4.6 Article

Representing dynamic lanes in road network models

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2022.2060981

关键词

Road network model; time-dependent lane; connectivity; node-link model; dynamic traffic

资金

  1. National Natural Science Foundation of China [41771420, 41601422, 41801305]
  2. Sustainable Construction of Advantageous Subjects in Jiangsu Province [164320H116]
  3. Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA)
  4. Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX19_0791]
  5. Special Fund for Public Welfare Scientific Institutions of Fujian Province [2020R11010009-2]
  6. China Scholarship Council Scholarship [202006860018]

向作者/读者索取更多资源

Road network models are essential for road network analysis, route planning, navigation, and traffic predictions. This study addresses the limitation of existing models in effectively representing the dynamic topological relationships among lanes. A time-dependent road network model (TRNM) is proposed to address this issue, and its construction method based on a traditional carriageway network model is presented. Experimental results demonstrate that TRNMs can be constructed easily from traditional road networks without introducing large volumes of data, while effectively representing the time-dependent topological relationships among lanes.
Road network models form the foundation of road network analyses, route planning, navigation and traffic predictions. However, existing models cannot effectively represent the dynamic topological relationships that exist among lanes due to the effects of time-dependent traffic control measures. To address this problem, we propose a time-dependent road network model (TRNM) to represent these topological relationships, and present its construction method based on a traditional carriageway network model. We constructed two TRNMs in Changzhou and Shanghai and then conducted path-planning experiments to verify the effectiveness of the models. Our results showed that TRNMs could be constructed readily from traditional road networks without introducing large volumes of data, while effectively representing the time-dependent topological relationships among lanes. It is particularly beneficial to path planning, as it not only provides valid and shorter paths but also lane-level navigation information. Time-dependent road network models mirror real-world road networks and can represent more time-dependent traffic controls, such as non-periodic changes at different frequencies. The TRNM developed here can provide support for applications based on road network models, as well as a useful reference for the geographic information system (GIS) and complex networks.

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