Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems

• URL: http://arxiv.org/abs/2508.03747v2
• Date: Fri, 12 Sep 2025 14:48:48 GMT
• Title: Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems
• Authors: Xinyu Chen, Qi Wang, Yunhan Zheng, Nina Cao, HanQin Cai, Jinhua Zhao,
• Abstract summary: This study first quantifies the periodicity in complex human mobility data and then discovers periodic patterns.<n>We apply the framework to large-scale metro passenger flow data in Hangzhou China and multi-modal mobility data in New York City and Chicago, USA.
• Score: 13.241642295147878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human mobility regularity is crucial for understanding urban dynamics and informing decision-making processes. This study first quantifies the periodicity in complex human mobility data as a sparse identification of dominant positive auto-correlations in time series autoregression and then discovers periodic patterns. We apply the framework to large-scale metro passenger flow data in Hangzhou, China and multi-modal mobility data in New York City and Chicago, USA, revealing the interpretable weekly periodicity across different spatial locations over past several years. The analysis of ridesharing data from 2019 to 2024 demonstrates the disruptive impact of the pandemic on mobility regularity and the subsequent recovery trends. In 2024, the periodic mobility patterns of ridesharing, taxi, subway, and bikesharing in Manhattan uncover the regularity and variability of these travel modes. Our findings highlight the potential of interpretable machine learning to discover spatiotemporal mobility patterns and offer a valuable tool for understanding urban systems.

Related papers

• The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region [64.30214722988666]
  This paper describes the survey design, collection protocol, processing methodology, and characteristics of the released dataset.<n>The dataset includes three components: (i) an Individuals database describing demographic, socioeconomic, and household characteristics; (ii) a Trips database with over 80,000 annotated displacements including timestamps, transport modes, and trip purposes; and (iii) a Raw GPS Traces database comprising about 500 million high-frequency points.
  arXiv  Detail & Related papers  (2025-06-06T09:22:21Z)
• Traffic and Mobility Optimization Using AI: Comparative Study between Dubai and Riyadh [0.0]
  We will explore how AI can be used to understand the traffic and mobility related issues and its effects on the residents sentiment.<n>The approach combines real-time traffic data with geo-located sentiment analysis, offering a comprehensive and dynamic approach to urban mobility planning.
  arXiv  Detail & Related papers  (2025-05-15T15:07:00Z)
• Geospatial and Temporal Trends in Urban Transportation: A Study of NYC Taxis and Pathao Food Deliveries [0.0]
  This study analyzes transportation patterns using two datasets: the NYC Taxi Trip dataset from New York City and the Pathao Food Trip dataset from Dhaka, Bangladesh.<n>Our goal is to identify key trends in demand, peak times, and important geographical hotspots.
  arXiv  Detail & Related papers  (2025-05-02T17:41:17Z)
• A Pseudo Markov-Chain Model and Time-Elapsed Measures of Mobility from Collective Data [0.0]
  We develop a pseudo Markov-chain model to understand time-elapsed flows, over multiple intervals, from time and space aggregated collective inter-location trip data, given as a time-series.<n>We develop measures of mobility that parallel those known for individual mobility data, such as the radius of gyration.<n>We apply these measures to the NetMob 2024 Data Challenge data, and obtain interesting results that are consistent with published statistics and commuting patterns in cities.
  arXiv  Detail & Related papers  (2025-02-06T15:46:43Z)
• 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)
• InclusiViz: Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation [41.758626973743525]
  InclusiViz is a novel visual analytics system for multi-level analysis of urban segregation.<n>We developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI.<n>The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail.
  arXiv  Detail & Related papers  (2025-01-07T07:50:36Z)
• GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [81.82487256783674]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.<n>This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction [2.807532512532818]
  Current data-driven models often struggle with data sparsity and the integration of diverse urban data sources. We introduce a deep dynamic learning framework designed for traffic accident prediction. It incorporates dual adaptive graph learning mechanisms that enable high-order cross-regional learning. It also employs an advance attention mechanism to fuse multiple views of accident data and urban functional features.
  arXiv  Detail & Related papers  (2024-07-24T21:10:34Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• 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)
• Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study [5.5114073907045205]
  understanding huge-temporal traffic patterns from this data is crucial. Case study is conducted to understand variations in the variations intemporal traffic patterns during CO-19VID. outputs will be useful in the fields of traffic management and management in various stages of pandemic or unavoidable in-relation to road traffic.
  arXiv  Detail & Related papers  (2021-11-05T16:24:43Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode Classification [0.0]
  We propose the use of a feature retained from the Ordinal Pattern Transition Graph, called the probability of self-transition for transportation mode classification. The proposed feature presents better accuracy results than Permutation Entropy and Statistical Complexity, even when these two are combined.
  arXiv  Detail & Related papers  (2020-07-16T23:25:09Z)
• K-Prototype Segmentation Analysis on Large-scale Ridesourcing Trip Data [0.0]
  This study examines emerging patterns of mobility using recently released City algorithm of Chicago public ridesourcing data. The goal is to investigate the systematic variations in patronage of ride-hailing. Six ridesourcing prototypes are identified and discussed based on significant differences in relation to adverse weather conditions.
  arXiv  Detail & Related papers  (2020-06-24T17:53:26Z)
• Extracting Spatiotemporal Demand for Public Transit from Mobility Data [0.0]
  Changing urban demographics pose several challenges to efficient management of transit services. We propose a method to identify a simple method to estimate demand for public transit in a city.
  arXiv  Detail & Related papers  (2020-06-05T10:21:31Z)

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.