Introduction

An urgent need in intelligent systems (IS) application for a number of problem areas is not in doubt. Among the basic IS applications are those given in the monograph [1]: medicine, engineering, transport system and others. Among the fundamental IS components we distinguish the data and knowledge base. In this paper we will concentrate our efforts on the base construction.

When developing the design standards for the highways we should take into account the regional features of the geographic territories. It is performed through the method of road-climatic zoning. The method serves as a basis for the development of building regulations, directives and guidelines valid in Russia [2,3], China [4,5], USA [6], Germany [7], Great Britain, Sweden [8] and in other countries, including such neighboring countries as Kazakhstan [9], Belorussia [10], Kyrgyzstan [11]. According to the building regulations [2,3] the Russian Federation territory is zone differentiated and divided into 5 roadclimatic zones, which are differing sufficiently in terms of the complexes of nature-climatic and geoengineering conditions. In their turn, the zones are divided into 9 subzones due to the road industry standards [9] and into 13 subzones due to the set of rules [3]. Depending on the position of the road section under design in one or another zone and subzone the road designers make technical decisions, providing safe and convenient traffic according to the requirement stated in [2,3].

A number of researchers in their papers [12][13][14][15] point out that the existing special position of the zones and subzones boundaries does not allow to provide the level of operational reliability of the highways due to the operability criterion since the position of the zones and subzones are not substantiated sufficiently. This situation is especially inherent to Western Siberia and Far East. That leads to an increase in financial and labor resources for maintaining and restoring the required technical condition of highways. Thus the research of the new approaches to the road-climatic zoning design is rather actually. The specificity of data and knowledge to solve the problem of the road-climatic zoning requires the new methods of the data and knowledge representation. Taking into consideration is proposed to choose intelligent instrumental software IMSLOG (IIS IMSLOG) [16,17] for IS construction of road-climatic zoning of territories (IS RCZT).

Hereafter we give a description of a complex matrix model for the data and knowledge representation for the IS RCZT. The IS RCZT is based on test methods of pattern recognition and cognitive graphics tools.

Complex Matrix Model for Data and Knowledge Representation

For the first time we suggest to represent the complex model by 3 types of the following matrix models of data and knowledge representation [18,19]:

1. An extended matrix model including an extended matrix of descriptions and a matrix of distinguishing. The extension is performed due to additional columns introduction to the matrix of descriptions [19]. The matrix of descriptions sets the objects description within the space of characteristic features. The additional columns correspond to compulsory features: zones, subzones, road districts, supporting point of the investigated territories. The present paper deals with the research results on Western Siberia territory. The columns of the extended matrix of descriptions correspond to characteristic features, represented by the 3 groups of factors. Those factors constitute the geographical complex of the territory: zonal, intrazonal and regional factors. The columns of the matrix of distinguishing correspond to the zones, subzones and road districts. We use integer features in the model.

2.

A matrix of expert knowledge description without compulsory features and a matrix of distinguishing. The columns of the matrix of distinguishing, as well as those of the matrix of distinguishing of the extended matrix model, correspond to the zones, subzones and road districts. Here we also use integer features.

3. A partial matrix model, consisting only of the extended matrix of description of the investigated territories under recognition. Now we concentrate on the elements description of the matrices under consideration. The integer values of the characteristic features including grouped ones and the compulsory features are the elements of the extended matrix of objects' description (Q e ). The group characteristic features are split into the features of the integer values, which correspond to a certain partition intervals of the feature under study. A column of the matrix Q e corresponds to each characteristic features. A row of the matrix Q e corresponds to the stronghold for which the values of characteristic features are determined. Thus, the element of the matrix Q e is the value of the integer characteristic features, including compulsory one. This feature correspond to a certain supporting point [18]. Note that the compulsory features are not used in regulations revealing. They are implemented only for the mapping of the zones, subzones and road districts.

Integer values of the classification features of three types are the elements of the matrix of distinguishing (R e ). We restrict our study to the diagnostic matrix of distinguishing. For the matrix under study each subsequent column splits the previous one into the classes of equivalency. Due to the methodology given in [20,21], we will use the three classification features of diagnostic type. The 1st feature corresponds to the zones, the 2nd oneto the subzones, the 3rd oneto the road districts.

We note that we need the 2nd matrix model due to the incomplete information on the zones, subzones and road districts. Such information is contained in the 1st matrix model. The rows of the matrix of description and the matrix of distinguishing are fulfilled by the highly qualified experts in the problem area. Matrix fulfillment with data is performed by the colleagues of the Automobile roads building department of the Tomsk State University of Architecture and Building.

The learning sample is represented by the extended matrix model. In the learning sample some combinations of the classification features could not be represented. Therefore, the dimensions of the matrix of description and the matrix of distinguishing could exceeded sufficiently the dimensions of the matrix of the extended matrix model, fulfilled beforehand. This is due to the absence of a number of combinations of the classification features values in the learning sample.

The extended matrix of description of the territories under recognition for the partial matrix model is fulfilled by the highly qualified experts. They use the reference data and the data acquired during field and/or laboratory research. The research results could be transmitted both to the system's users and to the enterprises, interested in the road-climatic zoning research results.

The decision making about the supporting point correspondence to a certain zone, subzone and road district we perform using the two aforementioned matrix representations (extended matrix and the one based on the expert knowledge) based on the rules of the total decision making with use of IS RCZT. The architecture IS RCZT is presented in the publication [22].

Data and knowledge structuring. Bases of a database and knowledge construction

The basis of the information technology of road-climatic zoning of territories is IS RCZT. To create the IS we united our efforts with our colleagues. Together with specialists in the cognitive science and experts in road-climatic zoning we have structured the data and knowledge on road-climatic zoning. The structuring has been performed based on the complex matrix model of data and knowledge representation, described in section 2.

A list of characteristic features with indicating their values for the matrices of description is given in Table 1. The characteristic features are grouped ones beginning with characteristic features z 10 . Symbolic characteristic features, intervals of the integer characteristic features partitions as well as the real characteristic features are coded by numbers. The number 20 (limiter) is used only for the sake of size reduction of the data and knowledge matrix representation. In table 1, the value of an integer feature is not greater than 8. To the above mentioned characteristic features we add 4 compulsory features (zone, subzone, road district, supporting point). The compulsory features are applied to 3 zones only, since Western Siberian territory has been investigated partly. We pointed out 1 subzone and 1 road district in the 1st zone, 2 subzones and 7 road districtsin the 2nd zone and 2 subzones and 3 road districtsin the 3rd zone.

                                                                                                                                                                                       R e R e Q Continuation table

Illustrating example of matrices Q e , R e and R' descriptions is given in Fig. 1. The matrices correspond to partial knowledge description. We use only a part of the characteristic features space and its values. For the matrix model, filled in by the experts, the expert knowledge on the four zones, all the subzones and all the road districts are included. The fragments of description and distinguishing the matrices are represented in Fig. 2.

There are examples of usage of some visualization tools including cognitive graphics tools. The free-distributed open-street maps (OSM) [23] with information layer overlay for the presentation of common information are proposed. Information layer presents road regions with borders and some information about its. This information is a number of zone and subzone which are determined for road region. The proposed visualization tool is presented on Fig. 3.

In doing so note that for the mapping of decision-making results with usage of cognitive graphics tools we use 3-simplex for the zones representation and 2-simplex for subzones representation in case when the number of subzones equals 3 [24].

The information layer is denoted by number 1. It is a transparent layer over the map. The thin black lines separate the different road regions. The different color tones are used for labeling the different zones (red color tone is used for zone 2, blue color tone is used for zone 3). Each color of the road region in every zone is unique color gradation from zone base color given from color transformation in the hue-saturation-bright palette (HSB palette). The wide black lines are used to separate the different zones. Hatching over road region shows subzone type. Only 3 hatching types are used and only 2 types from them presented on Fig. 3. Description for all used colors and hatching is presented in the legend (see Fig. 3) and it is denoted by number 2. There is a list of all used hatchings and presented subzones in the upper part of the legend. The list of all used zones and correlated road regions is in the bottom part of the legend.

                                                                                                                                                                                       R e R Q

The information window for a road region is shown after click on a road region presentation. This window contains full information about a region. This information contains the road region name, 3-simplex and 2-simplex as information about proximity to specific zone (left 3-simplex) and subzone (right 2-simplex). The OUI (road region) is displayed as the circle with a big radius. Objects of learning sample are displayed as circles with smaller radiuses. The distance from the object OUI to an edge is directly-proportional to proximity of the object to the pattern corresponding to the edge. Distances of an OUI to edges are displayed as color lines. Color of an OUI (or objects from a learning sample) is mapped to the pattern which belongs to the concrete object. Mathematical foundations of the visualization of these objects with use of n-simplex are given in [25,26]. We revealed the different types of regularities on the basis of algorithms proposed by A. Yankovskaya and realized in IS RCZT. The revealed regularities allowed to reduce the features space from 27 to 11. That, in turn, has led to reduction of quantity of revealed feature values on 59 %.

We also verified the decisions-making using the generated supporting point descriptions proposed by A. Yankovskaya. The research results showed the IS RCZT development will lead to reduce significantly the expenditure and the cost of field and laboratory works on the territories under investigation. That, in its turn, will essentially reduce time expenses the specialists of road branch for the identification of zone, subzone and road district of the territory under investigation.

The proposed approach on road-climatic zoning of territories will allow to provide the required level of operational reliability of the highways.

Conclusion

The analysis of domestic and foreign standards of designing and building of highways is given. The advisability of creation intelligent systems road-climatic zoning of territories is substantiated.

For the first time we proposed the complex matrix model of data and knowledge representation for road-climatic zoning. It has allowed to carry out structurization of the data and knowledge on the road-climatic zoning. Complex matrix model is represented by the 3 matrix models: the extended matrix model that includes the extended matrix of description and the matrix of distinguishing; the matrix of knowledge description and the matrix of distinguishing filled with highly qualified experts; the partial matrix model consisting of the extended matrix of description of the territories under study.

It is created a prototype of the intelligent system of road-climatic zoning. On the basis of extended matrix representation it is created data and knowledge base using the research results on natural and climatic conditions of Western Siberian regions. The base of date and knowledge was created highly qualified experts.

For the 1 st and the 2 nd matrix representations of data and knowledge are revealed and is eliminated at a finding of intersections of objects descriptions from different patterns.

Results of a research prototype approbation of road-climatic zoning of the territories intelligent system have shown as follows: reduction on 59 % of necessary number of characteristic features for decision-making on reference of territory part under study to this or that zone, a subzone and road district. Application IS RCZT will decrease significantly the expenditure and cost on the field and laboratory research of the territories under study. That will also save the reduce time expenses of the road branch specialists.

The proposed approach on road-climatic zoning of the territories will allow to provide demanded level of operational reliability of again under construction and reconstructed highways and first of all in regions with the poorly developed network of highways.