Summary of Deep Heuristic Learning For Real-time Urban Pathfinding, by Mohamed Hussein Abo El-ela and Ali Hamdi Fergany
Deep Heuristic Learning for Real-Time Urban Pathfinding
by Mohamed Hussein Abo El-Ela, Ali Hamdi Fergany
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces two novel methods for urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data. The enhanced A* algorithm dynamically adjusts routes based on current traffic and weather conditions, while the neural network model predicts optimal path segments using historical and live data. The performance of different deep learning models, including MLP, GRU, LSTM, Autoencoders, and Transformers, was evaluated in a simulated urban environment in Berlin. The results show that both methods outperformed traditional approaches, with the neural network model reducing travel times by up to 40% and the enhanced A* algorithm achieving a 34% improvement. These findings demonstrate the potential of deep learning for optimizing urban navigation in real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about using artificial intelligence to help people navigate cities more efficiently. The researchers developed two new methods that use real-time data like traffic and weather conditions to find the best route. One method adjusts routes on the fly, while the other predicts the next part of the route based on past experiences. They tested these methods in a virtual Berlin and found that they work much better than traditional ways of finding the best route. The new methods can reduce travel time by up to 40%! This is exciting because it could help people get around cities faster and more easily. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Neural network
