  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 traditional
machine learning 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.
