Impact Factor : 2 . 265

The progress in communication, computer and information sources technologies supplemented by new DSS (Decision Support Systems) tools giving new until now inaccessible information / knowledge about real traffic situations [4-6,19]. It opened up new horizons and opportunities for development of professional ITS (Intelligent Integrated Transportation Systems) systems offering much more advanced intelligent management, supervision and control system-wide actions. Such systems offer great deal of real-time responsiveness, high operational flexibility/ intelligence that may eliminate the weak points of the classical transportation systems and the need for human operator’s interventions. It is obvious that the efficiency of advanced control logic ( in intelligent control in particular) depends on the quality of real-time information and knowledge feedback used to recognize the multi-criteria dedicated control problem specifications and adequate solutions. In this context the intelligent recognition, analysis, diagnosis and prediction of traffic situations appears to be the most important prerequisite in implementing the sophisticated control concepts removing the existing weak points of classical transportation systems. [1-5, 11-12, 18-22]. INTRODUCTION The present weak points to be generators of high costs in transportation systems are as follows:  Lack of system reactions on unpredictable traffic incidents generating very high various costs.  Lack of adequate signal timings reactions for heterogeneous traffic conditions on intersection approaches, arteries links/segments and network sub-areas. [7-10]  Lack of multi-modal control actions dedicated to different traffic modes (e.g. public transport, individual traffic, logistics vehicles) in urban areas. [15-17,23].  Lack of recognition and exploration of favorable traffic situations of “synergic and opportunity” types for get high operational profits under low costs [18-21].  Implementation of historical off-line single criteria control methods of fixed –time/ vehicle actuated type which are not based on the exploration of fundamental feedback mechanism to be a crucial in the control area. The suggested remedies for significant improvement of ITS systems efficiency are as follows:  Proposals of dedicated ITS systems generated by professional system platform HITS [18,20 ].  Exploration of new possibilities offered by new enabling technologies (communication, information, sensors, computing etc.). [19]  Proposals of new intelligent, integrated ITS systems services based on exploration of intelligent technologies and modern tools [8, 10-13].  Proposals of multi-criteria intelligent control methods based on intelligent recognition, analysis, diagnosis and prediction of traffic situations (e.g. traffic incidents) by intelligent supervision and monitoring ITS system layer [1-5, 14, 17, 21].  High flexibility preferences of individual ITS system layers activities and ITS structure-related interlayers cooperative activities in generation of system-wide multi-time horizon, multi-criteria compatible management, adaptation, optimization, supervision/monitoring and control actions. In the paper the ITS system option dedicated to intelligent supervision and system monitoring layer is proposed which conditioning the intelligent flexible system functionalities. In particular the adequate traffic situations recognition and diagnosis to be the most important prerequisite of implementing dedicated control actions by bottom direct control layer is proposed. In the case of multi-criteria real-time traffic control it is realized by dedicated structure of control preferences. Therefore the crucial problem for the intelligent supervision and control layers concerns the recognition of adequate to observed and predicted traffic situations, structure of preferences for control actions. The above adequacy is conditioning by the traffic situations related quality / representatives [Adamski. , 2(7): July, 2015] ISSN 2349-4506 Impact Factor: 2.265 Global Journal of Engineering Science and Research Management http: // www.gjesrm. com © Global Journal of Engineering Science and Research Management [76] of the selected traffic information and implementation of dedicated intelligent tools. These requirements are realized respectively by multi-criteria traffic situations related selection of representative data sources and communications media and traffic situations markers based dedicated recognition, analysis and diagnosis tools. RANDOM FIELD MODELS OF THE TRAFFIC FLOW The road traffic is a good example of the 2-D (space x time) phenomena therefore the random fields (RF) as traffic control models were first proposed in [14, 16-17, 21]. The basic intelligent activities of the bottom supervision and control layers first of all deals with the estimation for the proposed classes of Vector Random Field (VRF) e.g. traffic volumes, speeds, headways, trip times) of theirs parameters relations like correlation function, entropy or traffic situations markers on the available data basis (e.g. data taken at scattered locations in the detection zones). In particular to illustrate the importance of the spatial information the optimal control problem with the RF of entropy real-time estimated parameter used as a measure of smoothness of the vehicle flow was presented in [710 ] for normal traffic situations. In the case of traffic incidents the entropy parameter can be used to detect and integrate the compatible episodes in the traffic incident and after incident diagnosis to propose the adequate structure of preferences for multi-criteria control of a mix of normal/ abnormal traffic situations. The attractiveness of entropy parameter as an traffic situations marker in normal situations is related to the fact that the entropy control with more sophisticated entropy definitions provides a means trading off some performance and robustness features like in 2 H and  H optimal control problems. Additionally, the time-domain entropy notion for LTI systems is equivalent to the usual frequency domain criterion, therefore some practical references to the expected power (energy) spectral density of stochastic representations are possible [10]. In general many of traffic features make sense only when one sees the traffic processes as spatio-temporal interactions i.e. represented by spatio-temporal model in the form: } ), ( : ) , ( { T t t D s t s    (1) Where s and t denote location in detection zone ) (t D and time indexes of the random observation ) , ( t s  . The particular cases of (1) i.e. purely spatial/temporal models representations are as follows: } ) ( : ) ( { n E t D s s    ; } : ) ( { 1 R T t t    . The detection zone D(t) may vary with time and take different forms; e.g. it may be continuous region, finite/countable set of sites (lattice models), random set of random events. The space and time coordinates can be on a par, in that they take values in sets of comparable cardinality, and the process may show stochastic invariance under transformations of space as well as time. On the other hand, it is along the time axis that one has "causal flow". A spatial process (i.e. a random function of s or of s and t) is often referred to as a Random Field (RF). In Fig.1 different degree (p) neighbors NH(N) of vehicles on two lanes are presented. Neighborhood (nearest) of NX denoted by NH(N) is the set consisting of N itself and all neighbors of members of N. The boundary N is the complement N in NH(N) (i.e. N=NH(N)\N), (see Fig.1.). To introduce the probabilistic formalism note that when the field variable can only take a discrete set of values at each site, than probabilities ] [ N P  and conditional probabilities ] | [ 2 1 N N P   have their definitions, at least for finite N. Then, at least for finite N the quantity ) ( P is well-defined.