Citation

Wood JS, Zhang S. Transp. Res. Rec. 2018; 2672(39): 1-10.

Affiliation

Department of Civil and Environmental Engineering, South Dakota State University, Brookings, SD 2Department of Computer Science, North Carolina State University, Raleigh, NC Corresponding Author: Address correspondence to Jonathan S. Wood: woohon@gmail.com

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118759005

PMID

unavailable

Abstract

Horizontal curves are a contributing factor to the number of observed roadway crashes. Identifying locations and geometric characteristics of horizontal curves plays a crucial role in crash prediction and prevention. However, most states in the USA face a challenge in maintaining detailed and high-quality roadway inventory databases for low-volume rural roads due to the labor-intensive and time-consuming nature of collecting and maintaining the data. This paper proposes a low-cost mobile road inventory system for two-lane horizontal curves based on off-the-shelf smartphones. The proposed system is capable of accurately detecting horizontal curves by exploiting a K-means machine learning technique. Butterworth low-pass filtering is applied to reduce sensor noise. Extended Kalman filtering is adopted to improve the GPS accuracy. Chord method-based radius computation and superelevation estimation are introduced to achieve accurate and robust results despite the low-frequency GPS and noisy sensor signals obtained from smartphones. This study implements this method using an Android-based smartphone and tests 21 horizontal curves in South Dakota. The results demonstrate that the proposed system achieves high curve identification accuracy as well as high accuracy for calculating curve radius and superelevation.

Language: en