A Functional Data Analysis Approach to Traffic Volume Forecasting

Wagner-Muns, Isaac Michael; Guardiola, Ivan G.; Samaranayke, V. A.; Kayani, Wasim Irshad

doi:10.1109/tits.2017.2706143

Cited by 56 publications

(27 citation statements)

References 34 publications

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“…Prediction not only provides information on future traffic conditions, but also helps evaluate the control decisions before they are implemented. In addition, considering the accuracy and timeliness of the prediction model, the time series model [8]- [10], Kalman filter model [11], support vector machine [12], non-parametric regression method [13]- [15] and long-term and short-term memory neural network [16], etc. have also been proposed for the prediction of short-term traffic flow in urban road networks.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Jia²,

Mei

et al. 2020

IEEE Access

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With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as ''road speed'') is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems. INDEX TERMS Urban traffic flow, speed prediction, fuzzy C-means, fuzzy-RBF, neural network.

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Section: Introductionmentioning

confidence: 99%

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Jia²,

Mei

et al. 2020

IEEE Access

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“…Outliers are inevitable in the process of traffic flow data collection. They are less creditable and significantly different from the real data [32]. Before traffic prediction, the data should be classified as real data and outlier data, and the outliers must be removed accurately.…”

Section: The Classification Algorithm Of Asym-gentle Adaboost Witmentioning

confidence: 99%

Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction

Zhang

Xin

2020

IEEE Access

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In order to eliminate outliers in traffic flow data collection and promote the generalization performance of traffic flow prediction, this paper proposes a dynamic optimization long short-term memory (LSTM) model based on data preprocessing for short-term traffic flow prediction. A new classification algorithm named Asym-Gentle Adaboost with Cost-sensitive support vector machine (AGACS) is used for preprocessing traffic flow data. AGACS tries to employ Cost-sensitive SVM (CS-SVM) as weak component classifier in Asymmetric Gentle AdaBoost, and divide the data collection into outlier data and normal data. Only normal data is used for training LSTM to predict traffic flow and an improved chaotic Particle Swarm Optimization (CPSO) is used for dynamic optimizing hidden layer structure of LSTM to promote the generalization and robustness performance of model. The efficiency of the proposed method is proved with real traffic flow data, and the experimental results show that preprocess collecting data and dynamic optimize model structure are conducive to improve the performance of algorithm, and the proposed method achieved better performance than those of three other classical deep learning prediction methods.

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“…A Functional Data Analysis Approach to Traffic Volume Forecasting A Prediction method based on the historical-data analysis of the region. This method emphasizes the use of pre-recorded historical data to determine the traffic flow in the region or the lane, thus providing the average vehicular distribution [6] based on time. This method makes use of a pre-recorded time stamped data samples taken at different time intervals.…”

Section: Cmentioning

confidence: 99%