SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS
Estimation of the state of road traffic conditions is gaining increasing attention in recent intelligent transportation systems. Accurate and real-time estimation of traffic condition changes is critical in the management and control of road network systems. Thus, efforts are been made to predict sho...
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Silesian University of Technology
2022-06-01
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| Series: | Scientific Journal of Silesian University of Technology. Series Transport |
| Subjects: | |
| Online Access: | https://sjsutst.polsl.pl/archives/2022/vol115/121_SJSUTST115_2022_OzinalAvsar_Avsar.pdf |
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| author | Yağmur ÖZİNAL AVŞAR Ercan AVŞAR |
| author_facet | Yağmur ÖZİNAL AVŞAR Ercan AVŞAR |
| author_sort | Yağmur ÖZİNAL AVŞAR |
| collection | DOAJ |
| description | Estimation of the state of road traffic conditions is gaining increasing attention in recent intelligent transportation systems. Accurate and real-time estimation of traffic condition changes is critical in the management and control of road network systems. Thus, efforts are been made to predict short-term traffic conditions based on measured traffic data such as speed, flow and density. In this work, the state of the traffic is estimated through a three-step process. First, both speed and flow predictions for 15-minute ahead are made for a particular freeway segment. Four different regression models are used for the prediction task, namely, multi-layer perceptron neural networks (MLPNN), support vector regression (SVR), gradient boosted decision trees (GBDT), and k-nearest neighbors (kNN). Next, the breakpoint (BP) flow is calculated using the distribution of these predicted speed and flow values. In the final step, these predictions are classified as belonging to a “stable state” or “metastable state” by using the calculated BP as the threshold between these states. According to the experimental results, the values for MLPNN are the highest for speed (0.8564) and flow (0.9862) predictions. An identical BP, 1050 pc/15min, is calculated for actual data as well as all prediction methods. |
| format | Article |
| id | doaj-art-df471e5fec3d46948a9e53e3b61cc78d |
| institution | Kabale University |
| issn | 0209-3324 2450-1549 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Silesian University of Technology |
| record_format | Article |
| series | Scientific Journal of Silesian University of Technology. Series Transport |
| spelling | doaj-art-df471e5fec3d46948a9e53e3b61cc78d2025-01-02T18:09:08ZengSilesian University of TechnologyScientific Journal of Silesian University of Technology. Series Transport0209-33242450-15492022-06-0111511512113410.20858/sjsutst.2022.115.9SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODSYağmur ÖZİNAL AVŞAR0https://orcid.org/0000-0002-8083-6494Ercan AVŞAR1https://orcid.org/0000-0002-1356-2753Faculty of Engineering, Department of Civil Engineering, Dokuz Eylül University, 35390, Buca/İzmir, TurkeyFaculty Engineering, Department of Computer Engineering, Dokuz Eylül University, 35390, Buca/İzmir, TurkeyEstimation of the state of road traffic conditions is gaining increasing attention in recent intelligent transportation systems. Accurate and real-time estimation of traffic condition changes is critical in the management and control of road network systems. Thus, efforts are been made to predict short-term traffic conditions based on measured traffic data such as speed, flow and density. In this work, the state of the traffic is estimated through a three-step process. First, both speed and flow predictions for 15-minute ahead are made for a particular freeway segment. Four different regression models are used for the prediction task, namely, multi-layer perceptron neural networks (MLPNN), support vector regression (SVR), gradient boosted decision trees (GBDT), and k-nearest neighbors (kNN). Next, the breakpoint (BP) flow is calculated using the distribution of these predicted speed and flow values. In the final step, these predictions are classified as belonging to a “stable state” or “metastable state” by using the calculated BP as the threshold between these states. According to the experimental results, the values for MLPNN are the highest for speed (0.8564) and flow (0.9862) predictions. An identical BP, 1050 pc/15min, is calculated for actual data as well as all prediction methods.https://sjsutst.polsl.pl/archives/2022/vol115/121_SJSUTST115_2022_OzinalAvsar_Avsar.pdfbreakpointmachine learningshort-term trafficpredictionspeed-flow relationship |
| spellingShingle | Yağmur ÖZİNAL AVŞAR Ercan AVŞAR SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS Scientific Journal of Silesian University of Technology. Series Transport breakpoint machine learning short-term traffic prediction speed-flow relationship |
| title | SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS |
| title_full | SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS |
| title_fullStr | SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS |
| title_full_unstemmed | SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS |
| title_short | SHORT-TERM TRAFFIC STATE ESTIMATION USING BREAKPOINT FLOW CALCULATION AND MACHINE LEARNING METHODS |
| title_sort | short term traffic state estimation using breakpoint flow calculation and machine learning methods |
| topic | breakpoint machine learning short-term traffic prediction speed-flow relationship |
| url | https://sjsutst.polsl.pl/archives/2022/vol115/121_SJSUTST115_2022_OzinalAvsar_Avsar.pdf |
| work_keys_str_mv | AT yagmurozinalavsar shorttermtrafficstateestimationusingbreakpointflowcalculationandmachinelearningmethods AT ercanavsar shorttermtrafficstateestimationusingbreakpointflowcalculationandmachinelearningmethods |