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Tytuł:
Performance analysis of LSTM model with multi-step ahead strategies for a short-term traffic flow prediction
Autorzy:
Doğan, Erdem
Powiązania:
https://bibliotekanauki.pl/articles/2134868.pdf
Data publikacji:
2021
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
traffic flow
LSTM
short-term prediction
multi-step ahead strategies
przepływ ruchu
prognozowanie krótkoterminowe
strategie wieloetapowego wyprzedzania
Opis:
In this study, the effect of direct and recursive multi-step forecasting strategies on the short-term traffic flow forecast performance of the Long Short-Term Memory (LSTM) model is investigated. To increase the reliability of the results, analyses are carried out with various traffic flow data sets. In addition, databases are clustered using the k-means++ algorithm to reduce the number of experiments. Analyses are performed for different time periods. Thus, the contribution of strategies to LSTM was examined in detail. The results of the recursive based strategy performances are not satisfactory. However, different versions of the direct strategy performed better at different time periods. This research makes an important contribution to clarifying the compatibility of LSTM and forecasting strategies. Thus, more efficient traffic flow prediction models will be developed and systems such as Intelligent Transportation System (ITS) will work more efficiently. A practical implication for researchers that forecasting strategies should be selected based on time periods.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2021, 111; 15--31
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of different approaches in traffic forecasting models for the D-200 highway in Turkey
Autorzy:
Dogan, E.
Korkmaz, E.
Akgungor, A. P.
Powiązania:
https://bibliotekanauki.pl/articles/196318.pdf
Data publikacji:
2018
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
traffic forecasting
SARIMA
differential evolution algorithm
artificial bee colony algorithm
prognozowanie ruchu
algorytm ewolucji różnicowej
algorytm sztucznej kolonii pszczół
Opis:
Short-term traffic estimations have a significant influence in terms of effectively controlling vehicle traffic. In this study, short-term traffic forecasting models have been developed based on different approaches. Seasonal autoregressive integrated moving average (SARIMA), artificial bee colony (ABC) and differential evolution (DE) algorithms are the techniques used in the optimization of models, which have been developed by using observation data for the D-200 highway in Turkey. 80% of the data were used for training, with the remaining data used for testing. The performances of the models were illustrated with mean absolute errors (MAEs), mean absolute percentage errors (MAPEs), the coefficient of determination (R2) and the root-mean-square errors (RMSEs). It is understood that all the models provided consistent and useful results when the developed models were compared with the statistical results. In the models created separately for two lanes, the R2 values of the models were calculated to be approximately 92% for the right lane, which is generally used by heavy vehicles, and 88% for the left lane, which is used by less traffic. Based on the MAE and RMSE values, the model developed by the ABC algorithm gave the lowest error and showed more effective performance than the other approaches. Thus, the ABC model showed that it is appropriate for use on other highways in Turkey.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2018, 99; 25-42
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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