A multi-channel adaptive neural network for querying the optimal time-varying damage route with collective spatial keywords

In geographic information system services, the optimal route planning with collective spatial keywords plays a crucial role in providing efficient and feasible travel solutions. Existing research on damage conditions of points of interest in road networks over time remains incomplete. To address this issue, we propose an innovative multi-channel adaptive neural network model and algorithm for querying the optimal path with collective spatial keywords on a time-varying damage network. To address the variations in edge lengths in time-varying networks, we have designed multi-channel gated neurons that incorporate node state judgment, departure time selection, and transmission time control. These neurons are integrated with a logic gate to manage the time-varying edge lengths. To ensure the accuracy of data exchange, we have introduced a multi-channel mechanism for data isolation. We have analyzed the time complexity and correctness of the proposed algorithms and conducted comparative experiments using a public road network dataset. The experimental results demonstrate the effectiveness and superiority of the proposed method in solving the optimal path with collective spatial keywords query problem on time-varying damage networks, providing technical support for intelligent traffic planning projects.