Attention-based Contextual Multi-View Graph Convolutional Networks for
Short-term Population Prediction
- URL: http://arxiv.org/abs/2203.00489v1
- Date: Tue, 1 Mar 2022 14:37:04 GMT
- Title: Attention-based Contextual Multi-View Graph Convolutional Networks for
Short-term Population Prediction
- Authors: Yuki Kubota, Yuki Ohira and Tetsuo Shimizu
- Abstract summary: We propose a novel deep learning model called Attention-based Contextual Graph Convolutional Networks (ACMV-GCNViews)
We first construct multiple graphs based on urban environmental information, and then ACM-GCNViews captures spatial correlations from various views with graph networks.
Using population count data collected through mobile phones, we demonstrate that our proposed model outperforms baseline methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Short-term future population prediction is a crucial problem in urban
computing. Accurate future population prediction can provide rich insights for
urban planners or developers. However, predicting the future population is a
challenging task due to its complex spatiotemporal dependencies. Many existing
works have attempted to capture spatial correlations by partitioning a city
into grids and using Convolutional Neural Networks (CNN). However, CNN merely
captures spatial correlations by using a rectangle filter; it ignores urban
environmental information such as distribution of railroads and location of
POI. Moreover, the importance of those kinds of information for population
prediction differs in each region and is affected by contextual situations such
as weather conditions and day of the week. To tackle this problem, we propose a
novel deep learning model called Attention-based Contextual Multi-View Graph
Convolutional Networks (ACMV-GCNs). We first construct multiple graphs based on
urban environmental information, and then ACMV-GCNs captures spatial
correlations from various views with graph convolutional networks. Further, we
add an attention module to consider the contextual situations when leveraging
urban environmental information for future population prediction. Using
statistics population count data collected through mobile phones, we
demonstrate that our proposed model outperforms baseline methods. In addition,
by visualizing weights calculated by an attention module, we show that our
model learns an efficient way to utilize urban environment information without
any prior knowledge.
Related papers
- StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model [12.789465279993864]
Geospatial predictions are crucial for diverse fields such as disaster management, urban planning, and public health.
We propose StreetViewLLM, a novel framework that integrates a large language model with the chain-of-thought reasoning and multimodal data sources.
The model has been applied to seven global cities, including Hong Kong, Tokyo, Singapore, Los Angeles, New York, London, and Paris.
arXiv Detail & Related papers (2024-11-19T05:15:19Z) - Diffusion-based Data Augmentation for Object Counting Problems [62.63346162144445]
We develop a pipeline that utilizes a diffusion model to generate extensive training data.
We are the first to generate images conditioned on a location dot map with a diffusion model.
Our proposed counting loss for the diffusion model effectively minimizes the discrepancies between the location dot map and the crowd images generated.
arXiv Detail & Related papers (2024-01-25T07:28:22Z) - Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series
Forecasting Approach [71.67506068703314]
Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services.
Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels.
In our research, we introduce a fresh perspective on urban mobility prediction.
Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
arXiv Detail & Related papers (2023-12-04T07:39:05Z) - Learning Dynamic Graphs from All Contextual Information for Accurate
Point-of-Interest Visit Forecasting [9.670949636600035]
Busyness Graph Neural Network (BysGNN) is a temporal graph neural network designed to learn and uncover the underlying multi-context correlations.
By incorporating all contextual, temporal, and spatial signals, we observe a significant improvement in our forecasting accuracy over state-of-the-art forecasting models.
arXiv Detail & Related papers (2023-06-28T05:14:03Z) - Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments.
Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion.
We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
arXiv Detail & Related papers (2022-06-29T07:49:48Z) - Predicting Future Occupancy Grids in Dynamic Environment with
Spatio-Temporal Learning [63.25627328308978]
We propose a-temporal prediction network pipeline to generate future occupancy predictions.
Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds.
We publicly release our grid occupancy dataset based on nulis to support further research.
arXiv Detail & Related papers (2022-05-06T13:45:32Z) - Effective Urban Region Representation Learning Using Heterogeneous Urban
Graph Attention Network (HUGAT) [0.0]
We propose heterogeneous urban graph attention network (HUGAT) for learning the representations of urban regions.
In our experiments on NYC data, HUGAT outperformed all the state-of-the-art models.
arXiv Detail & Related papers (2022-02-18T04:59:20Z) - Fine-Grained Population Mobility Data-Based Community-Level COVID-19
Prediction Model [5.548510262756311]
We propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction.
To mine as finer-grained patterns as possible for prediction, a spatial weighted aggregation module is introduced to aggregate the embeddings of CBGs to community level.
Our model outperforms existing forecasting models on community-level COVID-19 prediction.
arXiv Detail & Related papers (2022-02-13T08:40:47Z) - Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
We present a method for numerical taxonomy of urban form derived from biological systematics.
We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form.
After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
arXiv Detail & Related papers (2021-04-30T12:47:52Z) - Learning Geo-Contextual Embeddings for Commuting Flow Prediction [20.600183945696863]
Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development.
Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios.
We propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction.
arXiv Detail & Related papers (2020-05-04T17:45:18Z) - Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
Reasoning over visual data is a desirable capability for robotics and vision-based applications.
In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent.
Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
arXiv Detail & Related papers (2020-02-20T18:50:44Z)
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.