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.

基于城市计算的路径推荐研究综述了传统机器学习和现代深度学习方法在智能交通中的方法学应用、与城市计算场景相关的新应用、当前问题和挑战以及未来研究方向。

路径推荐综述：方法、应用与机遇

A Survey of Route Recommendations: Methods, Applications, and  Opportunities

Optimizing for humans' latent preferences is a grand challenge in route
recommendation, where globally-scalable solutions remain an open problem.
Although past work created increasingly general solutions for the application
of inverse reinforcement learning (IRL), these have not been successfully
scaled to world-sized MDPs, large datasets, and highly parameterized models;
respectively hundreds of millions of states, trajectories, and parameters. In
this work, we surpass previous limitations through a series of advancements
focused on graph compression, parallelization, and problem initialization based
on dominant eigenvectors. We introduce Receding Horizon Inverse Planning
(RHIP), which generalizes existing work and enables control of key performance
trade-offs via its planning horizon. Our policy achieves a 16-24% improvement
in global route quality, and, to our knowledge, represents the largest instance
of IRL in a real-world setting to date. Our results show critical benefits to
more sustainable modes of transportation (e.g. two-wheelers), where factors
beyond journey time (e.g. route safety) play a substantial role. We conclude
with ablations of key components, negative results on state-of-the-art
eigenvalue solvers, and identify future opportunities to improve scalability
via IRL-specific batching strategies.

本文提出了一种基于逆强化学习的新方法（RHIP），通过图形压缩、并行处理和基于特征向量的问题初始化等一系列方法，成功地解决了全局比例规模难题，并在实际交通环境中实现了更可持续的交通方案（例如，路线安全性），获得了全局路线质量的 16-24％的改善。