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Title:

Automated Superelevation Measurement Method Using a Low-Cost Mobile Device: An Efficient, Cost-Effective Approach Toward Intelligent Horizontal Curve Safety Assessment

Accession Number:

01652288

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/isbn/9780309441964

Abstract:

The horizontal curve is one of the focal points of roadway safety because this curve plays a critical role in transitioning vehicles between tangent roadway sections; moreover, car crashes are frequently concentrated on horizontal curves despite their disproportionate length in the road network. As a critical safety property of horizontal curves, superelevation is crucial to vehicle safety because it counteracts the lateral acceleration produced in vehicles when they travel the curves. Despite the emergence of several sensing-based methods in recent years, labor-intensive and time-consuming manual superelevation evaluation is often carried out by transportation agencies because the newer methods usually demand expensive equipment and complicated operations. Transportation agencies are in urgent need of low-cost, reliable alternatives to improve their data collection practices. This paper proposes an automated superelevation measurement method using inexpensive mobile devices. The proposed method integrates and processes sensing data from a mobile device and derives superelevation by using fundamental vehicle kinematics at a horizontal curve. Kalman filtering–based noise reduction, regression-based radius computation, and complementaryfiltering-based rolling angle computation methods are introduced to achieve accurate results despite low-frequency, noisy signals from the inexpensive devices. An experimental test on SR-2 in Georgia demonstrates that the proposed method delivers results with accuracies comparable to those of a lidar-based method. A case study of high friction surface treatment site selection using a ball bank indicator shows that the proposed method is a promising alternative for transportation agencies to achieve low-cost yet reliable data collection for safety analysis and improvement.

Monograph Accession #:

01652088

Report/Paper Numbers:

17-04607

Language:

English

Authors:

Tsai, Yichang (James)
Ai, Chengbo

Pagination:

pp 62-70

Publication Date:

2017

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2621
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309441964

Media Type:

Digital/other

Features:

Figures; Photos; References

Subject Areas:

Highways; Safety and Human Factors

Files:

TRIS, TRB, ATRI

Created Date:

Nov 29 2017 1:46PM

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