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Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation

Fangli Guan, Zhixiang Fang, Xucai Zhang (UGent) , Haoyu Zhong, Jianhui Zhang and Haosheng Huang (UGent)
(2023) COMPUTERS ENVIRONMENT AND URBAN SYSTEMS. 103.
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Abstract
The complexity of urban physical environments at road intersections is a primary factor characterizing the difficulty of wayfinding, which is a fundamental spatial activity of human beings in cities. A complex intersection may increase the difficulty of understanding the environment, which may result in incorrect turning decisions and even bring road safety issues. Existing methods measure the complexity of road intersections by solely considering their visual or structural features. More importantly, they only output a single complexity value for each intersection, failing to differentiate the decision-making complexity based on the specific entry/exit branches of a passing branches. This study proposes a computational model to quantify the fine-grained decision -making complexity of road intersections for the navigation data models and navigation systems based on specific passing branches, using the visual, structural, and semantic features from human perspectives. For each pair of two branches (i.e., one entry and one exit) passing through the road intersection, the model will output a specific decision-making complexity score. Furthermore, this study develops a route planning algorithm for generating the minimum complexity route to serve relevant navigation applications. This study contributes to human -centered route planning and communication, as well as enabling potential innovative applications in traffic safety studies and sustainable urban and environmental development.
Keywords
Urban Studies, General Environmental Science, Ecological Modeling, Geography, Planning and Development, Urban streetscape, Road intersection, Decision -making complexity, Route planning, PEDESTRIAN NAVIGATION, LANDMARKS, DIRECTIONS, ALGORITHMS, SELECTION, BEHAVIOR, IMAGERY, HEALTH, ROUTES, IMPACT

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Citation

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MLA
Guan, Fangli, et al. “Using Street-View Panoramas to Model the Decision-Making Complexity of Road Intersections Based on the Passing Branches during Navigation.” COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, vol. 103, 2023, doi:10.1016/j.compenvurbsys.2023.101975.
APA
Guan, F., Fang, Z., Zhang, X., Zhong, H., Zhang, J., & Huang, H. (2023). Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 103. https://doi.org/10.1016/j.compenvurbsys.2023.101975
Chicago author-date
Guan, Fangli, Zhixiang Fang, Xucai Zhang, Haoyu Zhong, Jianhui Zhang, and Haosheng Huang. 2023. “Using Street-View Panoramas to Model the Decision-Making Complexity of Road Intersections Based on the Passing Branches during Navigation.” COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 103. https://doi.org/10.1016/j.compenvurbsys.2023.101975.
Chicago author-date (all authors)
Guan, Fangli, Zhixiang Fang, Xucai Zhang, Haoyu Zhong, Jianhui Zhang, and Haosheng Huang. 2023. “Using Street-View Panoramas to Model the Decision-Making Complexity of Road Intersections Based on the Passing Branches during Navigation.” COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 103. doi:10.1016/j.compenvurbsys.2023.101975.
Vancouver
1.
Guan F, Fang Z, Zhang X, Zhong H, Zhang J, Huang H. Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS. 2023;103.
IEEE
[1]
F. Guan, Z. Fang, X. Zhang, H. Zhong, J. Zhang, and H. Huang, “Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation,” COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, vol. 103, 2023.
@article{01GZGQ6GSW870PCYZNPPEHVD9B,
  abstract     = {{The complexity of urban physical environments at road intersections is a primary factor characterizing the difficulty of wayfinding, which is a fundamental spatial activity of human beings in cities. A complex intersection may increase the difficulty of understanding the environment, which may result in incorrect turning decisions and even bring road safety issues. Existing methods measure the complexity of road intersections by solely considering their visual or structural features. More importantly, they only output a single complexity value for each intersection, failing to differentiate the decision-making complexity based on the specific entry/exit branches of a passing branches. This study proposes a computational model to quantify the fine-grained decision -making complexity of road intersections for the navigation data models and navigation systems based on specific passing branches, using the visual, structural, and semantic features from human perspectives. For each pair of two branches (i.e., one entry and one exit) passing through the road intersection, the model will output a specific decision-making complexity score. Furthermore, this study develops a route planning algorithm for generating the minimum complexity route to serve relevant navigation applications. This study contributes to human -centered route planning and communication, as well as enabling potential innovative applications in traffic safety studies and sustainable urban and environmental development.}},
  articleno    = {{101975}},
  author       = {{Guan, Fangli and Fang, Zhixiang and Zhang, Xucai and Zhong, Haoyu and Zhang, Jianhui and Huang, Haosheng}},
  issn         = {{0198-9715}},
  journal      = {{COMPUTERS ENVIRONMENT AND URBAN SYSTEMS}},
  keywords     = {{Urban Studies,General Environmental Science,Ecological Modeling,Geography, Planning and Development,Urban streetscape,Road intersection,Decision -making complexity,Route planning,PEDESTRIAN NAVIGATION,LANDMARKS,DIRECTIONS,ALGORITHMS,SELECTION,BEHAVIOR,IMAGERY,HEALTH,ROUTES,IMPACT}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation}},
  url          = {{http://doi.org/10.1016/j.compenvurbsys.2023.101975}},
  volume       = {{103}},
  year         = {{2023}},
}
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