Abstract
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.
Highlights
The significant increase in vehicle numbers and travel demand raises traffic density on roads to critical levels
The developed model results were slightly better than the standard Long ShortTerm Memory (LSTM) model and significantly better than other methods
There is an important research gap in this field. To close this important gap, this study investigated which multi-step forecasting strategy works efficiently with an LSTM model in the traffic flow prediction task
Summary
The significant increase in vehicle numbers and travel demand raises traffic density on roads to critical levels. In these studies, LSTM's traffic flow prediction performance was compared with other methods, or its structure was updated to improve its performance, or a hybrid model was developed using LSTM and other popular approaches In these studies, performance analysis of using a multi-step forecasting strategy with LSTM for traffic flow prediction was not performed. There is an important research gap in this field To close this important gap, this study investigated which multi-step forecasting strategy works efficiently with an LSTM model in the traffic flow prediction task. The investigation using different forecasting strategies with LSTM in terms of the traffic flow prediction problem, and the analysis of these results will contribute to the solution of this inconsistency. The recommendations that emerged from this study and plans for further studies are included in the conclusion
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