PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION
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...
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| Format: | Article |
| Language: | English |
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Silesian University of Technology
2021-06-01
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| Series: | Scientific Journal of Silesian University of Technology. Series Transport |
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| Online Access: | http://sjsutst.polsl.pl/archives/2021/vol111/015_SJSUTST111_2021_Dogan.pdf |
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| author | Erdem DOĞAN |
| author_facet | Erdem DOĞAN |
| author_sort | Erdem DOĞAN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-907e15e4cf274a71859a9a21172ec7d8 |
| institution | DOAJ |
| issn | 0209-3324 2450-1549 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Silesian University of Technology |
| record_format | Article |
| series | Scientific Journal of Silesian University of Technology. Series Transport |
| spelling | doaj-art-907e15e4cf274a71859a9a21172ec7d82025-08-20T02:57:52ZengSilesian University of TechnologyScientific Journal of Silesian University of Technology. Series Transport0209-33242450-15492021-06-01111153110.20858/sjsutst.2021.111.2PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTIONErdem DOĞANIn 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.http://sjsutst.polsl.pl/archives/2021/vol111/015_SJSUTST111_2021_Dogan.pdftraffic flowlstmshort-term predictionmulti-step ahead strategies |
| spellingShingle | Erdem DOĞAN PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION Scientific Journal of Silesian University of Technology. Series Transport traffic flow lstm short-term prediction multi-step ahead strategies |
| title | PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION |
| title_full | PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION |
| title_fullStr | PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION |
| title_full_unstemmed | PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION |
| title_short | PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION |
| title_sort | performance analysis of lstm model with multi step ahead strategies for a short term traffic flow prediction |
| topic | traffic flow lstm short-term prediction multi-step ahead strategies |
| url | http://sjsutst.polsl.pl/archives/2021/vol111/015_SJSUTST111_2021_Dogan.pdf |
| work_keys_str_mv | AT erdemdogan performanceanalysisoflstmmodelwithmultistepaheadstrategiesforashorttermtrafficflowprediction |