An Enhanced Network-Level Curve Safety Assessment and Monitoring Using Mobile Devices

US Transportation Collection2021PDFTech ReportTsai, Yichang (James)Yu, Pingzhou LucasLiu, TianqiYang, ZhongyuSteele, ArielGeorgia Institute of TechnologyGeorgia Tech Research CorporationGeorgia. Department of Transportation. Office of Performance-Based Management & ResearchUnited States. Department of Transportation. Federal Highway AdministrationGeorgia. Dept. of Transporation. Office of Performance-Based Management and ResearchUnited StatesAlgorithmsCalibrationData collectionData qualityField testsGlobal Positioning SystemHighway curvesHighway safetyMethodologyMobile applicationsPrototypesValidationVehicle trajectoriesCrowdsourcingBall Bank IndicatorSuperelevationHorizontal Curve SafetyLow-cost Mobile DeviceFHWA-GA-21-1917PI# 0016839Final Report; July 2019 \u2013 July 2021A 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) (FHWA, 2021; Atkinson et al., 2016). The in-service curve characteristics information, including curve radius, superelevation, and BBI angles, are extremely important for setting up adequate advisory speeds and for performing curve safety assessment and analysis. However, current transportation agencies\u2019 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) existing, low-cost mobile devices for collecting multiple runs of sensor data, including GPS data and IMU data, and b) agencies\u2019 existing vehicles and transportation engineers. The proposed data collection and computation framework 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\u2019s roll rate. The proposed method is validated using smartphone data collected from the NCAT test track with ground reference superelevation measurements. Results show that, using smartphones, the accuracy of superelevation computation can consistently achieve an RMSE of below 1.5 % slope at different speeds after using the calibration procedure to estimate the vehicle\u2019s 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 Route17 demonstrates the feasibility of the proposed method.120