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    Wyświetlanie 1-4 z 4
    Tytuł:
    An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity metrics
    Autorzy:
    Wang, Yang
    Xiao, Yu
    Lai, Jianhui
    Chen, Yanyan
    Powiązania:
    https://bibliotekanauki.pl/articles/949848.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    missing traffic data
    similarity metrics
    K-nearest neighbour method
    stochastic characteristics
    metoda porównywania danych
    metryki podobieństwa
    metoda najbliższego sąsiada
    cechy stochastyczne
    Opis:
    Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.
    Źródło:
    Archives of Transport; 2020, 54, 2; 59-73
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear
    Ocena wpływu zmiennych objaśniających na dokładność predykcji dobowego dopływu do oczyszczalni ścieków wybranymi modelami nieliniowymi
    Autorzy:
    Szeląg, B.
    Bartkiewicz, L.
    Studziński, J.
    Barbusiński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/205349.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wastewater treatment plant
    data mining
    random forest
    forecasting inflow
    k-nearest neighbour
    Kernel regression
    oczyszczalnia ścieków
    wydobywanie danych
    las losowy
    dopływ ścieków
    modelowanie
    k-najbliższy sąsiad
    regresja Kernela
    Opis:
    The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.
    Celem pracy jest ocena możliwości zastosowania różnych metod data mining do modelowania dopływu ścieków do komunalnej oczyszczalni ścieków. Do opracowania modeli statystycznych metodą wektorów nośnych, lasów losowych, k – najbliższego sąsiada i regresji Kernela wykorzystano szeregi pomiarowe dobowych wartości opadów deszczu, stanów wody w odbiorniku oraz dopływów do komunalnej oczyszczalni ścieków w Rzeszowie. Z obliczeń wykonanych metodami SVM, RF, k-NN i K wynika, że dla modeli z jedną zmienną objaśniającą opóźnioną o dobę w stosunku do wartości dopływu, najlepsze wyniki otrzymano modelem autoregresyjnym bazującym na metodzie k-NN a najgorsze regresją Kernela. W przypadku modeli z dwoma zmiennymi objaśniającymi najmniejsze wartości błędów uzyskano, dla modeli uwzględniających dopływ ścieków i całkowitą wysokość opadu deszczu z jednodobowym opóźnieniem; najlepsze wyniki uzyskano metodą RF a najgorsze regresji Kernela. Dla modeli z dwiema zmiennymi objaśniającymi, ale trzema sygnałami wejściowymi, najmniejsze błędy dopływu ścieków do OŚ uzyskano metodą SVM, a najgorsze regresji Kernela. Z wykonanych symulacji stwierdzono, że w większości przypadków najmniejsze wartości błędów dopływu ścieków do oczyszczalni otrzymano metodą SVM a największe metodą K. W przypadku najprostszego modelu z jednym sygnałem wejściowym opóźnionym o 1 dobę najlepsze wyniki obliczeń uzyskano metodą k-NN, a w dwóch przypadkach modeli, gdzie ujęto 2 sygnały wejściowe, najlepsza okazała się metoda RF.
    Źródło:
    Archives of Environmental Protection; 2017, 43, 3; 74-81
    2083-4772
    2083-4810
    Pojawia się w:
    Archives of Environmental Protection
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways
    Autorzy:
    Lee, Y.
    Wei, C.-H.
    Chao, K.-C.
    Powiązania:
    https://bibliotekanauki.pl/articles/223569.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    accident on freeway
    accident duration
    effect evaluating
    correlation
    artificial neural networks
    k-nearest neighbour method
    wypadek na autostradzie
    czas trwania wypadku
    ocena skutków
    korelacja
    sztuczne sieci neuronowe
    metoda najbliższego sąsiada
    Opis:
    Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.
    Źródło:
    Archives of Transport; 2017, 43, 3; 91-104
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Investigation and Study of Mode Splitting in Near Field Inductive Communication Systems
    Autorzy:
    Thanh, H. D.
    Agbinya, J. I.
    Powiązania:
    https://bibliotekanauki.pl/articles/226565.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    splitting frequencies
    modes
    nearest and second neighbour interactions
    coupling coefficient
    conventional resonant frequency
    power efficiency
    transfer function
    Opis:
    Frequency splitting is a near field inductive communication phenomenon where the resonant frequency divides into many separate frequencies or to different modes. In this paper, we show that this phenomenon depends on the coupling coefficients or the natural response of the circuit by using the circuit theory to derive these splitting frequencies. Also, the rules for the general matrix that is used to solve for splitting frequencies are also demonstrated clearly. Mode splitting is observed for peer-to-peer, three coils and four coil systems due to the existence of the nearest and second neighbour interactions. In particular, two, three and four modes have been analysed for two, three, and four coil systems respectively. However, the number of modes for these systems can be changed according to the degree of coupling. The differences in the resultant splitting frequencies with and without the second neighbour interaction are shown in the simulation results. Furthermore, we assess the system performances regarding to power efficiency through the inductive transfer functions. Besides, either coupling coefficients at resonance or the simplified transfer functions in some specific scenarios can be obtained by having an insight into these transfer functions. Finally, we recognise and propose that splitting frequency phenomenon can be deployed to transmit signals at many frequencies concurrently.
    Źródło:
    International Journal of Electronics and Telecommunications; 2013, 59, 2; 185-194
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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