# Attentive Crowd Flow Machines

arXiv: https://arxiv.org/abs/1809.00101 · HTML: https://mlsanity.com/paper/1809.00101/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2018.

## Built on by (most cited first, 22 of 22)

- [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2402.11838/) (2024)
- [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://mlsanity.com/paper/2309.13363/) (2023)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation](https://mlsanity.com/paper/2107.00946/) (2021)
- [Diffusion Transformers as Open-World Spatiotemporal Foundation Models](https://mlsanity.com/paper/2411.12164/) (2024)
- [UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction](https://mlsanity.com/paper/2411.12972/) (2024)
- [Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction](https://mlsanity.com/paper/2111.15119/) (2021)
- [Road Network Guided Fine-Grained Urban Traffic Flow Inference](https://mlsanity.com/paper/2109.14251/) (2021)
- [Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction](https://mlsanity.com/paper/2501.13794/) (2025)
- [Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach](https://mlsanity.com/paper/2312.01699/) (2023)
- [Multi-modal Crowd Counting via Modal Emulation](https://mlsanity.com/paper/2407.19491/) (2024)
- [STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction](https://mlsanity.com/paper/2107.04980/) (2021)
- [STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models](https://mlsanity.com/paper/2501.03583/) (2025)
- [LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction](https://mlsanity.com/paper/2507.17795/) (2025)
- [Vision-LLMs for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/2510.11282/) (2025)
- [Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting](https://mlsanity.com/paper/2206.03128/) (2022)
- [Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data](https://mlsanity.com/paper/2206.13274/) (2022)
- [Prediction of Tourism Flow with Sparse Geolocation Data](https://mlsanity.com/paper/2308.14516/) (2023)
- [Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems](https://mlsanity.com/paper/2502.11013/) (2025)
- [TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction](https://mlsanity.com/paper/2507.00945/) (2025)
- [Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic](https://mlsanity.com/paper/2511.17532/) (2025)
- [One-Shot Crowd Counting With Density Guidance For Scene Adaptation](https://mlsanity.com/paper/2602.07955/) (2026)

## Abstract (arXiv)

Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine~(ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism. Specifically, the ACFM is composed of two progressive ConvLSTM units connected with a convolutional layer for spatial weight prediction. The first LSTM takes the sequential flow density representation as input and generates a hidden state at each time-step for attention map inference, while the second LSTM aims at learning the effective spatial-temporal feature expression from attentionally weighted crowd flow features. Based on the ACFM, we further build a deep architecture with the application to citywide crowd flow prediction, which naturally incorporates the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks (i.e., crowd flow in Beijing and New York City) show that the proposed method achieves significant improvements over the state-of-the-art methods.