology
Scoring
Premium
Privacy
Cohort
Critical factors

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Stanojevic, R. (2017). How safe is your (taxi) driver? In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management (Vol. Part F131841, pp. 2319-2322). Association for Computing Machinery. https://doi.org/10.1145/3132847.3133068

How safe is your (taxi) driver? / Stanojevic, Rade.

CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841 Association for Computing Machinery, 2017. p. 2319-2322.

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

Stanojevic, R 2017, How safe is your (taxi) driver? in CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. vol. Part F131841, Association for Computing Machinery, pp. 2319-2322, 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Singapore, Singapore, 6/11/17. https://doi.org/10.1145/3132847.3133068
Stanojevic R. How safe is your (taxi) driver? In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841. Association for Computing Machinery. 2017. p. 2319-2322 https://doi.org/10.1145/3132847.3133068
Stanojevic, Rade. / How safe is your (taxi) driver?. CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841 Association for Computing Machinery, 2017. pp. 2319-2322
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