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A hybrid model predictive control for traffic flow stabilization and pollution reduction of freeways


Transportation Research Part D: Transport and Environment (2018)


Alfréd Csikós, István Varga, Katalin M. Hangos


Abstract: In this work a control system is developed and analyzed for the suppression of moving jamwaves and the reduction of pollutant concentrations near motorways. The system is based on the second-order macroscopic freeway traffic model METANET, joined by an emission dispersion model, introduced in a previous work of the authors. For the control tasks dedicated controllers are designed, both using the nonlinear model predictive control method. The control objectives require a distinction in the utilized control measures, thus different controllers are designed and used in predefined control modes. The first mode of the controller is responsible for keeping pollutant concentrations below prescribed limits under stable conditions. The second mode of the controller is working in case of a shockwave threat, aiming for traffic stabilization. Between the control modes switching is based on an appropriate rule set that satisfies the stability of the controlled system. The hybrid controller structure is realized by a finite automata. A complex case study is presented for the evaluation of the suggested controller.


Extensions of the Activity Chain Optimization Method


Journal of Urban Technology (2018)


Domokos Esztergár-Kiss, Zoltán Rózsa, Tamás Tettamanti


Abstract: For the optimization of daily activity chains a novel method has been elaborated, where flexible demand points were introduced. Some activities are not necessarily fixed temporally and spatially, therefore they can be realized in different times or locations. By using flexible demand points, the method is capable of finding new combinations of activity chains and choosing the optimal set of activities. The optimization algorithm solves the TSP-TW (Traveling Salesman Problem – Time Window) problem with many flexible demand points, which resulted in high complexity and long processing times. Therefore, two extensions were developed to speed up the processes. A POI (Point Of Interest) search algorithm enabled to search demand points in advance and store them in an offline database. Furthermore GA (genetic algorithm) was applied and customized to realize lower optimization times. During the implementation, three different transportation modes were defined: car, public transport, and combined (public transport with car-sharing opportunity). The simulations were performed on arbitrarily chosen test networks using Matlab. Promising test results were obtained for all transportation modes with total travel time reduction of 10-15 percent. The application of the extended optimization method produced shorter activity chains and decreased total travel time for the users.


Pattern recognition based speed forecasting methodology for urban traffic network


Transport (2018)


Tamás Tettamanti, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos, István Varga


Abstract: A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.


Single loop detector data validation and imputation of missing data


Measurement (2018)


Ádám Török, Mohammad Maghrour Zefreh


Abstract: The data derived from loop detectors are of great importance in terms of traffic monitoring and analysis. These data may contain many holes or incorrect values due to equipment malfunctions and communication faults that may produce unreliable results. These holes (missing samples) or incorrect values (bad samples) might be problematic for any algorithm that uses the data for analysis. In this paper, a method is described that detects bad data samples gathered by the loop detectors and imputes the best available samples in order to fill the holes caused by the bad declared samples. The diagnostics algorithm proposed in this paper is based on the statistical analysis. Unlike the previous approaches, this algorithm considers the time series of many samples, rather than basing decisions on single samples. The imputation algorithm proposed in this paper uses the “good” declared samples from the historical data of the investigated loop detector to fill the holes caused by the bad declared samples. This detection and imputation process allows the algorithms that use loop data to perform analysis without requiring them to compensate for missing or incorrect data samples.


Sustainable Urban Transport Development with Stakeholder Participation, an AHP-Kendall Model: A Case Study for Mersin


Sustainability (2018)


Szabolcs Duleba, Sarbast Moslem


Abstract: Public transport development decisions are generally made by local government representatives or managers of the local transport company through a top-down procedure. However, if the implications do not meet the demand of the public, the improvement cannot be considered as sustainable and in a long range, correction is necessary. This paper aims to introduce a new model which is capable of supporting public transport development decision making by examining the preferences of different stakeholder groups (passengers, potential passengers, and local government) and creating an acceptable coordination for an ultimate, sustainable decision. In the model, Analytic Hierarchy Process is applied, combined with Kendall rank correlation and an extra level of stakeholder significance in the decision. A case study is also presented on the situation of a Turkish city: Mersin. The results show, that by the application of the new model, a more integrated and thus more sustainable solution can be created for the public transport problems of the city, and by this, probably more citizens can be attracted to use public transport modes which might result in decreased CO2 emissions.


Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise


Journal of Advanced Transportation (2018)


Ádám Török, Mohammad Maghrour Zefreh


Abstract: Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (LWA) and subsequently the noise level at receiver (LA) were estimated by “ASJ RTN-Model.” Having estimated and for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (SEL) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.


Development of an ontology-driven, component based framework for the implementation of adaptiveness in a Jellyfish-type simulation model


Journal of Ambient Intelligence and Smart Environments (2017)


Gábor Bohács, Angéla Rinkács


Abstract: Simulation modelling has an ever increasing importance for complex systems. Manufacturing and related material flow or logistic systems are typical fields of application. Latest trends such as Cyber-physical systems and Industry 4.0 give a significant boost to simulation modelling as these require a digital model of the system. Complex manufacturing and related material flow systems are subject to frequent changes and pose a Big Data problem, which raises stronger requirements regarding self-adaptiveness. Conventional simulation models are to be adapted only via user interaction. Previous research steps have concentrated on the establishment of a novel simulation model structure, the so called “Jellyfish” model which unifies layout and process-type simulation models. Visualization of both aspects simultaneously enables interacting users to better understand the systems’ operation compared to the conventional models. The current paper focuses on the adaptive capability of the new model. We have concentrated on the hardest type of adaptation, the structural adaptation. In this paper, an ontology-driven component based approach is presented and explained further through an example. Application of automated ontology-matching in simulation environment is a novel approach enabling the simulation model to adapt its structure without the necessity of manual interaction.


Model of an integrated air passenger information system and its adaptation to Budapest Airport


Journal of Air Transport Management (2017)


Csaba Csiszár, Enikő Nagy


Abstract: Air passengers use several functions of passenger information systems during their journeys. Inadequate information management may generate uncertainties. Experience shows that the quality of information provision can be improved by the integration of data sources and information services. Development of increasingly integrated solutions requires the use of more abstract modelling methods. Nevertheless, these complex methods have only been partially elaborated and are yet to be adequately published. Therefore, our aim was to develop several methods for analysis and modelling. We applied an approach, where organisations, functions, data groups, and information terminals (interfaces) have been considered. All model components, service functions and their correspondences have been identified and visualised in matrix format. Finally, our method is applied for the case of Budapest Airport. Based on this research, useful subsystems, most notably integrated passenger mobile applications, may be developed in the future.


Static analysis and reanalysis of quasi-symmetric structure with symmetry components of the symmetry groups C3v and C1v


Engineering Structures (2017)


Péter Béda, Péter Harth, Pál Michelberger


Abstract: This work aims to discuss the static analysis and reanalysis of near-regular (quasi-symmetric) structures and to develop further the analysis of these structures based on the principle of general connect (coupling) and the group representation theory with symmetry groups C3v and C1v. During the analysis, only one-dimensional irreducible representations of different symmetry groups are taken into account, and we investigate the effect of these to the static analysis and to local modification method, as well. For these structures any general load can be decomposed into cyclic and asymmetric load, furthermore at most four symmetry components SSS, AAA (C3v) and S, A (C1v) (S – symmetric component, A – antimetric component) are used in this paper. The mentioned methods were applied for static analysis with two perpendicular symmetry planes; this paper examines the effect of three symmetry planes and considers the applicability in case of more-than-three symmetry planes for analysis


The modelling and design process of coordination mechanisms in the supply chain


Journal of Applied Logic (2017)


Gábor Kovács, Katarzyna Grzybowska


Abstract: The process description languages, which are used in business, may be useful in logistics processes. The planning, organisation, direction and the control of the logistics processes might be more efficient if these formal languages are applied. During the logistics processes, many problems might arise, which should have already been addressed in the planning phase. In our days, the symptomatic treatment is a common practice, but it does not provide predictability. The obvious solution would be process control, in order to handle the main sources of faults and to give a correct list of what needs to be done during the logistics process. The process description languages may be useful not only in standardisation, but they may also help to avoid losses. Simulation experiments, on the basis of built model, also allow for the elimination of problems, standardisation and the limitation of losses. The aim of the article is a discussion of selected coordination mechanisms in the supply chain, its modelling in the form of a reference, as well as a discussion of the simulation experiment with the use of the FlexSim tool.