A disproportionally high number of serious vehicle crashes (25% of fatal crashes) occur on horizontal curves, even though curves represent only a fraction of the roadway network (5% of highway miles). The in-service curve characteristics information, including curve radius, superelevation, and ball back indicator (BBI) angles, are extremely important for setting up adequate advisory speeds and for performing curve safety assessment and analysis. However, current transportation agencies’ practices, using dedicated devices operated by designated engineers are labor-intensive, time-consuming, and costly. The objectives of this research project are 1) to develop an enhanced curve safety assessment method that uses low-cost mobile devices and new computation methods and 2) to critically assess the feasibility of the proposed method for network level curve safety condition assessment. The proposed method, using a new intra-agency, crowdsourced data collection and computational framework, leverages a) low-cost mobile devices for collecting multiple runs of sensor data, including Global Positioning System(GPS) data and Inertial Measurement Unit (IMU) data, and b) agencies’ existing vehicles and transportation engineers. The data collection and computation framework of the proposed method consists of the following six modules: (1) mobile data collection, (2) mobile data registration and processing, (3) driving kinematics calculation, (4) curve geometry calculation, (5) advisory speed calculation, and 6) curve warning sign design. A refined superelevation computation method has been proposed in this study by revising path radius calculation using gyroscope data and GPS speeds, and refining a body roll calculation with a calibration procedure to estimate a vehicle’s roll rate. The proposed method is validated using smartphone data collected from the National Center for Asphalt Technology (NCAT) test track with ground reference superelevation measurements. Results show that, using smartphones, the accuracy of superelevation computation can consistently achieve a root mean squared error (RMSE) of below 1.5 % slope at different speeds after using the calibration procedure to estimate the vehicle’s roll rate; without roll rate estimation, the accuracy will continuously decrease with increasing speed, up to an RMSE of 3.2 % slope at high speed. A preliminary case study using multi-run smartphone data collected on Georgia State Route 17 demonstrates the feasibility of the proposed method.

The final report is available.