Quality of surface is an important aspect affecting both the safety and the performance of at-grade rail-highway crossings. Roughness may increase the risk of crashes for both trains and automobiles. Varying grades in crossing profiles increase the likelihood of high-centered crossing collisions between train and truck [1]. The US DOT Railroad Highway Grade Crossing Handbook [2] suggests that rough surfaces could distract a driver’s attention from oncoming trains and that the unevenness of the crossing could result in a driver losing control of their vehicle resulting in a crash.

No quantitative method currently exists to quickly and economically assess the condition of rail crossings in order to evaluate the long term performance of crossings and set a quantitative trigger for their rehabilitation. The conventional method to measure the surface of quality of crossings is based on expert judgment, whereby crossing surfaces are classified as poor, fair or good after an inspector visits and drives over the crossing. However, actual condition of the crossing could be different from the subjective rating. Poor condition rating crossings may not always present the most cost-effective locations for preventive maintenance to lower overall life-cycle costs. Conventional ratings may derive from driving a passenger car of pickup once over the crossing. Effects of various speed, on various vehicles (suspension), and at various places (laterally) cannot be determined or even estimated except at the smoothest of crossings. A quantifiable and extensible procedure is desired.

With rapid advances in computer science, 3D sensing and imaging technologies, it seems logical that a cost-effective quantitative method could be developed to determine the need to rehabilitate rail crossings and assess long term performance. Fundamental to the quantification of crossing condition is the acquisition of an accurate 3D surface model of the crossing in its present state. This paper reports on the development of an accurate, low cost and readily deployable sensor capable of rapid collection of this 3D surface. The research is seen as a first step towards automating the crossing inspection process, ultimately leading to the quantification and estimation of future performance of rail crossing.

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