Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction
Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/5/4/34 |
| _version_ | 1797381073526587392 |
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| author | Vienna N. Katambire Richard Musabe Alfred Uwitonze Didacienne Mukanyiligira |
| author_facet | Vienna N. Katambire Richard Musabe Alfred Uwitonze Didacienne Mukanyiligira |
| author_sort | Vienna N. Katambire |
| collection | DOAJ |
| description | Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable. |
| first_indexed | 2024-03-08T20:46:02Z |
| format | Article |
| id | doaj.art-c1ef15d4d4b941d19502256a9ef2efeb |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-08T20:46:02Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-c1ef15d4d4b941d19502256a9ef2efeb2023-12-22T14:09:08ZengMDPI AGForecasting2571-93942023-11-015461662810.3390/forecast5040034Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima JunctionVienna N. Katambire0Richard Musabe1Alfred Uwitonze2Didacienne Mukanyiligira3African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, RwandaRwanda Polytechnic, Kigali P.O. Box 164, RwandaAfrican Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, RwandaNational Council for Science and Technology, Kigali P.O. Box 2285, RwandaTraffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.https://www.mdpi.com/2571-9394/5/4/34ARIMALSTMtraffic flowITSforecastingInternet of Things |
| spellingShingle | Vienna N. Katambire Richard Musabe Alfred Uwitonze Didacienne Mukanyiligira Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction Forecasting ARIMA LSTM traffic flow ITS forecasting Internet of Things |
| title | Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction |
| title_full | Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction |
| title_fullStr | Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction |
| title_full_unstemmed | Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction |
| title_short | Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction |
| title_sort | forecasting the traffic flow by using arima and lstm models case of muhima junction |
| topic | ARIMA LSTM traffic flow ITS forecasting Internet of Things |
| url | https://www.mdpi.com/2571-9394/5/4/34 |
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