The ability of a Doppler LIDAR (Light Detection and Ranging) system to measure the speed of a moving rail vehicle in a non-contacting manner is extended to capture the lateral and vertical irregularities of the track itself and to evaluate the rail track quality. Using two pairs of lenses to capture speed signals from both rails individually, the track speed, curvature, and lateral and vertical geometry variations on each side are determined. LIDAR lenses are installed with a slight forward angle to generate velocity signals that contain two components: 1) the left and right track speeds, and 2) any lateral and/or vertical speed caused by track motion and/or spatial irregularities. The LIDAR system collects and outputs the track information in time domain. Separating each speed component (forward, vertical, and lateral) is possible due to the inherent separation of each phenomenon with respect to its spatial/temporal frequencies and related bandwidths. For the measurements to be beneficial in practice, the LIDAR data must be spatially located along the track. A data-mapping algorithm is then simultaneously developed to spatially match the LIDAR track geometry measurements with reference spatial data, accurately locating the measurements along the track and eliminating the need for a Global Positioning System (GPS).

A laboratory-grade LIDAR system with four Doppler channels, developed at the Railway Technologies Laboratory (RTL) of Virginia Tech, is body-mounted and tested onboard a geometry measurement railcar. The test results indicate a close match between the LIDAR measurements and those made with existing sensors onboard the railcar. The field-testing conducted during this study indicates that LIDAR sensors could provide a reliable, non-contact track-monitoring instrument for field use, in various weather and track conditions, potentially in a semi-autonomous or autonomous manner.

A length-based track quality index (TQI) is established to quantify the track geometry condition based on the geometry data collected by the LIDAR sensors. A phenomenological rail deterioration model is developed to predict the future degradation of geometry quality over the short track segments. The introduced LIDAR's TQI is considered as the condition-parameter, and an internal variable is assumed to govern the rail geometry degradation through a deterioration rule. The method includes the historical data, current track conditions collected by the LIDAR system, and traffic data to calculate the track deterioration condition and identify the geometry defects.

In addition to rail geometry inspection, a LIDAR system can potentially be used to monitor the rail surface structure and integrity. This is possible due to the fact that the Doppler shift imposed on the laser radiation reflected from a moving surface has the Doppler bandwidth broadened in proportion to the height and width of the surface features. Two LIDAR-based rail surface measures are introduced based on LIDAR measurements to identify different rail surface conditions and materials.