Flow descriptors of human mobility networks

• URL: http://arxiv.org/abs/2003.07279v1
• Date: Mon, 16 Mar 2020 15:27:00 GMT
• Title: Flow descriptors of human mobility networks
• Authors: David Pastor-Escuredo, Enrique Frias-Martinez
• Abstract summary: We propose a systematic analysis to characterize mobility network flows and topology and assess their impact into individual traces. This framework is suitable to assess urban planning, optimize transportation, measure the impact of external events and conditions, monitor internal dynamics and profile users according to their movement patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mobile phone data has enabled the timely and fine-grained study human mobility. Call Detail Records, generated at call events, allow building descriptions of mobility at different resolutions and with different spatial, temporal and social granularity. Individual trajectories are the basis for long-term observation of mobility patterns and identify factors of human dynamics. Here we propose a systematic analysis to characterize mobility network flows and topology and assess their impact into individual traces. Discrete flow-based descriptors are used to classify and understand human mobility patterns at multiple scales. This framework is suitable to assess urban planning, optimize transportation, measure the impact of external events and conditions, monitor internal dynamics and profile users according to their movement patterns.

Related papers

• Be More Real: Travel Diary Generation Using LLM Agents and Individual Profiles [21.72229002939936]
  This study presents an agent-based framework (MobAgent) to generate realistic trajectories conforming to real world contexts. We validate our framework with 0.2 million travel survey data, demonstrating its effectiveness in producing personalized and accurate travel diaries. This study highlights the capacity of LLMs to provide detailed and sophisticated understanding of human mobility through the real-world mobility data.
  arXiv  Detail & Related papers  (2024-07-10T09:11:57Z)
• 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)
• A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks [42.18264406168735]
  This study introduces a causal intervention framework to assess the impact of mobility-related factors on neural networks designed for next location prediction. We produce location sequences with distinct mobility behaviors, thereby facilitating the simulation of diverse yet realistic spatial and temporal changes. The framework is expected to promote the use of causal inference to enhance the interpretability and robustness of neural networks in mobility applications.
  arXiv  Detail & Related papers  (2023-11-20T13:21:10Z)
• Spatio-Temporal Branching for Motion Prediction using Motion Increments [55.68088298632865]
  Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques. We propose a noveltemporal-temporal branching network using incremental information for HMP.
  arXiv  Detail & Related papers  (2023-08-02T12:04:28Z)
• Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation [35.89805766554052]
  We propose a novel framework to model the dynamictemporal effects of locations, namely SRpatio-Augmented gaph neural networks. The STAR framework designs varioustemporal graphs to capture the behaviors correspondence and builds a novel branch to simulate the varying dwells in locations, which duration is finally optimized in an adversarial manner.
  arXiv  Detail & Related papers  (2023-06-15T11:47:45Z)
• Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark [49.58762201363483]
  We introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments. We propose a Dual-stream Motion-Scenario Decoupling framework that effectively separates scenario-oriented and motion-oriented features. We demonstrate significant performance improvements of the proposed textitDMSD framework on different difficulty-level tasks.
  arXiv  Detail & Related papers  (2023-05-17T14:14:31Z)
• On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
  We propose a socioeconomic-aware deep model called DeepSEI. The DeepSEI model incorporates two networks called deep network and recurrent network. We conduct extensive experiments on real mobility records data, POI data and house prices data.
  arXiv  Detail & Related papers  (2022-11-15T15:07:45Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• AttnMove: History Enhanced Trajectory Recovery via Attentional Network [15.685998183691655]
  We propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-03T15:45:35Z)
• Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
  Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing. Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
  arXiv  Detail & Related papers  (2020-09-23T14:56:19Z)
• TraLFM: Latent Factor Modeling of Traffic Trajectory Data [16.010576606023417]
  We propose a novel generative model called TraLFM to mine human mobility patterns underlying traffic trajectories. TraLFM is based on three key observations: (1) human mobility patterns are reflected by the sequences of locations in the trajectories; (2) human mobility patterns vary with people; and (3) human mobility patterns tend to be cyclical and change over time.
  arXiv  Detail & Related papers  (2020-03-16T04:41:39Z)

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.