Exploring the effect of spatial scales in studying urban mobility pattern

• URL: http://arxiv.org/abs/2506.16762v1
• Date: Fri, 20 Jun 2025 05:39:39 GMT
• Title: Exploring the effect of spatial scales in studying urban mobility pattern
• Authors: Hoai Nguyen Huynh,
• Abstract summary: This paper explores the impact of spatial scales on the performance of the gravity model in explaining urban mobility patterns using public transport flow data in Singapore.<n>Results indicate the existence of an optimal intermediate spatial scale at which the gravity model performs best.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Urban mobility plays a crucial role in the functioning of cities, influencing economic activity, accessibility, and quality of life. However, the effectiveness of analytical models in understanding urban mobility patterns can be significantly affected by the spatial scales employed in the analysis. This paper explores the impact of spatial scales on the performance of the gravity model in explaining urban mobility patterns using public transport flow data in Singapore. The model is evaluated across multiple spatial scales of origin and destination locations, ranging from individual bus stops and train stations to broader regional aggregations. Results indicate the existence of an optimal intermediate spatial scale at which the gravity model performs best. At the finest scale, where individual transport nodes are considered, the model exhibits poor performance due to noisy and highly variable travel patterns. Conversely, at larger scales, model performance also suffers as over-aggregation of transport nodes results in excessive generalisation which obscures the underlying mobility dynamics. Furthermore, distance-based spatial aggregation of transport nodes proves to outperform administrative boundary-based aggregation, suggesting that actual urban organisation and movement patterns may not necessarily align with imposed administrative divisions. These insights highlight the importance of selecting appropriate spatial scales in mobility analysis and urban modelling in general, offering valuable guidance for urban and transport planning efforts aimed at enhancing mobility in complex urban environments.

Related papers

• Street network sub-patterns and travel mode [0.5670066649802191]
  We classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration.<n>The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing.<n>These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.
  arXiv  Detail & Related papers  (2025-07-25T19:49:51Z)
• FloGAN: Scenario-Based Urban Mobility Flow Generation via Conditional GANs and Dynamic Region Decoupling [2.579904461452466]
  Existing generative models rely heavily on historical trajectories and often overlook evolving factors like changes in population density and land use.<n>Mechanistic approaches incorporate population density and facility distribution but assume static scenarios.<n>This study introduces a data-driven approach for generating origin-destination mobility flows tailored to simulated urban scenarios.
  arXiv  Detail & Related papers  (2025-07-16T09:12:38Z)
• TrajMoE: Spatially-Aware Mixture of Experts for Unified Human Mobility Modeling [10.338272381612112]
  We propose TrajMoE, a unified and scalable model for cross-city human mobility modeling.<n>TrajMoE addresses two key challenges: (1) inconsistent spatial semantics across cities, and (2) diverse urban mobility patterns.<n>Extensive experiments demonstrate that TrajMoE achieves up to 27% relative improvement over competitive mobility foundation models.
  arXiv  Detail & Related papers  (2025-05-24T12:17:47Z)
• Graph Network Modeling Techniques for Visualizing Human Mobility Patterns [8.537183852577687]
  We develop a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics.<n>We demonstrate how mobility data collected from trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.
  arXiv  Detail & Related papers  (2025-04-04T02:21:44Z)
• MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations [18.07010464073212]
  We propose a novel urban region representation learning model, which captures semantically meaningful embeddings from inflow and outflow mobility patterns.<n>We conduct experiments in Chicago, New York, and Washington, D.C. to predict income, educational attainment, and social vulnerability.
  arXiv  Detail & Related papers  (2025-02-05T06:18:43Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic [12.414957984956043]
  PriorMotion is a data integration framework designed to systematically uncover movement patterns through driving dynamics analysis.<n>Our approach combines multi-scale empirical observations with customized analytical tools to capture evolving spatial-temporal trends in urban traffic.
  arXiv  Detail & Related papers  (2024-12-05T09:56:24Z)
• 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)
• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
  We develop a novel generative deep learning approach for human mobility modeling and synthesis. It incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
  arXiv  Detail & Related papers  (2024-05-24T02:04:10Z)
• COLA: Cross-city Mobility Transformer for Human Trajectory Simulation [44.157114416533915]
  We develop a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework. COLA divides the Transformer into the private modules for city-specific characteristics and the shared modules for city-universal mobility patterns. Our implemented cross-city baselines have demonstrated its superiority and effectiveness.
  arXiv  Detail & Related papers  (2024-03-04T07:45:29Z)
• 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)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• 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)

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.