# Urban flows prediction from spatial-temporal data using machine learning: A survey

arXiv: https://arxiv.org/abs/1908.10218 · HTML: https://mlsanity.com/paper/1908.10218/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey](https://mlsanity.com/paper/2102.09759/) (2021)
- [Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting](https://mlsanity.com/paper/2202.12586/) (2022)
- [An Empirical Experiment on Deep Learning Models for Predicting Traffic Data](https://mlsanity.com/paper/2105.05504/) (2021)
- [Review of the state of the art in autonomous artificial intelligence](https://mlsanity.com/paper/2210.10659/) (2022)
- [Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather](https://mlsanity.com/paper/2204.09792/) (2022)

## Abstract (arXiv)

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.