Identifying Complex Junctions in a Road Network

Yang, Jianting; Zhao, Kongyang; Li, Muzi; Zhu, Xu; Li, Zhilin

doi:10.3390/ijgi10010004

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…However, there are still many problems worthy of study in this field. The key is feature extraction, especially automatic feature extraction to reduce manual annotation [13][14].…”

Section: Key Issues Involved In Automatic Synthesismentioning

confidence: 99%

Automatic Generalization of Residential Areas Based on “Paradigm” Theory and Big Data

Tian-lin

Zhang

2021

Computer Methods in Medicine and Health Care

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“Paradigm” theory is an important ideological and practical tool for scientific research. The research means and methods of Geographic Information Science follow the laws of four paradigms. Automatic cartographic generalization is not only the key link of map making, but also a recognized difficult and hot issue. Based on large-scale map data and deep learning technology, an automatic cartographic generalization problem-solving model is proposed in this paper. According to the key and difficult problems faced by residential area selection and simplification, residential area selection models and simplification models based on big data and deep learning are constructed respectively, which provides new ideas and schemes to solve the key and difficult problems of residential area selection and simplification.

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“…However, there are still many problems worthy of study in this field. The key is feature extraction, especially automatic feature extraction to reduce manual annotation [13][14].…”

Section: Key Issues Involved In Automatic Synthesismentioning

confidence: 99%

Automatic Generalization of Residential Areas Based on “Paradigm” Theory and Big Data

Tian-lin

Zhang

2021

Computer Methods in Medicine and Health Care

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“…For example, Ulugtekin et al [1] emphasized that road junctions should be represented in a suitable manner for the application of car navigation. Mackaness and Mackechnie [2], Touya [3], and Yang et al [4] highlighted that recognizing the structures of the junctions is essential for pattern-preserving road-network generalization. In addition, the recognition of road junctions contributes to traffic analysis and management [5][6][7] and urban planning and landscape design [8].…”

Section: Introductionmentioning

confidence: 99%

“…Similar ideas were developed by Touya [3] and Zhou and Li [11], who detected complex junctions by clustering characteristic road nodes, including Y-shaped, y-shaped, fork-shaped, and multi-leg nodes. Aiming to preserve the integrality of complex junctions, Yang et al [4] introduced road design principles to clarify the topological boundaries of complex junctions. Li et al [9] utilized a target detection model, that is, the faster-region convolutional neural network, to detect the locations of interchanges based on raster representations of road networks.…”

Section: Introductionmentioning

confidence: 99%

A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

Yang

Cheng

Cao

et al. 2022

IJGI

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Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage.

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“…The definition of pedestrian flow prediction can be considered as follows. Given a sequence of observed flow data in the road network, the task is to predict the pedestrian flow in the next moments [5][6][7][8]. The pedestrian can be affected by miscellaneous factors which pose great challenges to pedestrian prediction [9,10].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network

Liu

et al. 2021

IJGI

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Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time.

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Identifying Complex Junctions in a Road Network

Cited by 9 publications

References 29 publications

Automatic Generalization of Residential Areas Based on “Paradigm” Theory and Big Data

Automatic Generalization of Residential Areas Based on “Paradigm” Theory and Big Data

A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network

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