Managing Traffic Data through Clustering and Radial Basis Functions

Hernández, Heber; Alberdi, Elisabete; Pérez-Acebo, Heriberto; Álvarez, Igor; García, Maria José; Eguía, Isabel Mugartegui; Fernández, Kevin

doi:10.3390/su13052846

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…Generally, traffic prediction involves studying various transportation variables such as vehicular speed flow, traffic patterns, region industrialization, and various other topographical features involved in the exploratory analysis and forecasting of traffic trends [3,4]. Subsequently, this drives precise real-time traffic forecasting, an integral part of intelligent traffic systems, as well as traffic control and management [5,6]. Correspondingly, most countries' transportation ministries are currently deploying such intelligent transportation models to combat the various challenges of modern day transportation [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction

Oluwasanmi

Aftab

Qin

et al. 2023

Sensors

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Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.

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Section: Introductionmentioning

confidence: 99%

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction

Oluwasanmi

Aftab

Qin

et al. 2023

Sensors

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show abstract

“…Studying the literature reveals that most investigations in the field of road safety are based on logit models or regression models with artificial neural networks. On the one hand, the complexity and uncertainty of the factors affecting road safety, and on the other hand, the ability of machine learning algorithms to predict and navigate in the face of unexpected and uncertain issues, has resulted in the successful application of machine learning methods in road safety in recent years [57][58][59][60][61][62][63].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido

Haghshenas

et al. 2022

Safety

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The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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“…On the one hand, due to the complexity and uncertainty in the factors affecting road safety, and on the other hand, the ability of machine learning algorithms in the face of unpredicted and uncertain issues has led to the successful application of machine learning methods was used to road safety in recent years. [54][55][56][57][58][59][60].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Guido¹,

Haghshenas²,

Haghshenas³

et al. 2021

Preprint

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Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

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Managing Traffic Data through Clustering and Radial Basis Functions

Cited by 8 publications

References 40 publications

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

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