hybrid model of vehicle and traffic for combined dynamic ...includes non-linear stochastic dynamical...

Int. J. Heavy Vehicle Systems, Vol. 24, No. 2, 2017 97

Copyright © 2017 Inderscience Enterprises Ltd.

Hybrid model of vehicle and traffic for combined dynamic analysis

T. Péter* Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME), Stoczek utca 2. 1111, Budapest, Hungary Email: [email protected] *Corresponding author

I. Lakatos Széchenyi István University, SZE KVJT and JKK, Egyetem tér 1. 9026, Gyor, Hungary Email: [email protected]

Abstract: Analysis and research of vehicle dynamics processes is a complex task that should be carried out taking into account the real traffic and environment. To this end, the examined hybrid model is the union of the vehicle dynamics model and the traffic network process model. In their relationship, drivers or automatic pilots play a decisive role, who take into account both systems alike.

Keywords: mathematical methods; hybrid model; complex environment and dynamic analysis; laboratory concept.

Reference to this paper should be made as follows: Péter, T. and Lakatos, I. (2017) ‘Hybrid model of vehicle and traffic for combined dynamic analysis’, Int. J. Heavy Vehicle Systems, Vol. 24, No. 2, pp.97–112.

Biographical notes: Tamás Péter is a Research Professor, obtained his MSc degree in Mechanical Engineering from the BME, Hungary in 1972, Mathematics Doctorate in 1978 and a Technology Doktor-PhD in 1998. He is Habilitated Doctor in Transportation Engineering in 2012. His research field includes non-linear stochastic dynamical systems, mathematical modelling, analysis and optimisation, computer-mathematics, equivalence classes of vehicle vibration systems, stochastic vehicle dynamics, road traffic and logistic models and their applications, mathematical analysis and optimal control of large scale road traffic networks. He is a Lecturer of the following courses: Computing, Applied Mathematics for Engineers, Mathematical Methods for PhD students.

István Lakatos is an Associate Professor, obtained his MSc in Mechanical Engineering of Vehicles from the BME, Hungary in 1989, Technology Doktor-PhD in 2003, Habilitated Doctor in 2013. His technical expert activities are mobility and vehicle industry, energy and environmental researches, development of hybrid-electric vehicles, analysis of truck accidents, on-board diagnostics of environmental systems and development of a new method for the analysis of diesel injection pumps. His expert activity is in connection with

98 T. Péter and I. Lakatos

higher education vocational training and standardisation of official trainings as a Professional Leader. He is Head of Department of Road and Rail Vehicles, SZE University, Hungary.

1 Introduction

The paper presents the hybrid model that can be studied with mathematical methods in an exact manner. The goal following from this is the establishment of a laboratory concept that allows the execution of a wide range of precise and high-speed tests for vehicles and traffic systems. By means of vehicle testing signals obtained from real traffic simulations, new quality large-scale datasets will get to be evaluable. Risk-free, any number of extreme or dangerous events occurring in real traffic can be taken into account and analysed.

The foundation of the theory of vehicle dynamics analysis goes back to the Euler-Lagrange equations based on the principle of variation of mechanics, as well as to Lagrange’s equations of the first and second kind during the application of various physical considerations relating to the elements.

Of course, in the case of the latter, the hybridity of the vehicle and the models is realised by itself based on the complexity arising as a result of additional modern technical implementations.

This study focuses, however, on the consideration of complex environment. Consideration of the real traffic processes cannot be ignored in vehicle dynamics analyses. The exact mathematical modelling of these mass phenomena in complex traffic networks belongs to a different kind of model group, namely to the scope of the Euler-network models. At the dynamic coupling of the two systems, it implies more hybridity that the driver or the automatic pilot must take into account the operation of both different types of systems as well.

The analysis and application of this hybrid system can be extended to more areas. The laboratory is capable to perform high-speed computer analysis on large datasets, analyses of network emission and vehicle dynamics loads, fatigue tests, road and infrastructure loads and also to detect and to analyse accident sites. It also creates a high level of info-communication environment.

The vehicle dynamics model in this paper, of course, is just one example. It can always be replaced by a variety of other, more complex dynamic models, which are more appropriate for the test, for example the more complex dynamics of steering, brakes and tyres. In the mathematical modelling, we consider the application of computer-algebra important owing to the high degree of complexity, as well. It is possible to apply a real vehicle or, for example, a complex software model in the laboratory, on which a diagnostic test can be performed with the introduction of a simulation dataset as an input taking into account the real-world scenario. It is also a new opportunity that there exists an exact traffic network model based on new model paradigms developed by us for the description of the complex processes.

Hybrid model of vehicle and traffic for combined dynamic analysis 99

2 The research area

Our research embraces the complex dynamic-environmental-traffic effects relating to vehicles as well as their modelling, measurement (El-Gindy, 1995) and new principled laboratory analysis. In terms of models, we equally examine the conventional non-linear vehicle dynamics and the intelligent driver model (IDM) dynamics (Derbel et al., 2012, 2013) as well, including examining them in traffic procedures (Peter and Bokor, 2010, 2011; Peter, 2012). The modelling environment elaborated by us (despite the fact that we constructed a macroscopic model) is suitable for describing the actual traffic process, as well, leaving from an arbitrary starting point to any other reachable point of the system taking into consideration real traffic processes, traffic lights, congestions, parking places, etc. (Peter and Basset, 2009). Besides the optimal directing of vehicle groups and route proposals, this procedure is also important in other fields; for example, in the field of intelligent vehicles and also in the field of dynamic analysis, sizing, environmental loading, emission testing of vehicles (Lakatos, 2007, 2015), because it is possible to perform very fast calculations for a large number of vehicles in various times and places. We have developed an intelligent model-making system that uses a computerised algebraic method for the complex, non-linear, dynamic modelling of vehicles. Using this, we minimise the time required for model-design on the human side. Thanks to the available IT equipment and several electronic and electromechanic parts built into vehicles, targets set for complexity are almost fully achievable today.

Researches and developments in process are also very important in the following fields: Design of diagnostic systems (El-Gindy et al., 2011); Practical analysis of the real effects on hybrid-electric cars (Lakatos et al., 2014, 2015); Research in the modelling procedures of the green transport network implementations; Establishment of methods for transport network development and design and Development of new measurement methods and technologies for hybrid-electric vehicles (HEVs).

When discussing network dynamics, we suggest using the methodology based on the theory of positive systems, where the model basically is a macroscopic model. The conditions of controllability and observability of positive systems cannot be clearly deduced from known methods of general systems (Luenberger, 1979). The problem is especially evident if we demand a non-negative range of values not only for the states but also for the control signals as well. For this reason, describing public traffic processes clearly as a positive system is not a trivial task. The control task in this case means that one has to control the system in such a way from one state to another that the fact that states can only take up non-negative values also during the state transition remains to be true (Varga and Bokor, 2007).

3 International modelling and simulation techniques

Modelling and simulation techniques developed for conventional, electric and hybrid vehicles cover a large spectrum. Comparing the conventional vehicles with the electric, hybrid and fuel cell vehicles, the latter use more electric parts (e.g., electric equipment, electronics, continuously variable transmission (CVT) and embedded drivetrain controls) (Horie, 2006; Muta et al., 2004).


The most important ones are physically based ‘Resistive Companion Form techniques’ and the ‘Bond Graph’ methods. The Bond Graph modelling is highly applicable for HEV systems as well as for hydraulic, mechatronic, thermodynamic and electric systems. Modelling and simulation proved to be effective in ‘multidomain’ systems too, including in this field the automotive systems as well (Filippa et al., 2005; Khemliche et al., 2004).

Compared with the operation of vehicle systems, physics-based models with dynamic simulations can ensure a very high precision using different time scales (Gissinger et al., 1995). Applying physics-based modelling and simulation allows us to calculate and model the dynamic performance and fuel economy, energy efficiency and emission in advance. Software packages used for the previously mentioned and also further applications are presented by Glielmo et al. (2003), Otter and Elmqvist (2001), Bárdos et al. (2016) and online papers. In the case of a wide range of vehicles, MATLAB Simulink (Arendt, 2011; Radan, 2008) and SimPowerSystems (SPS) (Nord, 2006; Seenumani, 2010) are popular and widespread to simulate special hybrid-electric-driven systems and vehicle models. dSPACE hardware loop platform concerns the simulation of governance models. Because of simulations, for example the charge level of HEV models’ energy storage system, setting their driving speed and defining the optimal load are important areas.

The acceleration of designing automotive systems and complexity management requires such advanced designing tools that automate the design processes of low-level details as well (Gao and Porandla, 2005; Struss and Price, 2004).

Particular characteristic features of complex system components need to be taken into consideration. Solutions depend on whether at certain levels, the components in question in the vehicle model are steady state (stable), quasi-stationary or dynamic.

For instance, ADVISOR (Markel et al., 2002; Wipke et al., 1999) model can be considered a steady state model, a PSAT model is quasi-stationary and PSAT Documentation, PSIM website and Virtual Test Bed (VTB) virtual test system models are dynamic.

The main advantage of applying steady-state and quasi-steady (quasi-equilibrium) models is quick calculation, whereas compared with dynamic simulations their disadvantage is inaccuracy.

Referring to Gao and Porandla (2005), it is shown that model-based design optimisation is a common requirement. So, the various modelling and simulation methods play a significant role in the case of electric and hybrid vehicles. Besides, knowing more about vehicle modelling tools is also inevitable (PSAT, applications, ADVISOR modelling approach and drivetrain modelling). The explicit finding of international research studies show that in case of HEV prototypes and analyses, modelling and simulation are an absolute necessity. This is especially true at the development of new hybrid drivetrain configurations and controls. The complexity of design of the new drivetrain holds serious challenges for the automotive researches and the increased demand for the research and modelling of embedded software also affects developmental efforts.

Effective diagnostics can also be challenging. Modelling plays an important role in the field of diagnostics when examining the cooperation between elements and components.


It is also important to elaborate and put in practice the methodology of new and modern diagnostic procedures. To achieve this, the Automotive Laboratory of the Széchenyi István University provides excellent facilities (Lakatos, 2007, 2015) and there is the possibility for further development taking into consideration the existing and continuously expanding international contacts.

4 Development of dynamic analysis. Block diagram

Expected results: We can realise the analysis of complex dynamic effects by developing a simulator. In Figure 1, we can see the analysis of the dynamics of environmental changes and the joint appearance of traffic, vehicle dynamics and human systems.

This has been represented in Figure 1 with the EULER NETWORK SYSTEM block in which the state variables are the ρ vehicle densities. This sub-system describes the vehicle density ρ(t) on the whole system. This model (1) is designed to analyse large networks and large traffic flows. This model considers the traffic density at the border of the system ρext(t), and the topology of the graph represents the road infrastructure, direction of edges, order of traffic signs and lamps, location and capacity of parking places. Compared with the classical macroscopic models, this model also considers the location and time-dependent emission.

Figure 1 Complex dynamic traffic analyser (see online version for colours)

The DRIVER MAN/ROBOT SYSTEMS block describes the behaviour of drivers, or in case of autonomous vehicles the robot drivers. It describes the choice of driving velocities as a function of location. Towards this, the traffic density ρ(t) of the location, e environmental parameters (retarding/accelerator factor, road quality, visual range, weather, etc.) and the 3D Lagrange systems state variables ( ), ( )z t z t& and the most important ( )z t&& are considered. The calculated v(t) velocity will be the input data for the EULER NETWORK SYSTEM for the particular time and position. The v(t) velocity and manufacturing data is considered in the EMISSION BLOCK. The v(t) velocity is also


considered as input parameter in the LAGRANGE SYSTEM block Figure 1 vehicles, which are 3D Lagrange systems. The z(t) state variables are related to the rest position displacement and angular displacements and the ( )z t& time derivatives.

For this, the process-models of traffic network developed by us are available (Euler network system). With this, we can examine the vehicle dynamics processes in a unified system with a vehicle-simulator software developed by us (Vehicle systems). This takes into consideration the processes and decisions of humans taking part (controlling/ influencing) in the model (Driver and man/robot systems) (At the moment, this means choosing the speed).

Joining the measurements of the simulator and the real traffic creates objective conditions for the following analyses (Péter et al., 2015; Szauter et al., 2014):

a observation of network processes

b dynamic analysis of concurrences and detecting critical places

c the analysis of the joint probability of occurrence of environmental states

d the analysis of accident hazards in the vehicle

e the analysis of safety critical effects in the traffic and analysis of human qualities.

4.1 Dynamic analyses of vehicles

Our research is going to be carried out or adapted to traffic on public road/in town, for example, in the case of modelling and analysing the dynamic impacts on road infrastructure (Figure 3).

The stochastic road profile process typical of the given road type (Allan and El-Gindy, 2013) – significant from the point of view of our examinations – consists of the total of realisations, in which t ∈ [0, T], ω ∈ Ω. [0, T] indicates the time interval of the survey, and Ω indicates the set of elementary events.

Likewise determining the traffic-dependent speed processes along the trajectories in case of which heading from t0 start-point the calculated whereabouts and wheel toe can be calculated from the speed function.

We use three-dimensional models for the dynamic testing of vehicles in traffic. In the course of modelling, the vehicle follows an arbitrarily chosen path (trajectory) and also takes the speed required by the traffic (Figure 2). The car body is considered to be a rigid mass in which angular oscillation appears around both the longitudinal and the transverse axes, and the mass points of the vehicle swing vertically. At the suspension, the characteristics of the shock absorber, spring characteristics and tyre spring characteristics are also non-linear according to reality. Our model has been created with the application of a computer-algebra system. As a result, further non-linear mathematical models can be generated automatically (Gissinger et al., 2002), which are suitable for taking into account other type of or spatial non-linear relations. Non-linear vehicle dynamic systems can be further extended with other dynamic sub-systems.

The altering speed along the trajectory has an impact on vehicle dynamics. Taking into account altering speeds along the trajectory has an impact on the vertical processes of the vehicle at the stochastic road profile excitation (Péter and Bellay, 1986).


Figure 2 Measurements made alongside the GPS-based trajectories (see online version for colours)

Figure 3 The analysis of nonlinear vehicle dynamic impacts on road infrastructure

Taking into account the vehicle’s motions and traffic events along the real road trajectories is also important (El-Gindy et al., 2001). These produce the test opportunities of several complex dynamic processes; moreover, such special situations can be


generated that emphasise the significance of the research and can be modelled with the above-mentioned method. Analysing vehicle geometry issues in case of advancing in circular geometry is a good example. The same can be stated in connection with impacts on longitudinal and vertical vehicle dynamics regarding energy consumption and emission. The combination of the total dynamic processes of the vehicle and the surface processes forms a highly complex dynamic system and its analysis also requires complex methods.

4.2 Of the applied dynamic network model

In our research, we apply the equations of state of the restricted network model of traffic (Peter and Bokor, 2010, 2011; Peter, 2012; Dömötörfi et al., 2016; Peter and Szabo, 2012), which contains an internal network element indicated by an x ∈ ℜn state vector with ‘n’ sectors located in a range. ‘m’ pieces of external sectors belong to the model, which have direct connections with one or some of the internal sectors.

Analysing and defining the processes of speed, it is a model assumption that vi(t)≥0 speed value is attributable to ∀xi(t), (xi ∈ [0,1], i = 1,2, …, n) network density state characteristics applying a constantly differentiable fi function according to xi:

( ( )).i i iv f x t= (1)

The power demand and emission of the engines of certain vehicles can be examined using the macroscopic network model to gain particular speed processes and a driver-vehicle model. The speed processes are suitable for model validation as well.

The analysis and definition of acceleration processes are carried out on the basis of having knowledge about the speed processes. The sectional longitudinal accelerations on the arbitrary ‘i’th section of the traffic model can be calculated in the following way:

( )d ( ( ))( ) ( ) ( ) 1 , 2, , ,

di i

i i i ii

f x tv t a t x t f x i n

x′= = ⋅ = ⋅ = …& & & (2)

where ( )ix t& value is determined by the state function.

5 Laboratory and modelling processes for expedited stress analysis

The purpose of our research is to uncover the advantages of the analysis of traffic and vehicle dynamics systems integrated in one model, and bringing the corresponding laboratory developments into action, in the course of which 3D vehicle dynamics modelling is carried out based on real trajectories and real traffic processes. On the basis of the complex traffic system model, trajectory processes concerning speed and acceleration can be obtained from the traffic model, while non-linear spatial modelling depending on traffic serves as a base for defining dynamic load on car-body, vehicle elements, the driver and the passengers. Analysing energy and environmental load together with modelling and analysing the impacts on road and infrastructure are other significant areas.

On the one hand, laboratory developments related to the research aimed at both generating large-scale network processes and activities storing them in the laboratory. On the other hand, further goals were considering real circumstances at diagnostic


measurements in the laboratory along with carrying out accelerated analyses under the previously ensured conditions. Thus, applying this integrated system, high-speed methods can be developed and the evaluation of a large volume of data can be carried out. Impact assessments are carried out in the fields shown in Figure 4.

The main goal in relation to the research of modelling procedures of the green traffic network is that the laboratory can be involved in the examination of the environmental effects of the vehicles’ composition at the optimisation of city loads and usage as well. With the new traffic network developing and planning methods, the actual abilities of the conventional and the HEVs can be analysed. New intelligent transport systems (ITS)

traffic control systems can be developed with regard to the operation and the rate of HEVs in the total vehicle composition.

The important fields were: the Development of Information and Communication Technology, the Complex Modelling and Diagnostics of Traffic and Vehicle Dynamics of large-scale public road networks for analysing real processes and their optimal control.

For the traffic networks, the examinations are performed taking into consideration the functioning network ITS systems. The examination of the vehicles’ actual use is carried out taking into consideration the operational features. For example, there is a possibility for such examinations, as well, in which communicating with the ITS the vehicles do less energy transformation and in this way the traversing results in the optimisation of the braking energy on a given route (Aifandopoulou et al., 2015).

Figure 4 Impacts of the characteristic features of traffic and general phenomena (see online version for colours)

The displays built into the asphalt are also special and important; they provide information about road conditions, temperature and traffic situations. On the basis of this, drivers are always aware about which lane to take, are there any decelerations, possible congestions, wet or icy road sections ahead. The storage capacity of the batteries, the increase in the vehicle’s range, the installation, modernisation and development of charging points are also further important areas of research.

At the University of Salerno, Italy, Professor Gianfranco Rizzo and his team have developed a kit, which can convert your conventional car into a hybrid using solar panels (patent WO2011125084). In our department, we have carried out similar research and developed a prototype with small commercial vehicles. In Hungary, several local


governments deal with the problem of converting public transport and communal vehicles into electric-driven vehicles. These trends determine the directions of research in Hungary. The research on charging electric and hybrid vehicles without contact has been of prime importance recently. One of its realisations is building an intelligent motorway.

Exploiting the already existing complex domestic modelling and methods of analysis, we need to extend our examinations over further fields that give special and new results enabling us to contribute to international research results. On the basis of an international overview (Heinitz and Fritzlar, 2014), Hungarian complex research related to real operation and urban environment, which develop techniques and technologies of diagnostics, are timely and provide novelty (Csonka and Csiszár, 2016). Moreover, they apply modern control theory and optimal design methods. These fields of research are regarded as innovative both in Hungary and in the EU.

In the case of further automotive examinations and planning research fields concerning hybrid and electric vehicles, the following target areas can be articulated:

1 Utilising our modelling processes and commencing further research in the field of:

Designing diagnostic systems of electric and HEVs taking into account the effects of real traffic dynamics (Elkafoury and Negm, 2016).

• diagnostic analyses for the purpose of operation

• measurements specified by law

• enacting of laws, amplification of existing ones or modification

• elaboration of the operational diagnostics of hybrid and electric vehicles.

5.1 Environment analysis

This examination integrates traffic processes and the examination of the dynamic processes of vehicles travelling on the networks. In this way, the complex analysis is done by taking into consideration the real traffic processes in a unified dynamic system for 3D hybrid vehicles as well as conventional vehicles that are suitable for a stochastic dynamic calculus using the emission blocks belonging to them. When calculating, environmental load emissions are examined using real traffic simulations.

Real environment and the exact consideration of the processes makes development necessary (Stasiak-Betlejewska, 2015; Zefreh and Török, 2016; Zöldy and Török, 2015). In cities as surface transport is the biggest producer of emission. In case of some pollutants, its ratio could be up 42–80% of all emissions. A new principle modelling – modern control – and new measurement system will be introduced, where the connection of those systems will be also investigated.

From the large-scale model used by us, based on former validations, we are able to extract the progression profiles, which are, just like in reality, complicated and complex and consist of a sequence of accelerations, decelerations and frequent stops (Bede and Péter, 2010, 2011; Péter and Fazekas, 2014). Real processes are such and deviate from processes applied in conventional laboratories on chassis dynamometer. The new European driving cycle (NEDC) approved by the EU uses namely such laboratory tests of which the starting points were the traffic data of two European capitals, Paris and Rome. In reality, however, the dynamic load of vehicles and their harmful emissions greatly


depend on the driving style and is significantly affected by the given traffic situation. On the basis of the above, there can be significant deviations in different regions and countries. Today, for example, more and more places use closed-loop traffic control. Processing the information provided by the detectors placed in various junctions, they regulate the operation of traffic lights so that it is optimal regarding the flow of the traffic. In the project, we elaborate the new diagnostic and control methods taking into consideration the real vehicle dynamics-traffic processes and the optimisation of environmental load. We determine the expectable emission by taking into consideration the characteristic speed profiles and identifying the categories of the vehicles passing. We validate the methods by a sequence of measurements.

5.2 Safety and accident analysis

Traffic procedures and regrettable events, i.e., accidents related to them, are determined by the following boundary conditions:

• the existing traffic regulations (Varga and Bokor, 2007)

• structure, establishment and transparency of the road network (Peter, 2012)

• parameters characterising the environment of the vehicle at a particular spot (visibility (good visibility distance and surface illumination [Lux]), rapid/slow change of strength of illumination, weather conditions, road quality, road direction, lane width, lane material, surface roughness/smoothness of lane and quality of roughness and spectrum of road profile) (Péter and Bellay, 1986; Peter and Szabo, 2012)

• type and technical condition of the vehicle and parameters influencing safety (Gissinger et al., 1995)

• characteristic features of vehicle drivers (Derbel et al., 2012, 2013) and their individual characteristics influencing safety.

Of course, the above-mentioned list does not follow the order of importance. Each positive feature can save life, whereas each shortcoming may have serious consequences (Brenac et al., 2015; Yi et al., 2014; Csonka and Csiszár, 2016; Esztergár-Kiss and Csiszár, 2016). The list is not definitive; it can be supplemented with further important correlations gained from the present analysis. Traffic procedures are basically influenced by the environment and its change (Esztergár-Kiss et al., 2016). An environmental analysis is a determining factor in case of all vehicle types regarding safety and accidents. With respect to the research carried out by us, we can differentiate between static and dynamic types. The pattern analysis belongs to the static, whereas the quasi-accident analysis falls within the group of dynamic analyses. It helps us take the right measures, which, on the one hand, weaken unwanted effects, and on the other hand establish proportional conditions contributing to increase safety.

This type of analysis means not only a further thorough analysis of road accidents (Oulha et al., 2016) but also new research activities aiming at automatising accident measurement and recording exact physical parameters. To explore more distinct correlations and to minimise accidents, we find it important to highlight the following:

As regards accident reports, it is advisable to encourage the record of physically measurable parameters that are as exact as possible and to make sure that any calculations


of them are reconstructed. It is necessary to improve and automate measurements, to send the results to the appropriate database automatically, with the help of info-communication tools, to ensure quick on-the-spot actions and to cut down on on-the-spot administration work. It is particularly important to analyse the joint effects in an exact way since different negative effects being present at the same time can increase the probability of an accident. According to the above, the space and time points of the network where joint negative effects are very likely to occur have to be explored. Also, they continuously have to be recorded in the database for that particular network and they continuously have to be eliminated. The overall results of the above lead to the establishment of an optimal closed-circuit control. This, on the one hand, improves the parameters of the complex traffic system, and on the other hand, regulates them dynamically (timely) in such a way that it continuously increases traffic safety.

Estimating the probability of future accidents, analysing the environment scientifically and keeping the likelihood of unfavourable joint events to a minimum are all priorities.

Mathematical modelling covers both the improvement of static calculation methods and the analysis of dynamic effects. The improvement of static analyses can basically serve for the complex analysis of environmental effects.

The analysis of environmental parameters can be carried out with the introduction of the so-called incidence matrix K[kij] proposed by us, which refers to the incidence (relationship) between the environmental parameters at a particular accident site. The elements of the matrix are the following: kij = I × J; (i, j = 1, 2, …, n; n refers to the number of environmental parameters taken into account, ‘I’ stands for ith, ‘J’ refers to jth. These are binary variables.)

We would like to emphasise the importance of detecting near-miss accidents alongside the trajectories and further developing this analysis, detection could be made automatic.

A near-miss accident is such an event where actual situations, physical changes have happened to the road, environment or the vehicle but this did not yet result in an accident. These situations are higher in number than the actual accidents and they give us warnings and help us detect danger. How can accident risk be detected? Where and why are they most likely to happen? The most effective way of preventing accidents is to eliminate causes of accidents before the accidents actually happen. For prevention, a system approach accident prevention process analyser can be built in the system. An up-to-date, unified traffic network IT database and a regulated organisational system of connections on top of this offers several advantages and new opportunities. During its development, it is very important to use info-communication assets and that the information is simple and readily accessible. From the classic data supply cycle, the valuable information is missing that contains significant circumstances about near-miss accidents.

6 Conclusions

In case of electric and hybrid-electric-driven vehicles, it is important to uncover the connection between modelling and laboratory measurements.

There are new possibilities for developing and validating an accelerated method for the analysis of environmental load alongside the city trajectories. Today, the decrease in


emission and engine power by using the modelling of harmful emission is a very important field of research (Lakatos, 2007, 2015).

The project and the adjoining researches result in the elaboration of such new industrial diagnostic methods that are expanded onto the environmental research and safety effect analysis. Equipment development is realised through the cooperation with our international research and industrial partners. These results simultaneously have a positive effect on the development of new diagnostic systems, on the public road transportation in cities as well as on the environment and safety.

The project fits into the row of cutting-edge international research (Pariota et al., 2016; Roque and Masoumi, 2016; Simha, 2016; Tettamanti et al., 2016; Törő et al., 2016). Beyond the international research, in compliance with the above-mentioned and advised, the analysis of the network and the vehicle dynamic processes travelling on networks can be effectuated along with its application in laboratory environment, which can create a new measurement environment. On the one hand, the new method can calculate the speed and acceleration processes on any trajectory of road network models of arbitrary size. On the other hand, these network processes are integrated with the dynamic processes and their analyses of vehicles travelling on networks.

Each intersection and road-section has an accident pattern frequency, a distribution and a weighted distribution. As regards the research of accident prevention, it is important to determine the set of critical patterns. As all these patterns belong to past accidents, their joint structure can provide us with significant information. With the knowledge of the above, the graph of traffic network can be investigated with respect to the environmental parameters. It has to be examined how each environmental parameter influences the patterns, too. Apart from this, the distance of the patterns has to be defined as well. This way we can explore both the distance and the correlation between these parameters. The knowledge of the above and that of the conclusions, which can be drawn from them, e.g., ITS, using modifiable road signs, can reduce the number of future accidents.

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hybrid model of vehicle and traffic for combined dynamic ...includes non-linear stochastic dynamical...

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