A 3D Evaluation Method for Rail–Highway Hump Crossings

Abstract

Rail–highway grade crossing crashes may be caused by long vehicles with low clearances becoming stranded when attempting to negotiate rail–highway crossings with steeply graded profiles—hump crossings. Although design standards have been established to encourage the construction that most vehicles are able to successfully navigate, the combination of grade profile and cross‐section interacting with unique vehicle dimensions may lead to instances where vehicles may have difficulty crossing. This article describes a three‐dimensional (3D)‐based (LiDAR) methodology to identify and evaluate the severity of hump crossings. A five‐level criterion is proposed to rate the magnitude of contact. The methodology is demonstrated by modeling seven common low‐clearance vehicle types at three types of hump crossings. Field testing indicates a good correlation between the predicted degree of vehicle‐crossing conflicts and those observed in the field. Results are applicable to operations (e.g., truck routing) or for maintenance and reconstruction programs. Future research could extend the methodology to tunnel and bridge clearances, real‐time warning systems, autonomous vehicles, or even nontransportation applications.

Underpass clearance checking in highway widening projects using digital twins

“…Sometimes, long vehicles with low clearances could be stranded when attempting to pass rail-highway crossings with steeply graded profiles-hump crossings due to the combination of grade profile and cross-section interacting with unique vehicle dimensions. Thus, Liu, et al [35] proposed a 3D LiDAR-based methodology to identify and evaluate the severity of hump crossings. A five-level criterion was proposed to rate the magnitude of contact, which could provide a good predicted degree of vehicle-crossing conflicts compared to those observed in the field and the method applied to operations (e.g., truck routing) or maintenance and reconstruction programs.…”

A Method for Automatically Recreating the Horizontal Alignment Geometry of Existing Railways

“…In recent years, various intelligent and automatic methods have been widely developed for railway safety and efficiency, such as risk analysis of railway lines integrating human errors (Castillo et al., ), safety evaluation for railway bridges (Park et al., ), identifying and evaluating the severity of rail‐highway hump crossings (Liu et al., ), integrated train timetabling (Castillo et al., ; Yue et al., ), and optimizing location and capacity of locomotive maintenance shops (Xie et al., ). These methods help improve railway safety and efficiency.…”