Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

Chen, Jeng-Fung; Lo, Shih-Kuei; Hung, Quang

doi:10.1155/2018/5469428

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…Recently, the emergence of sensing and telecommunications technologies integrated to ITSs started to generate vast volumes of traffic data, which in turn caused a switch in the modelling paradigm towards a data-driven approach [20]. Since that time, a variety of methods have been proposed placing special emphasis on Computational Intelligence-based approaches, such as NNs [36,15], Fuzzy logic [4,5], and Bio-inspired algorithms [24,16], among others [33].…”

Section: A Brief History Of Traffic Forecastingmentioning

confidence: 99%

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems

Angarita-Zapata

Masegosa

Triguero

2020

Computational Intelligence in Emerging Technologies for Engineering Applications

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Traffic Forecasting is a well-known strategy that supports road users and decision-makers to plan their movements on the roads and to improve the management of traffic, respectively. Current data availability and growing computational capacities have increased the use of Machine Learning methods to tackle Traffic Forecasting, which is mostly modelled as a supervised regression problem. Despite the broad range of Machine Learning algorithms, there are no baselines to determine what are the most suitable methods and their hyper-parameters configurations to approach the different Traffic Forecasting regression problems reported in the literature. In Machine Learning, this is known as the Model Selection Problem, and although Automated Machine Learning methods has proved successful dealing with this problem in other areas, it has hardly been explored in Traffic Forecasting. In this work, we go deeply into the benefits of Automated Machine Learning in the aforementioned field. To this end, we use Auto-WEKA, a well-known AutoML method, on a subset of families of Traffic Forecasting regression problems characterised by having loop detectors, as traffic data source, and scales of predictions focused on the point and the road segment levels within freeway and urban environments. The experiments include data from the Caltrans Performance Measurement System and the Madrid City Council. The results show that AutoML methods can provide competitive results for TF with low human intervention.

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Section: A Brief History Of Traffic Forecastingmentioning

confidence: 99%

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems

Angarita-Zapata

Masegosa

Triguero

2020

Computational Intelligence in Emerging Technologies for Engineering Applications

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“…Each component of the traffic flow corresponds to a wide range of frequencies. The WL reduces the error and analyzes the traffic data [46]. Examples of application of this method are presented in [47][48][49][50][51].…”

Section: Traffic Volume Forecasting Models Based On Deep Learningmentioning

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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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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“…The emergence of sensing and telecommunications technologies integrated to transportation infrastructure started to generate vast volumes of traffic data, which in turn caused a switch in the modelling paradigm towards a datadriven approach [81]. Since then, a variety of methods have been proposed placing special emphasis on computational intelligence-based approaches, such as NNs [67], [132], Fuzzy logic [16], [17], and Bio-inspired algorithms [70], [87], among others [116].…”

Section: A Brief History Of Traffic Forecastingmentioning

confidence: 99%

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective

2019

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One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of machine learning (ML) to address traffic prediction, which is mostly modeled as a supervised regression problem. Although some studies have presented taxonomies to sort the literature in this field, they are mostly oriented to classify the ML methods applied and a little effort has been directed to categorize the traffic forecasting problems approached by them. As far as we know, there is no comprehensive taxonomy that classifies these problems from the point of view of both traffic and ML. In this paper, we propose a taxonomy to categorize the aforementioned problems from both traffic and a supervised regression learning perspective. The taxonomy aims at unifying and consolidating categorization criteria related to traffic and it introduces new criteria to classify the problems in terms of how they are modeled from a supervised regression approach. The traffic forecasting literature, from 2000 to 2019, is categorized using this taxonomy to illustrate its descriptive power. From this categorization, different remarks are discussed regarding the current gaps and trends in the addressed traffic forecasting area.

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Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

Cited by 14 publications

References 33 publications

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems

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

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective

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