PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Fac Transport Silesian Univ Technology
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
traffic flow; LSTM; short-term prediction; multi-step ahead strategies
Kaynak
Scientific Journal of Silesian University of Technology-Series Transport
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
111
