Representing dynamic lanes in road network models

Abstract

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.

Keywords:

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

Acknowledgments

We would like to thank Associate Professor Chen-Chieh Feng of the Department of Geography at the National University of Singapore, Singapore and Professor Luliang Tang of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China for their valuable comments and suggestions. We would also like to thank the editors Dr. May Yuan and Dr. Urska Demsar for their constructive comments. The criticism, comments, suggestions and corrections from the anonymous referees are also highly appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and code that support the findings of this study are available in the figshare repository with their identifier(s) at: https://doi.org/10.6084/m9.figshare.19403069.

Additional information

Funding

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

Notes on contributors

Xiuquan Li

Xiuquan Li is a Ph.D. student of the Cartography and Geographic Information Systems unit in the Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, China. His research interests focus on transportation geography, dynamic modeling, space-time analysis and urban sustainability. Email: [email protected].

Meizhen Wang

Meizhen Wang is currently an associate professor in the Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, China. Her research interests include space-time analysis and geospatial video analysis. Email: [email protected].

Xuejun Liu

Xuejun Liu is a professor in the Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, China. His research interests include transportation geography, digital terrain analysis and geospatial video analysis. Email: [email protected].

Ziran Wang

Ziran Wang is currently an associate professor with Nanjing Normal University Taizhou College, and is pursuing his doctorate degree in Nanjing Normal University. His research interests include spatiotemporal analysis and geospatial video analysis. Email: [email protected].

Yuxia Bian

Yuxia Bian is a lecturer with Chengdu University of Information Technology, China. Her research interests include uncertainty analysis, spatiotemporal analysis and geospatial video analysis. Email: [email protected].