Short-term traffic state estimation using breakpoint flow calculation and machine learning methods

• Autorzy: Özinal Avşar Yağmur , Avşar Ercan

Identyfikatory

DOI

10.20858/sjsutst.2022.115.9

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

breakpoint; machine learning; short-term traffic; prediction; speedflow relationship

PL

punkt wstrzymania; uczenie maszynowe; ruch krótkoterminowy; przewidywanie; relacja prędkość - przepływ

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2022
• Tom: z. 115
• Strony: 121--134
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Özinal Avşar Yağmur

• Faculty of Engineering, Department of Civil Engineering, Dokuz Eylül University, 35390, Buca/İzmir, Turkey

• https://orcid.org/0000-0002-8083-6494

autor

Avşar Ercan

• Faculty Engineering, Department of Computer Engineering, Dokuz Eylül University, 35390, Buca/İzmir, Turkey

• https://orcid.org/0000-0002-1356-2753

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).