This chapter provides a comprehensive survey of route recommendation methods and applications within the context of urban computing. It is organized into three main sections: methodology, application, and current challenges and future directions. The methodology section categorizes classic methods and modern deep learning approaches, highlighting their historical relations and advancements. The application section presents various novel applications of route recommendation in urban computing scenarios. The current challenges and future directions section discusses existing problems and proposes promising research directions, such as incorporating multi-modal data, enhancing user privacy, and integrating with large models. The survey aims to help researchers quickly understand the current state of route recommendation research and guide future research trends.This chapter provides a comprehensive survey of route recommendation methods and applications within the context of urban computing. It is organized into three main sections: methodology, application, and current challenges and future directions. The methodology section categorizes classic methods and modern deep learning approaches, highlighting their historical relations and advancements. The application section presents various novel applications of route recommendation in urban computing scenarios. The current challenges and future directions section discusses existing problems and proposes promising research directions, such as incorporating multi-modal data, enhancing user privacy, and integrating with large models. The survey aims to help researchers quickly understand the current state of route recommendation research and guide future research trends.