Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

Doğan, Erdem

doi:10.7307/ptt.v32i1.3154

Cited by 8 publications

(4 citation statements)

References 50 publications

(61 reference statements)

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“…In the method proposed in this research, the time series data will be pre-processed using normalization. Then, using the genetic algorithm and a thinning method, the best features will be selected for prediction [63][64][65][66]. Then it will be predicted with the help of a method based on neural network or other methods.…”

Section: Suggested Methodsmentioning

confidence: 99%

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

2024

MJET

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Intelligent traffic management systems, urban planning, and the reduction of traffic congestion all depend on traffic flow prediction. In this research, a method based on machine learning based on neural network combination and feature selection based on genetic algorithm is presented for predicting short-time traffic flow. The genetic algorithm-based approach seeks to find a model's optimal parameters globally. Inner-city traffic constantly changes and can be unpredictable. This is because traffic patterns repeat over time (have periodic characteristics) but also swing wildly from moment to moment (high fluctuations). As a result, it's very hard to guess what traffic will be like in the future. Thin operators have been used duo to it good performance for short-time traffic prediction in neural networks system. In Isfahan gathered traffic data to see how well a new model, called LSTM, predicts traffic flow. We compared LSTM's performance against other established methods like wavelet neural networks (WNN) and multilayer perceptron (MLP). the proposed neural network prediction model and genetic algorithm results have %97 accuracy, %97 correlation coefficient, 14.67 less average absolute error, higher signal-to-noise ratio, 0.97 entropy value, and 3.95 standard deviation. Compared to other methods, it has shown its superiority, such as ordinary neural networks. This model excels at finding the best solution quickly and accurately, even with noisy or complex data.

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Section: Suggested Methodsmentioning

confidence: 99%

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

2024

MJET

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“…Over the past years, different approaches have been attempted to develop a robust short-term traffic model using various approaches, such as time series methods (Han et al 2004;Zeng et al 2008;Wang et al 2017;Doğan 2018Doğan , 2020a, Support Vector Machine (SVM) (Zhao-sheng et al 2006;Yang and Lu 2010;Zhang et al 2011;Feng et al 2018) genetic algorithm (Abdulhai et al 2002;Vlahogianni et al 2005;Xu et al 2016) However, there is still a lack of a robust approach. Artificial Neural Network (ANN) is a promising tool for developing such an approach and has been used in many studies (Vlahogianni et al 2005;Hu et al 2008;SUN and LIU 2008;Doğan 2020b;Yao et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

Doğan

2022

Soft Comput

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Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenge due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways' main lanes. The time resolution of the dataset was set to 15 minutes, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyzes for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during mid-day periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

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“…The k-means-based algorithm, k-ear, was developed to analyze the energy needs related to the seasonal access characteristics for data management systems in [33]. The supporting role of k-means in the process of data preparation for the artificial neural network was used in [34] to predict traffic flow patterns. A similar topic was discussed in [35], where the cyclic distance for time-of-day interval partition was developed to perform a traffic analysis.…”

Section: Introductionmentioning

confidence: 99%

Using K-Means Clustering in Python with Periodic Boundary Conditions

2022

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Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.

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Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

Cited by 8 publications

References 50 publications

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

Using K-Means Clustering in Python with Periodic Boundary Conditions

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