A Survey of Route Recommendations: Methods, Applications, and Opportunities
- URL: http://arxiv.org/abs/2403.00284v2
- Date: Sat, 6 Apr 2024 07:02:46 GMT
- Title: A Survey of Route Recommendations: Methods, Applications, and Opportunities
- Authors: Shiming Zhang, Zhipeng Luo, Li Yang, Fei Teng, Tianrui Li,
- Abstract summary: This survey offers a comprehensive review of route recommendation work based on urban computing.
We categorize a large volume of traditional machine learning and modern deep learning methods.
We present numerous novel applications related to route commendation within urban computing scenarios.
- Score: 20.248023419047847
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Continual Learning for Smart City: A Survey [20.248023419047847]
Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments.
Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development.
arXiv Detail & Related papers (2024-04-01T07:59:29Z) - Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety.
We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj)
Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
arXiv Detail & Related papers (2024-03-21T05:57:27Z) - Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook [28.103555959143645]
We propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing.
We classify the methodology into four primary categories: feature-based, alignment-based, contrast-based, and generation-based fusion methods.
We further categorize multi-modal urban applications into seven types: urban planning, transportation, economy, public safety, society, environment, and energy.
arXiv Detail & Related papers (2024-02-29T16:56:23Z) - A sequential transit network design algorithm with optimal learning
under correlated beliefs [4.8951183832371]
This study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning to address the operation under limited data.
An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand.
For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City.
arXiv Detail & Related papers (2023-05-16T14:14:51Z) - Milestones in Autonomous Driving and Intelligent Vehicles Part I:
Control, Computing System Design, Communication, HD Map, Testing, and Human
Behaviors [72.63895188785922]
The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs.
arXiv Detail & Related papers (2023-05-12T02:32:01Z) - Deep Learning for Embodied Vision Navigation: A Survey [108.13766213265069]
"Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation.
This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey.
arXiv Detail & Related papers (2021-07-07T12:09:04Z) - The 5th AI City Challenge [51.83023045451549]
The fifth AI City Challenge attracted 305 participating teams across 38 countries.
The evaluation was conducted on both algorithmic effectiveness and computational efficiency.
Results show the promise of AI in Smarter Transportation.
arXiv Detail & Related papers (2021-04-25T19:15:27Z) - Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
We provide a systematic review of the techniques used in current conversational recommender systems (CRSs)
We summarize the key challenges of developing CRSs into five directions.
These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
arXiv Detail & Related papers (2021-01-23T08:53:15Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - Deep Learning on Traffic Prediction: Methods, Analysis and Future
Directions [32.25707921285397]
This paper provides a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives.
We first summarize the existing traffic prediction methods, and give a taxonomy.
Second, we list the state-of-the-art approaches in different traffic prediction applications.
arXiv Detail & Related papers (2020-04-18T08:28:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.