Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections

AlKheder, Sharaf; Alkhamees, Wasan; Almutairi, Reyouf; Alkhedher, Mohammad

doi:10.1007/s00521-020-05115-y

Cited by 18 publications

(7 citation statements)

References 33 publications

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“…In this section, a comparative analysis was performed with CWRNN [58], T‐GCN [50], WL+GRU+ARIMA [55], NARX [13], and RBFNN [12] models to verify the robustness and accuracy of the hybrid model presented here. It should be noted that the results were compared with the mentioned models with the available data.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, they showed that the traffic volume data of adjacent intersections could be used as the input data to the Radial Basis Function Neural Network (RBFNN) model for forecasting the short‐term traffic of a given intersection. Another study was done in Kuwait [13] to examine the intersections’ affection on each other, and valuable results were obtained through the Bayesian Combined Neural Network (BCNN). Despite various studies, short‐term traffic volume forecasting with noisy data at adjacent intersections is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid deep and machine learning model for short‐term traffic volume forecasting of adjacent intersections

Mirzahossein

Gholampour

Sajadi

et al. 2022

IET Intelligent Trans Sys

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Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL) noise reduction algorithm (WL+GRU-LSTM) are used to analyze raw traffic volume data. The WL+GRU-LSTM is constructed by comparing different machine learning and deep learning methods. A comparative study was used to choose the model's network structure, training technique, and optimizer type. To prove the model's accuracy and resilience, it was compared with the leading short-term traffic forecasting approaches. Experimental results confirm that the WL+GRU-LSTM model can forecast complex traffic volume fluctuations in different approaches of intersections with an accuracy of over 94%. It also shows better results compared to current methods. The proposed model could replace intermediate loop detectors.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid deep and machine learning model for short‐term traffic volume forecasting of adjacent intersections

Mirzahossein

Gholampour

Sajadi

et al. 2022

IET Intelligent Trans Sys

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show abstract

“…The sigmoid activation function is used in middle layers and output layers [10] , as shown in equation ( 4)…”

Section: Artificial Neuron Settingmentioning

confidence: 99%

Application of artificial neural network in highway traffic volume prediction

Xu,

Tong

2024

Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024)

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“…To improve the accuracy, Gu et al [87] proposed an Improved Bayesian Combination Model with Deep Learning (IBCM-DL) to increase not only the accuracy, but also the stability. AlKheder et al [88] focused on evaluating the impacts of adjacent intersections in terms of the traffic volume and using a BCNN; the authors were able to show improvements in both model coherency and accuracy with an average MSE of 0.003468 during weekdays. Table 14 presents the works found using BNNs and their focus, limitations, and performances.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

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Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections

Cited by 18 publications

References 33 publications

A hybrid deep and machine learning model for short‐term traffic volume forecasting of adjacent intersections

A hybrid deep and machine learning model for short‐term traffic volume forecasting of adjacent intersections

Application of artificial neural network in highway traffic volume prediction

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

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