# An Incentive Factor-Based Dynamic Comprehensive Evaluation on a High-Speed Railway Track
## 1 Introduction
Previous research has presented various methods of SCE, including subjective weighting (e.g., AHP, binary fuzzy comparison method (BFCM)), objective weighting (e.g., the technique for ordering of preference by similarity to ideal solution (TOPSIS), Delphi, entropy weight method [12], coefficient of variation, factor analysis, principal component analysis (PCA)) and combination weighting (e.g., AHP-TOPSIS [13], AHP-grey relational analysis process [14], entropy weight-based lower confidence bounding approach [12]). [PERSON] [15] first applied the analytic hierarchy process to determine weight indicators. [PERSON] et al. [16] proposed the data envelopment analysis (DEA) and used it to evaluate intellectual capital [17], railway [18], etc. [PERSON] et al. [19] combined the AHP and the DEA to evaluate the economic benefits of energy-saving technology applications. Besides, there are other methods of comprehensive evaluation, such as the fuzzy comprehensive evaluation method, artificial network neural evaluation, and grey comprehensive evaluation. [PERSON] et al. provided an overview of the concepts, advantages, and disadvantages of these methods [20]. Because of subjectivity, the evaluation of the same object based on different evaluation criteria would lead to different results. Moreover, objective weighting is affected by random data error or the lack of data even though the method avoids the influence of subjectivity. To overcome these limitations, combination weighting methods have been proposed, which can be divided into three categories: Combination of the evaluation process, combination of the evaluation results, and combination of the evaluation method itself [21]. If a time series is added to SCE, the evaluation problem becomes DCE. [PERSON] [22] used a time series solid data table to record the comprehensive evaluation results for the evaluated object over a sustained period of time. [PERSON] et al. [23] evaluated the livable city using a combination of the dynamic information entropy and fuzzy comprehensive evaluation. The positive and negative incentive lines were set up according to the evaluation of the objects, and the predicted values were obtained based on a first-order one-variable grey model. The rewarded and punished incentive lines were introduced to evaluate the objects [24]. [PERSON] et al. [25] proposed a method of DCE based on the gain level incentive, which played a role in stimulating and guiding the dynamic development of evaluated objects. [PERSON] et al. [26] presented a mapping algorithm for weight determination and introduced acceleration and acceleration index to reflect the variation in index values over time. This algorithm could better determine the weight information's ranges caused by fluctuations in the index in different stages, and it could coordinate the adjustments of evaluation index values and weight information. The existing literature also provides other methods, including double incentives dynamic comprehensive [27], dynamic evaluation method based on TOWA operator, dynamic evaluation method integrating SOM and K-means clustering algorithm [28], and dynamic comprehensive evaluation method based on the accelerated genetic algorithm [29]. A review of the literature shows that a large number of studies have focused on SCE methods. The TQI [30; 31] is the primary method for comprehensive evaluation of high-speed railway lines. This method evaluates the average quality of track segments based on statistical characteristic value, and it is used as the key index to evaluate the state of track geometry [8], This paper describes the TQI method in detail in the third section. TQI is calculated as the ratio of the traced space curve length to the track segment length in the U.S. [10]. In other countries, TGI and J are another two common indexes used to evaluate track geometry [32]. Other computing modes used in a different country have been described by the authors of [6].