Smartphone-based hard-braking event detection at scale for road safety services

Liu, Luyang; Racz, David; Vaillancourt, Kara; Michelman, Julie; Barnes, Matt; Mellem, Stefan; Eastham, Paul; Green, Bradley; Armstrong, Charles; Bal, Rishi; O'Banion, Shawn; Guo, Feng

doi:10.1016/j.trc.2022.103949

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…They also have wide-ranging effects, including economic losses. Thus, methods for resolving traffic problems, such as traffic event detection techniques, have attracted attention among researchers [1,2].…”

Section: Introductionmentioning

confidence: 99%

Regional Traffic Event Detection Using Data Crowdsourcing

Kim,

Song,

Lee

et al. 2023

Applied Sciences

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Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness.

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Section: Introductionmentioning

confidence: 99%