Correlation between Road Network Accessibility
and Urban Land Use: A Case Study
of Fuzhou City

Li, Ya-xing; Bojie, Yan; Yan, Jun

doi:10.15244/pjoes/144913

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Transportation network design is infuenced by development patterns on land surfaces, infuencing infrastructure location, type, and characteristics of roads and buildings [8]. Urban transportation faces trafc congestion due to the growth of the road network, necessitating a sustainable approach to improve efciency and reduce costs.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Urban Land Use Characteristics on Road Network Accessibility in a Case Study of Addis Ababa City

Berta,

Emagnu

2024

Journal of Advanced Transportation

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Rapid urbanization in emerging nations such as Ethiopia encourages participants in the area to consider the sustainability and balanced growth of metropolitan centers from a variety of angles and to offer suitable solutions. This study aims to comprehend how urban land use influences the efficiency and connection of road networks, which in turn affects how accessible different parts of the city are, to determine the influencing factors and critical indicators that enable a systematic categorization of subcity accessibility from the perspectives of urban land use. Geographic information system (GIS) techniques are used to gather and analyze precise geographical data on road networks, land use patterns, and other pertinent properties. In addition, a regression model is created to measure the effect of land use features on the accessibility of the road network. The primary questionnaire data are used to estimate the current situation of road accessibility and are used in the study validation process. This research has incorporated qualitative and quantitative approaches to sustainability. The findings imply that residential areas are 90% significant in making transport along the city, and 98% of the accessibility of the road network is dependent on land use features, including road density and subcity areas. Among the subcities, Bole, Kirkos, and Arada have 87% road network accessibility and better access to public transportation. Overall, these techniques emphasize the value of spatial analytic tools in planning procedures to better comprehend and control the intricate relationships between transportation and land use dynamics and to assist in the sustainable design and growth of cities.

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Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Urban Land Use Characteristics on Road Network Accessibility in a Case Study of Addis Ababa City

Berta,

Emagnu

2024

Journal of Advanced Transportation

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Improved Road Extraction Models through Semi-Supervised Learning with ACCT

Yu,

Du,

Tan

et al. 2024

IJGI

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Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances.

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Correlation between Road Network Accessibility and Urban Land Use: A Case Study of Fuzhou City

Cited by 2 publications

References 9 publications

Modeling the Impact of Urban Land Use Characteristics on Road Network Accessibility in a Case Study of Addis Ababa City

Modeling the Impact of Urban Land Use Characteristics on Road Network Accessibility in a Case Study of Addis Ababa City

Improved Road Extraction Models through Semi-Supervised Learning with ACCT

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