Deep Heuristic Learning for Real-Time Urban Pathfinding
This work addresses real-time urban navigation optimization for city planners and commuters, representing an incremental advancement by integrating deep learning with existing heuristic methods.
The paper tackled urban pathfinding by transforming heuristic-based algorithms into deep learning models using real-time contextual data, resulting in a neural network model that reduced travel times by up to 40% and an enhanced A* algorithm with a 34% improvement in a simulated Berlin environment.
This paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an enhanced A* algorithm that dynamically adjusts routes based on current environmental conditions, and a neural network model that predicts the next optimal path segment using historical and live data. An extensive benchmark was conducted to compare the performance of different deep learning models, including MLP, GRU, LSTM, Autoencoders, and Transformers. Both methods were evaluated in a simulated urban environment in Berlin, with the neural network model outperforming traditional methods, reducing travel times by up to 40%, while the enhanced A* algorithm achieved a 34% improvement. These results demonstrate the potential of deep learning to optimize urban navigation in real time, providing more adaptable and efficient routing solutions.