This survey provides a comprehensive review of route recommendation methods, applications, and future research directions within the context of urban computing. It is organized into three main parts: methodology, applications, and challenges. The survey discusses both classic methods and modern deep learning approaches for route recommendation. Classic methods include search-based, probability-based, biomimetic-based, clustering-based, and constraint-based approaches. These methods aim to find optimal routes based on various constraints and preferences. Modern deep learning methods have significantly improved the efficiency and effectiveness of route recommendation by leveraging large-scale data and complex models. The survey highlights the importance of route recommendation in urban computing, where data from various sources, including trajectory data, POI information, and traffic data, are used to provide personalized and efficient route suggestions. It discusses the challenges and limitations of current route recommendation systems, such as handling multi-modal data, enhancing user privacy, and integrating with large models. The survey also outlines promising research directions, including the use of deep learning for route recommendation, which has shown great potential in capturing complex relationships in data and improving the accuracy of route recommendations. The survey presents a variety of deep learning approaches for route recommendation, including hybrid models, sequence-based models, graph neural network models, multi-modal models, and deep reinforcement learning models. These models are designed to handle the complexity of route recommendation tasks by incorporating various data sources and modeling techniques. The survey also discusses the evaluation metrics used to assess the performance of route recommendation systems, such as edit distance, precision, recall, and F1 score. In conclusion, this survey provides a comprehensive overview of route recommendation methods, applications, and future research directions within the context of urban computing. It emphasizes the importance of integrating advanced data and computational techniques to improve the efficiency and effectiveness of route recommendation systems. The survey highlights the potential of deep learning in route recommendation and outlines promising research directions for future work.This survey provides a comprehensive review of route recommendation methods, applications, and future research directions within the context of urban computing. It is organized into three main parts: methodology, applications, and challenges. The survey discusses both classic methods and modern deep learning approaches for route recommendation. Classic methods include search-based, probability-based, biomimetic-based, clustering-based, and constraint-based approaches. These methods aim to find optimal routes based on various constraints and preferences. Modern deep learning methods have significantly improved the efficiency and effectiveness of route recommendation by leveraging large-scale data and complex models. The survey highlights the importance of route recommendation in urban computing, where data from various sources, including trajectory data, POI information, and traffic data, are used to provide personalized and efficient route suggestions. It discusses the challenges and limitations of current route recommendation systems, such as handling multi-modal data, enhancing user privacy, and integrating with large models. The survey also outlines promising research directions, including the use of deep learning for route recommendation, which has shown great potential in capturing complex relationships in data and improving the accuracy of route recommendations. The survey presents a variety of deep learning approaches for route recommendation, including hybrid models, sequence-based models, graph neural network models, multi-modal models, and deep reinforcement learning models. These models are designed to handle the complexity of route recommendation tasks by incorporating various data sources and modeling techniques. The survey also discusses the evaluation metrics used to assess the performance of route recommendation systems, such as edit distance, precision, recall, and F1 score. In conclusion, this survey provides a comprehensive overview of route recommendation methods, applications, and future research directions within the context of urban computing. It emphasizes the importance of integrating advanced data and computational techniques to improve the efficiency and effectiveness of route recommendation systems. The survey highlights the potential of deep learning in route recommendation and outlines promising research directions for future work.