A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Bratsas, Charalampos; Koupidis, Kleanthis; Salanova, Josep-Maria; Γιαννακόπουλος, Κωνσταντίνος; Kaloudis, Aristeidis; Aifadopoulou, Georgia

doi:10.3390/su12010142

Cited by 53 publications

(24 citation statements)

References 29 publications

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“…Data mining and machine learning techniques are widely used in data analysis for various objectives such as pattern identification, and decision making with minimal human intervention. Some examples of machine learning applications include human activity recognition [ 32 ], face recognition [ 33 ], traffic predictions [ 34 , 35 ], weather prediction [ 36 , 37 ], stock market prediction [ 38 ], health prediction [ 39 ], etc. Hence, some studies have integrated machine learning in their solution to determine the moisture content in grain.…”

Section: Related Workmentioning

confidence: 99%

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RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

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Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

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Section: Related Workmentioning

confidence: 99%

“…Various measures that can be used to assess the performance of a model. These measures include the determination coefficient ( R 2 ), Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) [ 34 ]. The R 2 , MAE, MSE and RMSE can be calculated using Equations (6)–(9), respectively.…”

Section: Experimental Measurementmentioning

confidence: 99%

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…The experiment results indicate that the improved-SVM forecasting accuracy is high, which is superior to other traffic flow forecasting methods [20]. Bratsas et al conducted multi-scenario experimental verification on the random forest model, support vector regression model, and multi-layer perceptron method to compare their prediction performance [21]. SVM is a classic traffic flow prediction method.…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

et al. 2020

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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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“…Generally, traffic flow is defined as a measure of traffic status (i.e., total volume [3][4][5][6][7], average speed [1, [8][9][10], average travel time [11,12]) in a constant time interval at a target road segment. Obviously, the traffic flow is time-varying.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition

et al. 2020

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Accurate and timely traffic flow forecasting is a critical task of the intelligent transportation system (ITS). The predicted results offer the necessary information to support the decisions of administrators and travelers. To investigate trend and periodic characteristics of traffic flow and develop a more accurate prediction, a novel method combining periodic-trend decomposition (PTD) is proposed in this paper. This hybrid method is based on the principle of “decomposition first and forecasting last”. The well-designed PTD approach can decompose the original traffic flow into three components, including trend, periodicity, and remainder. The periodicity is a strict period function and predicted by cycling, while the trend and remainder are predicted by modelling. To demonstrate the universal applicability of the hybrid method, four prevalent models are separately combined with PTD to establish hybrid models. Traffic volume data are collected from the Minnesota Department of Transportation (Mn/DOT) and used to conduct experiments. Empirical results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) of hybrid models are averagely reduced by 17%, 17%, and 29% more than individual models, respectively. In addition, the hybrid method is robust for a multi-step prediction. These findings indicate that the proposed method combining PTD is promising for traffic flow forecasting.

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A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Cited by 53 publications

References 29 publications

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition

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