Objectives In order to ensure the safety of railway operation, it's necessary to regularly and accurately inspect and adjust the track to maintain track geometry. The function of track fine adjustment of railway is to eliminate track irregularities. The traditional track fine adjustment method of railway based on the design alignment has many shortcomings, of which, how to improve the efficiency and quality of track fine adjustment has become one of the hot issues of railway operation and maintenance.

Methods The dominating characteristics of track deviation curve are fitted by cubic spline curve which takes advantages of the local spatial correlation and the global multi-extremum characteristics of track deviation curve. Then, the fitting curve is served as the zero line of track fine adjustment, so that the spatial correlation of track deviation is fully considered. The correctness and feasibility of cubic spline curve fitting functional model are verified by fitting error analysis.

Results Two examples of track fine adjustment show that the total amount of track fine adjustment calculated by the proposed algorithm has decreased by 40.6% for a section of ballastless track and 46.7% for a section of ballasted track compared with the traditional method, and the adjustment result is completely qualified, demonstrating that the proposed algorithm is able to significantly improve the quality and efficiency of track adjustment, and is valuable in practice.

Conclusions Even if the track deviations show randomness globally, spatial correlation is still significant locally. In addition, restricted by the design alignment of railway, track deviations are limited, leading to a multi-extremum characteristic. Therefore, considering track deviation characteristics are necessary in track fine adjustment. To this end, track fine adjustment algorithm considering track deviation characteristics is proposed, of which the outcomes are proved desirable. The proposed algorithm is worth popularizing in practice.