Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning

• URL: http://arxiv.org/abs/2403.16049v2
• Date: Sun, 26 May 2024 04:58:09 GMT
• Title: Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning
• Authors: Sangjoon Park, Yongsung Kwon, Hyungjoon Soh, Mi Jin Lee, Seung-Woo Son,
• Abstract summary: In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. We apply this method to public bicycle rental-and-return data in Seoul, South Korea.
• Score: 2.684349361878955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rental-and-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.

Related papers

• TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version [24.544665297938437]
  Topological Evolution-aware Framework (TEAM) for traffic forecasting incorporates convolution and attention. TEAM is capable of much lower re-training costs than existing methods are without jeopardizing forecasting accuracy.
  arXiv  Detail & Related papers  (2024-10-24T22:50:21Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• XXLTraffic: Expanding and Extremely Long Traffic Dataset for Ultra-Dynamic Forecasting Challenges [3.7509821052818118]
  XXLTraffic is the largest available public traffic dataset with the longest timespan and increasing number of sensor nodes. Our dataset supplements existing-temporal data resources and leads to new research directions in this domain.
  arXiv  Detail & Related papers  (2024-06-18T15:06:22Z)
• 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)
• An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments [51.99765487172328]
  We have developed a new framework using time-series analysis for dynamically assigning mobile network traffic prediction models. Our framework employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies.
  arXiv  Detail & Related papers  (2023-11-30T18:58:38Z)
• Deep trip generation with graph neural networks for bike sharing system expansion [7.737133861503814]
  We propose a graph neural network (GNN) approach to predicting the station-level demand based on multi-source urban built environment data. The proposed approach can be regarded as a generalized spatial regression model, indicating the commonalities between spatial regression and GNNs.
  arXiv  Detail & Related papers  (2023-03-20T16:43:41Z)
• 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)
• Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context [0.0]
  Recent contributions from deep learning and distance-based predictors show limited success on forecasting bike sharing demand. This work proposes a comprehensive set of new principles to incorporate both historical and prospective sources of spatial, meteorological, situational and calendrical context in predictive models of station demand.
  arXiv  Detail & Related papers  (2021-05-03T18:53:58Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.