Double Layer A*: An Emergency Path Planning Model Based on Map Grid and Double Layer Search Structure

With the vigorous development of transportation infrastructure in various countries, the traffic network within the city is becoming more and more complex, and when an emergency occurs in one or more areas of the city, it will inevitably cause traffic congestion in the area and keep spreading. There are still many challenges to solve the urban emergency route planning problem. In this paper, the authors have employed a double layer search structure, where the authors have empowered the traditional A* model with a neural network, to construct a region-level dynamic path planning model known as “Double Layer A*”. The model divides the road network into two layers, and implements the outer layer and inner layer search. In the outer layer search, the authors use the historical cab travel data for training to achieve the general direction planning; in the inner layer search, the authors update the original planning according to the changes of the road condition characteristics of the regional nodes, and perform the re-planning in real time. The authors conducted experimental evaluations using the road network data of Beijing, and the results showed that compared to a single-layer search structure path planning model, their Double layer A* model planned paths with higher similarity in land characteristics, connectivity, and average connectivity between adjacent nodes, which demonstrates the effectiveness and reasonableness of the Double layer A* model in emergency path planning.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2024.3370828
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Cai, Zhi
  • Hou, Zhihao
  • Meihui, Shi
  • Su, Xing
  • Guo, Limin
  • Ding, Zhiming
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 11509-11521
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Emergencies; Machine learning; Taxi services; Traffic congestion; Traffic data; Trajectory control
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Security and Emergencies;

Filing Info

• Accession Number: 01939882
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 16 2024 11:59AM