FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation

• URL: http://arxiv.org/abs/2307.11214v1
• Date: Thu, 20 Jul 2023 19:56:30 GMT
• Title: FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation
• Authors: Zhewei Liu, Lipai Huang, Chao Fan, Ali Mostafavi
• Abstract summary: We present a novel, fairness-aware deep learning model, FairMobi-Net, for inter-region human flow prediction. We validate the model using comprehensive human mobility datasets from four U.S. cities, predicting human flow at the census-tract level. The model maintains a high degree of accuracy consistently across diverse regions, addressing the previous fairness concern.
• Score: 2.30238915794052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management. However, a notable shortcoming of most existing mobility generation methodologies is neglect of prediction fairness, which can result in underestimation of mobility flows across regions with vulnerable population groups, potentially resulting in inequitable resource distribution and infrastructure development. To overcome this limitation, our study presents a novel, fairness-aware deep learning model, FairMobi-Net, for inter-region human flow prediction. The FairMobi-Net model uniquely incorporates fairness loss into the loss function and employs a hybrid approach, merging binary classification and numerical regression techniques for human flow prediction. We validate the FairMobi-Net model using comprehensive human mobility datasets from four U.S. cities, predicting human flow at the census-tract level. Our findings reveal that the FairMobi-Net model outperforms state-of-the-art models (such as the DeepGravity model) in producing more accurate and equitable human flow predictions across a variety of region pairs, regardless of regional income differences. The model maintains a high degree of accuracy consistently across diverse regions, addressing the previous fairness concern. Further analysis of feature importance elucidates the impact of physical distances and road network structures on human flows across regions. With fairness as its touchstone, the model and results provide researchers and practitioners across the fields of urban sciences, transportation engineering, and computing with an effective tool for accurate generation of human mobility flows across regions.

Related papers

• Comparing Fairness of Generative Mobility Models [3.699135947901772]
  This work examines the fairness of generative mobility models, addressing the often overlooked dimension of equity in model performance across geographic regions. Predictive models built on crowd flow data are instrumental in understanding urban structures and movement patterns. We propose a novel framework for assessing fairness by measuring utility and equity of generated traces.
  arXiv  Detail & Related papers  (2024-11-07T06:01:12Z)
• 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)
• Pre-trained Transformer Uncovers Meaningful Patterns in Human Mobility Data [0.0]
  We show that a transformer pre-trained on country-scale unlabeled human mobility data learns embeddings capable of developing a deep understanding of the target geography. We evaluate the performance of our pre-trained embeddings in encapsulating a broad spectrum of concepts related to human mobility.
  arXiv  Detail & Related papers  (2024-06-06T12:59:46Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Reinforcement Learning with Human Feedback for Realistic Traffic Simulation [53.85002640149283]
  Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge. This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
  arXiv  Detail & Related papers  (2023-09-01T19:29:53Z)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Enhancing crowd flow prediction in various spatial and temporal granularities [0.02578242050187029]
  We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.
  arXiv  Detail & Related papers  (2022-03-12T12:03:47Z)
• FloMo: Tractable Motion Prediction with Normalizing Flows [0.0]
  We model motion prediction as a density estimation problem with a normalizing flow between a noise sample and the future motion distribution. Our model, named FloMo, allows likelihoods to be computed in a single network pass and can be trained directly with maximum likelihood estimation. Our method achieves state-of-the-art performance on three popular prediction datasets, with a significant gap to most competing models.
  arXiv  Detail & Related papers  (2021-03-05T11:35:27Z)
• Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information [1.479639149658596]
  Existing solutions to flow generation are mainly based on mechanistic approaches. We propose the MultiFeature Deep Gravity model as an effective solution to flow generation. Our experiments, conducted on commuting flows in England, show that the MFDG model achieves a significant increase in the performance.
  arXiv  Detail & Related papers  (2020-12-01T13:49:46Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

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.