Automated Superelevation Measurement Method Using a Low-Cost Mobile Device: An Efficient, Cost-Effective Approach Toward Intelligent Horizontal Curve Safety Assessment
Automated Superelevation Measurement Method Using a Low-Cost Mobile Device: An Efficient, Cost-Effective Approach Toward Intelligent Horizontal Curve Safety Assessment
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使用低成本移动设备的自动超高测量方法：一种高效、经济的智能水平曲线安全评估方法
DOI：
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发表时间：
2017
期刊：
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通讯作者：
Chengbo Ai
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文献类型：
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作者：
Y. Tsai;Chengbo Ai
水平曲线是道路安全的焦点之一，因为该曲线对于车辆在相切路段之间的过渡起着至关重要的作用；此外，尽管水平弯道在道路网络中的长度不成比例，但车祸经常集中在水平弯道上。作为水平弯道的一项关键安全特性，超高对于车辆安全至关重要，因为它抵消了车辆在弯道行驶时产生的横向加速度。尽管近年来出现了几种基于传感的方法，但交通机构经常进行劳动密集型且耗时的手动超高评估，因为较新的方法通常需要昂贵的设备和复杂的操作。运输机构迫切需要低成本、可靠的替代方案来改进其数据收集实践。本文提出了一种使用廉价移动设备的自动超高测量方法。所提出的方法集成并处理来自移动设备的传感数据，并通过使用水平曲线上的基本车辆运动学来推导超高。引入基于卡尔曼滤波的降噪、基于回归的半径计算和基于互补滤波的滚动角计算方法，以在来自廉价设备的低频、噪声信号的情况下获得准确的结果。在乔治亚州对 SR-2 进行的实验测试表明，所提出的方法所提供的结果的精度与基于激光雷达的方法相当。使用球库指示器进行高摩擦表面处理地点选择的案例研究表明，所提出的方法是运输机构实现低成本且可靠的数据收集以进行安全分析和改进的有前途的替代方案。
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 complementary-filtering-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.