# An Incentive Factor-Based Dynamic Comprehensive Evaluation on a High-Speed Railway Track
## 1 Introduction  
With the rapid development of high-speed railway technology, the dynamic performance requirements for high-speed trains are becoming increasingly demanding due to the increase in vehicle speed. This brings a significant challenge to the service quality of high-speed railway tracks. However, wheel-rail nonlinear interaction leads to a constant deterioration in the service quality of high-speed tracks, significantly increasing the potential risks to the operation of high-speed trains. Therefore, the need to improve service quality promotes the development of methods for high-speed railway track management. Many studies have provided qualitative evaluation of high-speed railway tracks. The power spectrum density (PSD) of track irregularity was used to evaluate the railway tracks both qualitatively and quantitatively [1]. [PERSON] et al. [2] presented a new approach to enhancing the assessment of track geometry quality and rail roughness using train-track interaction simulation and wavelength content analysis. The simulations of dynamic track-vehicle interaction were also presented to assess vertical track geometry quality. It proved an effective tool to analyze the collected track geometry, and helped track engineers monitor track condition and make better track maintenance plans [3]. [PERSON] et al. [4] proposed a new method of track evaluation based on the observation of track's structural defects. The method could recognize the causes of track defects along the line and the correlations between geometric and structural defects in track. Methodologies for inspection and evaluation of slab-track quality conditions were developed, and correlations between geometry irregularities and structural conditions were found. The methods have proven to be efficient and practical tools to evaluate slab-track conditions, prioritize the required repair activities, and then make appropriate preventive maintenance decisions [5]. Several methods for evaluating track geometrical quality were presented and compared to each other in reference [6]. The results showed that the rate of track degradation varied according to the measurement method employed [6]. [PERSON] et al. [7] established the probabilistic transfer matrix model between dynamic and static TQI. The model verified that the probabilistic transfer matrix model could be used for quantitative reference in track fine adjustment and dynamic acceptance tests. [PERSON] [8] summarized the methods and criteria available for track geometric quality assessment. [PERSON] [9] established a risk assessment model for the underpass bridge subgrade based on a fuzzy comprehensive assessment method. [PERSON] et al. [10] developed a data-driven condition-based policy for the inspection and maintenance of track geometry and found that the track with low level of TQI still suffered from the risk of failure due to geometry defects. Track quality evaluation was based not only on track geometry but also on vehicle performance. A CNN-LSTM (the combination of the convolutional neural network and short-term memory) model was proposed to predict vehicle-body vibration, which was helpful in locating potential track geometry defects [11]. Although these evaluation methods can provide better qualitative results, they remain unable to adequately describe the change in service quality of track based on a single physical quantity. To tackle this issue, pioneering scientists and engineers have proposed comprehensive evaluation strategies that evaluate multiple indicators and multiple units at the same time using a relatively systematic, standardized method, such as DCE and static comprehensive evaluation (SCE).