AN AUTOMATED SUPERELEVATION MEASUREMENT METHOD FOR HORIZONTAL CURVE SAFETY ASSESSMENT USING A LOW COST MOBILE DEVICE
1 The horizontal curve is one of the focal points of roadway safety because it plays a critical role 2 in smoothly transitioning vehicles among tangent roadway sections; more importantly, vehicle 3 crashes frequently concentrate on horizontal curves despite their disproportional length in the road 4 network. As one of the most critical safety properties of horizontal curves, adequate supereleva5 tion is crucial to vehicle safety because it counteracts the lateral acceleration produced in vehicles 6 when they travel the curve. Despite several sensing based methods having emerged in recent 7 years, labor intensive and time consuming manual superelevation evaluation is often carried out 8 by transportation agencies because emerging methods usually demand expensive equipment (e.g. 9 high resolution cameras, mobile light detection and ranging (LiDAR) sensors etc. and compli10 cated operations. Therefore, transportation agencies are still urgently searching for low cost, re11 liable alternatives to improve their data collection practices. This paper proposes an automated 12 superelevation measurement method using inexpensive mobile devices (e.g. smartphone, tablet) 13 The proposed method integrates and processes sensing data from a mobile device (including a 14 global positioning system (GPS) an accelerometer, and a gyroscope) and derives superelevation 15 values by employing the fundamental vehicle kinematics at a horizontal curve. Kalman Filtering16 based noise reduction, regression based radius computation, and complementary filtering based 17 rolling angle computation methods are introduced to achieve accurate and robust results in spite of 18 low frequency, noisy signals from the inexpensive devices. An experimental test on State Route 19 2 in Georgia demonstrates that the proposed method delivers results with accuracy comparable to 20 the LiDAR based method. A case study of applying the proposed method in high friction surface 21 treatment (HFST) site selection using ball bank indicator (BBI) shows that the proposed method 22 provides a promising alternative for transportation agencies to achieve low cost, yet reliable data 23 collection for safety analysis and improvement. 24 25