Fine-grained urban flow inference based on diffusion models with incomplete data

Yuhao ZHENG; Senzhang WANG

doi:10.11959/j.issn.2096-6652.202330

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Fine-grained urban flow inference based on diffusion models with incomplete data

Special Topic: Diffusion Model and Artificial Intelligence Generated Content | 更新时间：2024-04-04

- Fine-grained urban flow inference based on diffusion models with incomplete data
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 3, Pages: 389-396(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202330
  CLC： TP39
- Revised：2023-07-05，
  
  Online First：2023-09，
  
  Published：15 September 2023
- 稿件说明：
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Yuhao ZHENG, Senzhang WANG. Fine-grained urban flow inference based on diffusion models with incomplete data[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 389-396.
DOI：

Yuhao ZHENG, Senzhang WANG. Fine-grained urban flow inference based on diffusion models with incomplete data[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 389-396. DOI： 10.11959/j.issn.2096-6652.202330.

摘要

为了获取城市每个路段上精细的交通流数据，需要部署大量的传感装置以及较密集的观测站，这会增加日常运营与设备维护的成本。同时，传统的交通流数据采集技术存在很多噪声和误差，检测得到的数据结果并不能保证其真实可靠。因此，如何利用粗粒度、混入噪声的传感器观测数据推断细粒度的城市交通流，是一个重要的研究课题。针对上述问题，提出了一种基于时空注意力的去噪扩散模型，提供细粒度的城市交通基础数据，以满足不同场景下的交通需求，为交通规划与智能交通系统构建奠定基础。

Abstract

To obtain detailed traffic flow data for each road segment of the city

it is necessary to deploy a large number of sensing devices and dense observation stations

which increases the costs of daily operations and equipment maintenance.At the same time

traditional traffic flow survey techniques are noisy and inaccurate

and the reliability of the detected data results is not guaranteed.Therefore

inferring fine-grained urban traffic flow based on coarse-grained and noiseinclusive sensor observations has become an important research topic.To address the above problems

we proposed a denoising diffusion model based on spatio-temporal attention

with the intention of providing fine-grained urban traffic base data in different scenarios of traffic demand

and laying the foundation for traffic planning and intelligent transportation system construction.

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references

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