Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens’ travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: (1) Methodology-wise. We categorize a large volume of classic methods and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. (2) Application-wise. We present numerous novel applications related to route commendation within urban computing scenarios. (3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends.

中文翻译：

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路线推荐调查：方法、应用和机会

如今，先进的信息技术在全市范围内部署，大数据量和强大的计算资源正在推动现代城市发展的智能化。作为智能交通的重要组成部分，路线推荐及其应用的广泛应用，直接影响着市民的出行习惯。基于大数据（可能是多模式）开发智能高效的出行路线已成为路线推荐研究的核心挑战。我们的调查对基于城市计算的路线推荐工作进行了全面回顾。它由以下三个部分组成：（1）方法论。我们对大量经典方法和现代深度学习方法进行了分类。此外，我们还讨论了它们的历史关系并揭示了前沿进展。 (2)应用方面。我们提出了许多与城市计算场景中的路线推荐相关的新颖应用。 (3)我们讨论了当前的问题和挑战，并展望了几个有前景的研究方向。我们相信，本次调查可以帮助相关研究人员快速熟悉路线推荐研究的现状，进而指导未来的研究趋势。