Characterising the structural pattern of urban road networks in Ghana using geometric and topological measures

Dumedah, Gift; Garsonu, Emmanuel Kofi

doi:10.1002/geo2.95

Cited by 7 publications

(2 citation statements)

References 72 publications

(182 reference statements)

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“…Network Science is a discipline devoted to the study of networks [29,30] that has been heavily applied to study systems of all kinds [31,32], and in particular complex systems such as road systems [13,24,[33][34][35][36][37][38][39][40][41][42][43]. Urban road networks are generally modelled as spatial and planar networks [44].…”

Section: Network Sciencementioning

confidence: 99%

A Geometric Classification of World Urban Road Networks

et al. 2022

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This article presents a method to uncover universal patterns and similarities in the urban road networks of the 80 most populated cities in the world. To that end, we used degree distribution, link length distribution, and intersection angle distribution as topological and geometric properties of road networks. Moreover, we used ISOMAP, a nonlinear dimension reduction technique, to better express variations across cities, and we used K-means to cluster cities. Overall, we uncovered one universal pattern between the number of nodes and links across all cities and identified five classes of cities. Gridiron Cities tend to have many 90° angles. Long Link Cities have a disproportionately high number of long links and include mostly Chinese cities that developed towards the end of the 20th century. Organic Cities tend to have short links and more non-90 and 180° angles; they also include relatively more historical cities. Hybrid Cities tend to have both short and long links; they include cities that evolved both historically and recently. Finally, Mixed Cities exhibit features from all other classes. These findings can help transport planners and policymakers identify peer cities that share similar characteristics and use their characteristics to craft tailored transport policies.

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Section: Network Sciencementioning

confidence: 99%

A Geometric Classification of World Urban Road Networks

et al. 2022

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“…Current methods employ techniques like Graph Convolutional Networks (GCN) to learn features of road networks, but there is still room for improvement (Cheng et al, 2023 ). Future research could explore more effective graph neural network models or methods that combine graph structure information with road attribute information to enhance the modeling capabilities of road topology structure (Dumedah and Garsonu, 2021 ). Temporal Data Modeling: Studies are focusing on better capturing the temporal characteristics of traffic data (Ma et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Design of urban road fault detection system based on artificial neural network and deep learning

Lin

2024

Front. Neurosci.

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IntroductionIn urban traffic management, the timely detection of road faults plays a crucial role in improving traffic efficiency and safety. However, conventional methods often fail to fully leverage the information from road topology and traffic data.MethodsTo address this issue, we propose an innovative detection system that combines Artificial Neural Networks (ANNs), specifically Graph Convolutional Networks (GCN), Bidirectional Gated Recurrent Units (BiGRU), and self-attention mechanisms. Our approach begins by representing the road topology as a graph and utilizing GCN to model it. This allows us to learn the relationships between roads and capture their structural dependencies. By doing so, we can effectively incorporate the spatial information provided by the road network. Next, we employ BiGRU to model the historical traffic data, enabling us to capture the temporal dynamics and patterns in the traffic flow. The BiGRU architecture allows for bidirectional processing, which aids in understanding the traffic conditions based on both past and future information. This temporal modeling enhances our system's ability to handle time-varying traffic patterns. To further enhance the feature representations, we leverage self-attention mechanisms. By combining the hidden states of the BiGRU with self-attention, we can assign importance weights to different temporal features, focusing on the most relevant information. This attention mechanism helps to extract salient features from the traffic data. Subsequently, we merge the features learned by GCN from the road topology and BiGRU from the traffic data. This fusion of spatial and temporal information provides a comprehensive representation of the road status.Results and discussionsBy employing a Multilayer Perceptron (MLP) as a classifier, we can effectively determine whether a road is experiencing a fault. The MLP model is trained using labeled road fault data through supervised learning, optimizing its performance for fault detection. Experimental evaluations of our system demonstrate excellent performance in road fault detection. Compared to traditional methods, our system achieves more accurate fault detection, thereby improving the efficiency of urban traffic management. This is of significant importance for city administrators, as they can promptly identify road faults and take appropriate measures for repair and traffic diversion.

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Analysis of Spatiotemporal Factors Affecting Traffic Safety Based on Multisource Data Fusion

Cheng

Zhang

et al. 2023

J. Transp. Eng., Part A: Systems

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Characterising the structural pattern of urban road networks in Ghana using geometric and topological measures

Cited by 7 publications

References 72 publications

A Geometric Classification of World Urban Road Networks

A Geometric Classification of World Urban Road Networks

Design of urban road fault detection system based on artificial neural network and deep learning

Analysis of Spatiotemporal Factors Affecting Traffic Safety Based on Multisource Data Fusion

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