# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 2 Preliminaries  
In addressing the optimization of urban path planning under constraints such as avoidance zones, congestion distance, and traffic signals, this study integrates the A* search algorithm with the Deep Q-Network (DQN) algorithm, as illustrated in the overall structure shown in Figure 1. The A* algorithm is employed to generate a global path, while the DQN algorithm performs local optimization. Furthermore, the heuristic function's weight parameters are dynamically adjusted in response to varying travel scenarios. This section provides an overview of the A* algorithm, the DQN algorithm, and the dynamic weight adjustment mechanism. In addressing the optimization of urban path planning under constraints such as avoidance zones, congestion distance, and traffic signals, this study integrates the A* search algorithm with the Deep Q-Network (DQN) algorithm to form a hybrid decision-making framework that balances global optimality with local adaptability. Urban environments are dynamic and often involve complex conditions--such as sudden congestion, temporary road closures, and variable traffic light timings--that traditional single-layer path planning methods struggle to handle effectively. In this framework, the A* algorithm is primarily responsible for generating an initial global path that satisfies static constraints and ensures overall efficiency from origin to destination. This path serves as the backbone for navigation, providing a high-level trajectory through the urban environment that avoids predefined restricted zones and minimizes the estimated travel distance. On the other hand, the DQN algorithm acts as a local optimizer, refining the path in real time to adapt to dynamically changing traffic conditions. It is particularly effective in responding to unforeseen obstacles or localized disturbances, such as sudden traffic buildup or adaptive traffic signals, by making intelligent detours or re-routing decisions based on learned experience. Moreover, to enhance the flexibility and contextual responsiveness of the planning system, the heuristic function within the A* algorithm is embedded with dynamically adjustable weight parameters. These parameters are fine-tuned during execution based on real-time travel scenarios--such as peak vs. off-peak hours or different urban regions--thereby allowing the system to prioritize different optimization goals (e.g., minimizing time vs. minimizing fuel consumption) as conditions evolve. Figure 1: The refined system architecture of the proposed multi-source data-driven terrestrial path planning framework. It consists of five functional layers: (1) Data acquisition integration layer gathers user-defined targets and multi-source geographic data; (2) Preprocessing modeling layer constructs a unified topological and spatial representation; (3) Path planning core combines global A* and local DQN-based adjustment; (4) Adaptation conflict handling layer dynamically adjusts optimization priorities and resolves inconsistent inputs; (5) Output interaction layer provides real-time visualized results and vehicle feedback. This section provides an overview of the collaborative use of the A* and DQN algorithms within the proposed hybrid framework, and introduces the adaptive weight adjustment mechanism that enables more intelligent and responsive urban path planning. The terrestrial path planning task focuses on road network scenarios. By acquiring multi-modal geographic information and real-time dynamic data--including road network topology, traffic signal statuses, and road congestion indices--the heterogeneous data is processed through cleaning, format standardization, and spatial coordinate unification. This results in the construction of a standardized data repository for terrestrial transportation. Based on the processed data, road intersections are abstracted as network nodes, and road segment capacity is modeled as weighted edges. Leveraging the hierarchical characteristics of the road network, a topological network model is established.