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Computational Intelligence and Neuroscience
Volume 2014, Article ID 470758, 10 pages
Research Article

High Speed Railway Environment Safety Evaluation Based on Measurement Attribute Recognition Model

1School of Automation, Nanjing University of Science & Technology, Nanjing, Jiangsu 2100984, China
2East China Jiaotong University, Nanchang, Jiangxi 330013, China

Received 20 July 2014; Revised 22 September 2014; Accepted 25 September 2014; Published 9 November 2014

Academic Editor: Yongjun Shen

Copyright © 2014 Qizhou Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to rationally evaluate the high speed railway operation safety level, the environmental safety evaluation index system of high speed railway should be well established by means of analyzing the impact mechanism of severe weather such as raining, thundering, lightning, earthquake, winding, and snowing. In addition to that, the attribute recognition will be identified to determine the similarity between samples and their corresponding attribute classes on the multidimensional space, which is on the basis of the Mahalanobis distance measurement function in terms of Mahalanobis distance with the characteristics of noncorrelation and nondimensionless influence. On top of the assumption, the high speed railway of China environment safety situation will be well elaborated by the suggested methods. The results from the detailed analysis show that the evaluation is basically matched up with the actual situation and could lay a scientific foundation for the high speed railway operation safety.

1. Introduction

According to the high speed railway safety operation research carried out in the laboratory of Nanjing University of Science and Technology, the high speed railway operation failure directly caused by bad environments accounts for 29% from July 2011 to December 2012, and comparatively the speed railway accidents in severe weather take up 81.4% of the total ones at the same time. The above statistics thus give us a better understanding of the fact that the bad weather has significant effects on the high speed railway safety operation.

In China, the current researches of environment impact on high speed railway can be mainly divided into the following two categories: first, the macrodisaster emergency prediction and warning system design and second, the microenvironmental factors impact mechanism analysis. As to the first one, Sun et al., Wang et al., and Tao et al. have outlined some key problems of high speed railway environment safety, such as alarm threshold, the layout of monitoring points, train controlling mode, and the basic component of high speed railway warning system [13]. Xiao et al., Calle-Sánchez et al., and Wang et al. also made an analysis of the potential factors which caused railway disaster from the following four aspects: personnel, equipment, management, and environment [46]. And Miyoshi and Givoni introduced analytic hierarchy process to set up railway environmental risk assessment system [7]. In the aspect of environmental factors impact mechanism, Zhou and Shen, Ling et al., and Lee et al. have made a specific discussion of such impact mechanism such as earthquake, wind, and other disasters in high speed railway from the view of engineering construction [810].

The comparison of the studies from abroad and home reveals that the researches of the high speed railway environment safety have been repeatedly carried out in an extremely earlier time and have been carefully studied by a lot of foreign researchers. Many countries have built up their own efficient high speed railway disaster warning system such as the Hokkaido and Shinkansen disaster warning system in Japan, which leads many other countries to conduct the earthquake prediction. For instance, France is now in possession of its Mediterranean earthquake monitoring system and Germany owns high speed railway disaster prevention system. Though the disaster monitoring systems of JingJingtang, Fuxia, and Wuguang have been already built in China, Zhang and Zeng contend that all the systems can be still well improved on the basis of the original ordinary railway disaster warning system [11] because there is a certain gap between foreign and China’s high speed railway disaster warning systems after a relatively fair comparison.

Through the comparison of present researches between domestic and foreign, we can find that the domestic high speed railway disaster prevention is now in a transition from theory to practice, while foreign high speed railway disaster prevention system has been at a relatively perfect stage. Therefore, it is an urgent mission for the domestic researchers to make an intensive effort to the theory research of high speed railway disaster protection and system construction process so as to promote China high speed railway operating safety level.

2. High Speed Railway Environmental Impact Evaluation Indexes

2.1. High Speed Railway Index System of Environmental Impacts

The operational problems of the high speed railway are mainly caused by such uncertain factors as raining, thundering and lightning, horizontal wind, earthquake, and so forth, whose degree of intensity will directly decide the degree of danger posing to the high speed railway operation safety. The analysis of the characteristics of various environmental factors in the process of high speed railway operation in recent years and the conclusion of the mechanism of different environmental factors on high speed railway safe operation are presented in Table 1.

Table 1: High speed railway mechanism analysis of environmental impact factors.

Besides the six factors listed in Table 1, problems in the high speed railway are also being influenced by debris flow and water and rock burst. However, given the complexity of geological conditions and the difficulty of data acquisition, we only use average annual rainfall, average annual maximum lightning density, annual disaster monsoon winds, average disasters incidence of monsoon, average magnitude grade, average incidence of earthquakes, average annual maximum snow depth, average highest temperature, and average minimum temperature as the environment factor evaluation index, which are shown in Figure 1.

Figure 1: High speed railway environmental impact evaluation indexes system.

It is necessary to be mentioned that the usual climate environment will not exert any influence upon the operation of high speed railway, except typhoon, sandstorm, blizzard, and earthquakes, while high or low temperatures have significant influence on the operation of high speed railway. Therefore, with the exclusive of the average rainfall in Figure 1, other factors represent the extreme climate environment. Each environmental factor evaluation index calculation formula and specification is shown in the following equations.

Average annual rainfall level is where is the maximum rainfall in the th  year (mm) and is the number of the years.

Maximum lightning density is where is the thunder lightning happening in certain region in the th year (time) and is the area of a city or region (m2).

Disasters wind speed is where is the speed of the th disaster wind (m/s) and is the area of a city or region (Km2).

Average wind happening is where is the total times of the disaster wind happening (time).

Average magnitude grade is where is the magnitude of the th earthquake (degree) and is the total times of the earthquake (time).

Average magnitude happening is

Average high and low temperature are where is the highest temperature in the th year (°C) and is the lowest temperature in the th year (°C).

Average snow depth is where is the deepest depth in the th year (cm).

2.2. Demarcation of the Environmental Climate Factor Affected Threshold (Modify)
2.2.1. Threshold under Horizontal Wind Influence

The representative research about the effects of horizontal wind on high speed railway train running is conducted preciously in Japan, which calculates the horizontal wind velocity under the condition of critical capsize under different running speed by wind tunnel experiment and takes the critical wind speed as the threshold of Shinkansen disaster warning (Table 2). China’s high speed railway line train CRH series are characterized by the similar features with those of Japanese train in the shape and the axle load. Therefore, the Japanese Shinkansen warning horizontal wind speed is adopted as the influencing factors of high speed railway in our country horizontal wind threshold.

Table 2: Japanese Shinkansen winds threshold.
2.2.2. Threshold under Earthquake Influence

In terms of the research results at home and abroad, the calculation of earthquake alarm threshold () of high speed railway can be referred to as the following formula (9): where is the maximum lateral acceleration threshold ensuring that the normal operation of the train can withstand without orbit (Gal), is the maximum dynamic response coefficient of various structures of railway under different seismic wave excitation, and suggestive value is 2.55.

Researches show that whencase  Gal, the train begins to pour;case  Gal, the train will completely overturn.

Therefore, we define  Gal and  Gal as the threshold of strong impact and general impact on the safe operation of the high speed railway train. And the earthquake magnitude threshold of high speed railway operation is calculated by different value method, which is shown in Table 3.

Table 3: Earthquake magnitude threshold of high speed railway ( takes 2.55).
2.2.3. Threshold under Rain Influence

Domestic railway department limits the train running speed based on the size of the rain.

If the rain runs moderately which lasts 12 (or 24) hours and the rainfall capacity arrives at 10.0 mm–22.9 mm (17 mm–37.9 mm), its speed should be reduced.

If the rain runs in a heavy rainy day which lasts 12 (or 24) hours, and the rainfall capacity reaches 23.0 mm–49.9 mm (33.0 mm–74.9 mm), the railway lines are supposed to be blocked and the train operation is supposed to be prohibited.

For the sake of dimensional consistency, we can turn the hour rainfall volume into annual rainfall volume by the following method: it is universal knowledge that our country’s rain season will experience a period of 3 months that can be calculated by 12 rainfall times; thus, we categorize the annual rainfall volume into 900 mm, 1980 mm, and 2970 mm, respectively, as the moderate rainfall city, heavy rainfall city, and the storm rainfall city. Accordingly, we can calculate rainfall threshold effects on high speed railway compared with the provisions of the railway departments in Table 4.

Table 4: The annual rainfall threshold of high speed railway.
2.2.4. Other Environmental Factors Threshold

The current theoretical researches both at home and abroad pay less attention to the lightning, snowing, temperature, and snowfall which will definitely bring some influences on the characteristics of the high speed railway operations. Because it is difficult to set up a uniform standard to measure the factors, experts suggest that the reference value and the method of combining qualitative analysis can be employed to determine what degree of lightning, snow, and temperature influencing the high speed rail threshold. The environment impact assessment index of high speed railway can be discriminated as in Table 5.

Table 5: High speed railway environment impact assessment index discrimination safety threshold.

3. High Speed Railway Environmental Impacts Attribute Recognition Model

Attribute recognition model is in essence the problems of multidimensional space between sample and attribution, which is proposed by professor Cheng and has been widely used in evaluation and classification. The sample space has been calculated in 31 provinces and autonomous regions in our country, among which each has been given nine high speed rail environmental impact indexes as , and the th environmental impact assessment index value in the th region is expressed as . is defined on a sample space ordered split sets, where the environmental impact is divided into five progressive ways as serious, severe, moderate, mild model, and no effect. An ordered set of split is defined as , which is in accordance with the relationship as . Each ordered set is then to be split into a collection of environmental evaluation threshold segmentation classes. To make a clear illustration of the ordered stripe set, a standard form has been set up as follows: where :  .

The value of the sample properties has attributes characterized by a sample and expressed as , among which the measurement function is the core of attribute recognition model. Hu et al., Yan, and Xiao et al. make an analysis of the usual linear discriminated function, whose accuracy is less than that of a nonlinear function. Therefore, the recent researches have found that the normal distribution function is used much more frequently, while other nonlinear functions are often being regarded as an attribute identification measure function [1214]. However, the normal distribution function as a measure function has its shortcomings because data should be standardized before handling bias and the separated index weights should also be determined. What is more, the last attribute recognition result is relative.

However, there is no certain way to evaluate the relative importance of objective indicators in a fairly way. The essence of attribute recognition is to determine the attributes space similarity and methods used to calculate the spatial distance are Euclidean distance, Ming distance, and Mahalanobis distance. Todeschini et al. and Kayaalp and Arslan assert that the Mahalanobis distance has the advantages of weakening the correlation between impact indicators and automatic weight in the index calculation based on data changes [15, 16].

Therefore, in order to compensate for normal function, we use Mahalanobis distance as the measurement function to build the attribute recognition model.

Step 1 (Mahalanobis distance between sample and attribute class calculations). Assuming the sample has been an area of environment evaluation, the sample Mahalanobis distance with the attribute class is where , representing the th region environment factor evaluation vector, and , representing each classification criteria value of environmental factors on the properties class vector. = the covariance matrix between and is where .

Step 2 (standard attribute measurement value calculations). Generally, the greater the similarity of Mahalanobis distance, the smaller the measurement value. Therefore, assuming that Mahalanobis distance between area and attribute class has been derived , the standard attribute measurement value is

Step 3 (sample class attribute recognition). Class attribute identification is in accordance with the confidence value : where normal circumstances take .

Step 4 (security score calculations). Assuming each evaluation category corresponding score of , then the combined attribute security score is

4. Case Studies

4.1. Chinese Regions Environment Overview

Five domestic environmental factors such as rainfall, lightning, wind, temperature, and earthquake in recent years are collected from 2002 to 2012 as the basic assessments data [17] as is shown in Table 6. (The data of rain factor is summary of annual average rainfall in various regions, the data of thunder and lightning factors comes from various regions’ monitoring reports, and the data of wind factor represents the influence extent by monsoon in various regions.)

Table 6: Chinese regional environment situation in recent years from 2002 to 2012.

The program of MATLAB is employed to work out the estimation. The specific method is made by 31 districts samples and each has 9 indexes. Then we constitute the sample matrix . There are five characteristics consisting of particularly serious, severe, moderate, mild, and no effect, whose intermediate values will be made up of attribute matrix ; that is,

Use the function of MATLAB to work out the Mahalanobis distance between the districts sample and the attribute class: where is the Mahalanobis distance matrix between the sample and the attribute and is representing the use of the function Mahalanobis distance to work out the distance of matrix.

Then make confidence level , and each of the area’s environmental attribute recognition values and attribute classification can be obtained as that in Table 7.

Table 7: Chinese regional environment impacts attribute recognition value of high speed railway.

The calculation results in the above table show that the environmental safety situation of Xinjiang, Sichuan, Heilongjiang, and Jilin belongs to serious category, which takes up 12.9%. The situation in the Medium level areas accounts for 32.2%, such as Heilongjiang, Hebei, Liaoning, Jiangsu, and Guangdong, and that of the 17 areas such as Beijing, Tianjin, Guizhou, Gansu, and other regions belongs to slight level, which accounts for 54.9%. It is notable that, in addition to Sichuan, the high speed railway environment impacts in the serious level areas are mostly distributed in coastal areas and northern regions, while Chinese abdominal regions are mostly in the medium and light level (see Figure 2).

Figure 2: Chinese environment impacts of high speed railway distribution.

For further analysis, security score in the areas of the serious level should be calculated by means of grading criterion. All kinds of scores are clarified in Table 8.

Table 8: The attribute recognition of high speed railway classification score.

The calculation results show that Sichuan has the lowest scores of 62.460, followed by 63.280 in Heilongjiang and 63.489 in Xinxiang, and Yunnan has the highest score of 72.23.

4.2. High Speed Railway Line Safety Environment Analysis

There are 25 high speed railway operational lines in our country currently, which constitute the total mileage of 10192 kilometers. Most of the high speed railways are located in southeast of China, where complex geological accidents such as landslip, earthquake, and other geological disasters take place frequently. The high speed railway environment safety situation is clearly illustrated in Table 9.

Table 9: The environment impacts of high speed railway lines distribution.

The Jinghu line, Fuxia line, and Huning line mainly go across regions of Beijing, Tianjin, Jinan, Nanjing, Shanghai, Hangzhou, and so on. Most of these regions are located in the medium impacted or light impacted areas where raining and storm happen frequently. Thus, we have to pay attention to the influence of heavy rain and storm.

The Wuguang line and Guangshengang line mainly go cross such cities as Guangzhou, Foshan, and others in Guangzhou. These cities are vulnerable to the typhoon from coastal regions, which will affect the progress of the high speed railway.

The Yiwan line, Suiyu line, and Dacheng line go cross Wanzhou, Suining, Shizishan, Chengdu, or other cities of Sichuan province. High speed railway in these areas will suffer seriously from the tough environment, and we should pay attention to prevent cost and loss from landslip and earthquake.

5. Conclusions

Firstly, the paper makes a detailed analysis of the impact from such environment factors as rainfall, earthquake, lightning, wind, and snow on the high speed railway safety mechanism. On the basis of the analysis, the evaluation index system of safety has been established and the threshold of high speed railway environmental safety has been calibrated by citing the results of domestic and abroad. At last, the high speed railway uncertain safety attribute recognition model is created based on the Mahalanobis distance with the features of dimensionless and weak effect correlation, which simplifies the comprehensive calculation process.

Secondly, the examples of China’s 31 provinces and regions in the paper are selected to make the data of the high speed railway environmental safety much more convincing. The degree of danger is divided into five categories, among which the cities that the high speed railways pass in the serious category account for 16.1%, those in the middle class account for 38.7%, those in the mild category account for 38.7%, and those in the no effect category account for 6.51%. It deserves our attention that cities of Xinjiang, Sichuan, Guangdong, Heilongjiang, and Liaoning belong to the serious category, whose evaluation results are basically consistent with the environmental characteristics. And the results have a certain theoretical reference for the “135” planning of high speed railway operation safety in Xinjiang and other areas.

At last, the analysis of the high speed railway environmental safety is directed to the aspect of weather, geology, and other factors. However, considering the complexity of data acquisition, the high speed railway evaluation index has its own drawbacks in this paper. It is needed to introduce more methods and factors into the evaluation of the high speed railway safety operation to facilitate the further researches.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


The authors are very grateful to the anonymous referees for their insightful and constructive comments and suggestions that have led to an improved version of this paper. The work also was supported by National Nature Science Funding of China (Project no. 51178157), The Basic Scientific Research Business Special Fund Project in Colleges and Universities (no. 2011zdjh29), National Statistical Scientific Research Projects (no. 2012LY150), “Blue Project” Projects in Jiangsu Province Colleges and Universities (no. 201211), and Youth Fund Projects in Jiangxi Province Department of Education (no. GJJ13314).


  1. L. Sun, H. Zhong, and G. Lin, “An overview of earthquake early warning systems for high speed railway and its application to Beijing-Shanghai high speed railway,” World Earthquake Engineering, vol. 27, no. 3, pp. 14–16, 2011. View at Google Scholar
  2. L. Wang, Y. Qin, J. Xu, and L. Jia, “A fuzzy optimization model for high-speed railway timetable rescheduling,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 827073, 22 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  3. H. Tao, N. Hong-Xia, and F. Duo-Wang, “Speed contron for high-speed railway on multi-mode intelligent control and feature recognition,” Telkomnika, no. 8, pp. 2069–2074, 2012. View at Google Scholar
  4. X. Xiao, L. Ling, and X. Jin, “A study of the derailment mechanism of a high speed train due to an earthquake,” Vehicle System Dynamics, vol. 50, no. 3, pp. 449–470, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Calle-Sánchez, M. Molina-García, J. I. Alonso, and A. Fernández-Durán, “Long term evolution in high speed railway environments: feasibility and challenges,” Bell Labs Technical Journal, vol. 18, no. 2, pp. 237–253, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Wang, W. Xu, F. Wang, and C. Jia, “A cloud-computing-based data placement strategy in high-speed railway,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 396387, 15 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Miyoshi and M. Givoni, “The environmental case for the high-speed train in the UK: examining the London-Manchester route,” International Journal of Sustainable Transportation, vol. 8, no. 2, pp. 107–126, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Zhou and Z. Shen, “Progress in high-speed train technology around the world,” Journal of Modern Transportation, vol. 19, no. 1, pp. 1–6, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Ling, X. Xiao, and X. Jin, “Study on derailment mechanism and safety operation area of high-speed trains under earthquake,” Journal of Computational and Nonlinear Dynamics, vol. 7, no. 4, Article ID 041001, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. K. S. Lee, J. K. Eom, J. Lee et al., “The preliminary analysis of introducing 500 km/h high-speed rail in Korea,” International Journal of Railway, vol. 6, no. 1, pp. 26–31, 2013. View at Google Scholar
  11. W. Zhang and J. Zeng, “A review of vehicle system dynamics in the development of high-speed trains in China,” International Journal of Dynamics and Control, vol. 1, no. 1, pp. 81–97, 2013. View at Google Scholar
  12. Q.-Z. Hu, H.-P. Lu, and W. Deng, “Evaluating the urban public transit network based on the attribute recognition model,” Transport, vol. 25, no. 3, pp. 300–306, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Yan, “Multigranulations rough set method of attribute reduction in information systems based on evidence theory,” Journal of Applied Mathematics, vol. 2014, Article ID 857186, 9 pages, 2014. View at Publisher · View at Google Scholar
  14. Z. Xiao, W. Chen, and L. Li, “A method based on interval-valued fuzzy soft set for multi-attribute group decision-making problems under uncertain environment,” Knowledge and Information Systems, vol. 34, no. 3, pp. 653–669, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Todeschini, D. Ballabio, V. Consonni, F. Sahigara, and P. Filzmoser, “Locally centred Mahalanobis distance: a new distance measure with salient features towards outlier detection,” Analytica Chimica Acta, vol. 787, pp. 1–9, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. N. Kayaalp and G. Arslan, “A fuzzy bayesian classifier with learned mahalanobis distance,” International Journal of Intelligent Systems, vol. 29, no. 8, pp. 713–726, 2014. View at Google Scholar