Human Mobility Patterns Research Articles

Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion

Trajectory generation is a vital element in AI applications. Firstly, it enables simulation such as traffic simulation and epidemic spreading modeling. Secondly, it can provide synthetic privacy-preserving data for training AI models. Notably, trajectory generation featuring controllable user profiles holds substantial value in generating customized mobility trajectories tailored to diverse requirements. However, relevant work is still lacking. On the one hand, traditional deep generative models fall short in guiding controllable trajectory generation due to the statistical nature of human mobility patterns and the corresponding insufficient control mechanisms. On the other hand, though the diffusion model has demonstrated strong generative capabilities in many fields, to achieve controllable generation on discrete trajectory data, we still need to redesign the structure of the continuous diffusion model. In this paper, we introduce a controllable trajectory generation framework that leverages a continuous diffusion model and classifier guidance for more robust condition control. Our proposed framework comprises two modules: a latent trajectory diffusion model and a trajectory classifier for profile guidance. Experiments on two real-world mobility datasets consistently demonstrate its capability of generating trajectories matching given user profiles and conforming to human mobility patterns. Our source code and trained models are released at https://github.com/tsinghua-fib-lab/User-Profile-Guided-Latent-Diffusion .

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

The impact of the built environment on human mobility patterns during Covid-19: A study of New York City's Open Streets Program

This study uses the Open Streets Program in New York City as a natural experiment to test the effects of change in street use on foot traffic changes during COVID-19. In a two-stage-least-squares (2SLS) design, the Open Streets Program is used as an instrumental variable to isolate the exogenous effect of expanded streets for pedestrians on foot traffic patterns. We then estimate a difference-in-differences model that compares the change in foot traffic to public points-of-interests (POI) in neighborhoods that are part of the Open Streets Program with those that are not “before and after” the start of the city-wide program, in addition to other controls such as street types and weather characteristics to help reduce the error variance of the regression. We find that the Open Streets Program helped increase pedestrian activity at a neighborhood level, even when controlling for street types and other confounding temporal factors such as precipitation and temperature.

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

Characterizing Temporal Patterns of Intra-Urban Human Mobility in Bike-Sharing through Trip Analysis: A Case Study of Shanghai, China

Human mobility, encompassing the movement of individuals and/or groups across space and time, significantly impacts various aspects of society, with intra-urban mobility being a major research focus of scholars in diverse disciplines. Bike-sharing systems have become an alternatives in cities for achieving more sustainable transportation. Hence, bike-sharing-related data are considered an important data source to study intra-urban human mobility. To better understand human mobility in cities, it is essential to characterize the typical patterns involved in intra-urban human mobility. This paper mainly focuses on characterizing the temporal patterns of intra-urban human mobility on bike-sharing based on the trip information of the acquired bike-sharing data. To achieve this, on the one hand, we adopted an exploratory data analysis (EDA) method to describe the temporal patterns by performing exploratory analyses of bike-sharing trips. On the other hand, we used the continuous triangular model (CTM) to conduct multi-temporal-scale analysis of bike-sharing trips for further explorations of the temporal patterns where necessary. The data of bike-sharing trips in Shanghai, China, were adopted as the dataset for the case study. Generally, the study was conducted at two different levels: the trip level and the bike level. Specifically, at each level, the explorations were conducted from varying perspectives. According to the analyses, numerous meaningful temporal patterns were discovered, and several distinctive findings were acquired. The results of this study show the effectiveness of the EDA and CTM methods in characterizing temporal patterns of intra-urban human mobility, based on which potentially insightful information and suggestions can be provided to assist related actions.

Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

AbstractThe potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Trajectory-User Linking via Multi-Scale Graph Attention Network

Trajectory-User Linking (TUL) aims to link anonymous trajectories to their owners, which is considered an essential task in discovering human mobility patterns. Although existing TUL studies have shown promising results, they still have specific defects in the perception of spatio-temporal properties of trajectories, which manifested in the following three problems: missing context of the original trajectory, ignorance of spatial information, and high computational complexity. To address those issues, we revisit the characteristics of the trajectory and propose a novel model called TULMGAT (TUL via Multi-Scale Graph Attention Network) based on masked self-attention graph neural networks. Specifically, TULMGAT consists of four components: construction of check-in oriented graphs, node embedding, trajectory embedding, and trajectory user linking. Sufficient experiments on two publicly available datasets have shown that TULMGAT is the state-of-the-art model in task TUL compared to the baselines with an improvement of about 8% in accuracy and only a quarter of the fastest baseline in runtime. Furthermore, model validity experiments have verified the role of each module.

Holocene to present-day coastal landscapes of Bar al Hikman (Oman): Neolithic waypoints on the shores of the Arabian Sea

Dramatic climatic and environmental changes over the last 12,000 years have significantly impacted Arabian coastal stratigraphy and human populations. The Bar Al Hikman peninsula (BAH), the largest low-lying area (1000 km2) along the Arabian Sea coast of Oman is a monsoon storm-dominated carbonate-evaporite system, where late Neolithic artifacts suggest human presence from at least 5.75–5.05 ka before the present (BP). Despite its archaeological significance at the crossroads of important Neolithic coastal sites in Dhofar, Masirah, and Ja'alan coast, paleoenvironmental interpretation, timing, and expression of relative sea level (RSL) changes in this coastal area remain poorly understood. Fortunately, the extensive size and arid climate of the area have preserved carbonate-dominated Mid-Late Holocene coastal geomorphologies exceptionally well, making this area an excellent geological archive to study past coastal environments, thereby understanding how environmental changes at BAH influenced Neolithic human mobility and settlement patterns. This study reconstructs the paleoenvironment of BAH over the last 12,000 years using a combination of field and remote sensing techniques, including satellite imagery, digital elevation models, bathymetric data, GIA modeling, and Sea Level Index Points to trace RSL history. The Holocene transgression began flooding the extensive continental shelf offshore BAH around 10 ka BP, progressively separating Masirah from the mainland through channel formation between 9.5 and 8.0 ka BP. It then inundated the present-day BAH peninsula around 7.7 ka BP, reaching a highstand of 2.5–3.2 m above present sea level by 6.0 ka BP (Mid-Holocene Highstand; MHHS), before gradually declining to the current level. Human presence documented at BAH (5.75–5.05 ka BP) coincides with the end of the mid-Holocene highstand, and the onset of the regression. This relative sea level history is contemporaneous to the aridification of Arabia, a period of transition from mangrove-dominated intertidal settings to coral reef and carbonate coastal barriers. The disappearance of mangrove-like gastropods around 5.4 cal ka BP and the subsequent appearance of significant coral fragments in the sediment indicates a critical change in terms of environmental settings with less nutrients and a warmer sea surface temperature). Comparing the findings at BAH to well-established nearby Neolithic sites on Masirah Island, the scarcity of prehistoric remains at BAH suggests that during the Mid-Holocene Highstand (MHHS), the small, rocky, paleo-low-lying islands at BAH may have served as waypoints and shelters between Masirah and the mainland.

Modeling the Spatial Dynamics and Human Mobility in Zika Virus Transmission: A Review

Spatial dynamics and human mobility significantly influence the transmission dynamics of vector-borne diseases like Zika virus. This paper reviews advancements in mathematical modeling that integrate spatial factors and human movement to enhance our understanding of disease transmission dynamics. Traditional SEIR (Susceptible-Exposed-Infectious-Recovered) models have been foundational but lack spatial heterogeneity and human mobility considerations. Recent studies have addressed these limitations by developing spatially explicit models that incorporate local interactions, environmental conditions, and human mobility patterns. By synthesizing findings from these studies, we identify strengths, limitations, and future research directions for improving predictive modeling of Zika virus transmission. Key insights include the amplifying effect of local interactions on disease spread and the critical role of human mobility networks in shaping transmission pathways. Addressing remaining challenges, such as refining spatial modeling techniques and integrating real-time data sources, will enhance the accuracy and applicability of spatial disease models in informing public health strategies.

Quantifying local mobility patterns in urban human mobility data

Understanding fine-scale dynamics of human mobility patterns is pivotal for effective urban planning, public health strategies, and retail analysis. This study introduces a novel mobility measure – the Local Mobility Index (LMI) – combining geometry-based mobility metrics and accessibility measures. The LMI can be considered a measure of ‘relative localness’ by integrating preferences into the assessment of local mobility patterns, offering a novel measure for understanding mobility behavior in urban contexts. The LMI improves upon existing measures as it captures individual choice for local destinations through measuring whether individuals select nearby destinations; accounting for the unequal spatial distribution of urban amenities. Our contribution is mainly methodological, advancing the field by introducing a metric that captures different aspects of mobility compared to conventional mobility metrics. Leveraging mobile-phone-based GPS data, we examine the LMI using 759 individuals across three cities in England. We found that the LMI captures a new and distinct dimension of urban mobility, as evidenced by its weak correlation with established metrics. Therefore, LMI's capacity to highlight previously undetected aspects of mobility behavior, underscores its importance for advancing research and urban planning.

Rare and highly destructive wildfires drive human migration in the U.S.

The scale of wildfire impacts to the built environment is growing and will likely continue under rising average global temperatures. We investigate whether and at what destruction threshold wildfires have influenced human mobility patterns by examining the migration effects of the most destructive wildfires in the contiguous U.S. between 1999 and 2020. We find that only the most extreme wildfires (258+ structures destroyed) influenced migration patterns. In contrast, the majority of wildfires examined were less destructive and did not cause significant changes to out- or in-migration. These findings suggest that, for the past two decades, the influence of wildfire on population mobility was rare and operated primarily through destruction of the built environment.

Mobility and the use of littoral resources in the Late Mesolithic of Northern Spain: the case of La Chora cave (Voto, Cantabria, N Spain)

Littoral resources have been consumed by humans since at least the Middle Palaeolithic. Examples of the use of molluscs have been documented along the shores of Europe during that period but it was not until many millennia later that European hunter-fisher-gatherer societies exploited those resources intensively—see the case of Nerja cave during the Younger Dryas. This economic activity caused the accumulation of shells at archaeological sites during the Mesolithic, resulting in the formation of the so-called shell middens, a very common type of deposit along the Atlantic seaboard of Europe. Despite the large number of research projects that have studied the exploitation of coastal environments and the way of life of Mesolithic populations, questions such as the relationship between human mobility and mollusc exploitation patterns still remain. The archaeomalacological study of the shell midden in La Chora cave (Cantabria, Spain) confirms that people foraged for shellfish at several places along the coast, mainly in the estuary of the River Asón. The main difference between La Chora and other Mesolithic sites is its longer shellfish collection radius as the inhabitants travelled over 10 km to the open coast to collect shellfish. This study has expanded the available data about the subsistence strategies of Mesolithic groups in a little-studied area and improved our knowledge of mobility patterns among Mesolithic societies in the northern Iberian Peninsula.

An integrated approach to analyze equitable access to food stores under disasters from human mobility patterns.

Limited access to food stores is often linked to higher health risks and lower community resilience. Socially vulnerable populations experience persistent disparities in equitable food store access. However, little research has been done to examine how people's access to food stores is affected by natural disasters. Previous studies mainly focus on examining potential access using the travel distance to the nearest food store, which often falls short of capturing the actual access of people. Therefore, to fill this gap, this paper incorporates human mobility patterns into the measure of actual access, leveraging large-scale mobile phone data. Specifically, we propose a novel enhanced two-step floating catchment area method with travel preferences (E2SFCA-TP) to measure accessibility, which extends the traditional E2SFCA model by integrating actual human mobility behaviors. We then analyze people's actual access to grocery and convenience stores across both space and time under the devastating winter storm Uri in Harris County, Texas. Our results highlight the value of using human mobility patterns to better reflect people's actual access behaviors. The proposed E2SFCA-TP measure is more capable of capturing mobility variations in people's access, compared with the traditional E2SFCA measure. This paper provides insights into food store access across space and time, which could aid decision making in resource allocation to enhance accessibility and mitigate the risk of food insecurity in underservedareas.

Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study

Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes. In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017-23). We constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25-April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6-March 1, 2021), for the purposes of model validation. We found that flights alone transported 79·9 million (95% CI 58·3-101·4 million) passengers annually within Brazil during 2017-22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25-April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6-16, 2021. By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts. Our results unlock the potential of designing country-wide clinical sample collection networks with mobility data-informed approaches, an innovative practice that can improve current surveillance systems. Rockefeller Foundation.

Unveiling community adaptations to extreme heat events using mobile phone location data

The escalating frequency, duration, and intensity of extreme heat events have posed a significant threat to human society in recent decades. Understanding the dynamic patterns of human mobility under extreme heat will contribute to accurately assessing the risk of extreme heat exposure. This study leverages an emerging geospatial data source, anonymous cell phone location data, to investigate how people in different communities adapt travel behaviors responding to extreme heat events. Taking the Greater Houston Metropolitan Area as an example, we develop two indices, the Mobility Disruption Index (MDI) and the Activity Time Shift Index (ATSI), to quantify diurnal mobility changes and activity time shift patterns at the city and intra-urban scales. The results reveal that human mobility decreases significantly in the daytime of extreme heat events in Houston while the proportion of activity after 8 p.m. is increased, accompanied with a delay in travel time in the evening. Moreover, these mobility-decreasing and activity-delaying effects exhibited substantial spatial heterogeneity across census block groups. Causality analysis using the Geographical Convergent Cross Mapping (GCCM) model combined with correlation analyses indicates that people in areas with a high proportion of minorities and poverty are less able to adopt heat adaptation strategies to avoid the risk of heat exposure. These findings highlight the fact that besides the physical aspect of environmental justice on heat exposure, the inequity lies in the population's capacity and knowledge to adapt to extreme heat. This research is the first of the kind that quantifies multi-level mobility for extreme heat responses, and sheds light on a new facade to plan and implement heat mitigations and adaptation strategies beyond the traditional approaches.

Content-Aware Human Mobility Pattern Extraction.

Extracting meaningful patterns of human mobility from accumulating trajectories is essential for understanding human behavior. However, previous works identify human mobility patterns based on the spatial co-occurrence of trajectories, which ignores the effect of activity content, leaving challenges in effectively extracting and understanding patterns. To bridge this gap, this study incorporates the activity content of trajectories to extract human mobility patterns, and proposes acontent-aware mobility pattern model. The model first embeds the activity content in distributed continuous vector space by taking point-of-interest as an agent and then extracts representative and interpretable mobility patterns from human trajectory sets using a derived topic model. To investigate the performance of the proposed model, several evaluation metrics are developed, including pattern coherence, pattern similarity, and manual scoring. A real-world case study is conducted, and its experimental results show that the proposed model improves interpretability and helps to understand mobility patterns. This study provides not only a novel solution and several evaluation metrics for human mobility patterns but also a method reference for fusing content semantics of human activities for trajectory analysis and mining.

Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction

The COVID-19 pandemic has dramatically transformed human mobility patterns. Therefore, human mobility prediction for the “new normal” is crucial to infrastructure redesign, emergency management, and urban planning post the pandemic. This paper aims to predict people’s number of visits to various locations in New York City using COVID and mobility data in the past two years. To quantitatively model the impact of COVID cases on human mobility patterns and predict mobility patterns across the pandemic period, this paper develops a model CCAAT-GCN ( C ross- and C ontext- A ttention based Spatial-Temporal G raph C onvolutional N etworks). The proposed model is validated using SafeGraph data in New York City from August 2020 to April 2022. A rich set of baselines are performed to demonstrate the performance of our proposed model. Results demonstrate the superior performance of our proposed method. Also, the attention matrix learned by our model exhibits a strong alignment with the COVID-19 situation and the points of interest within the geographic region. This alignment suggests that the model effectively captures the intricate relationships between COVID-19 case rates and human mobility patterns. The developed model and findings can offer insights into the mobility pattern prediction for future disruptive events and pandemics, so as to assist with emergency preparedness for planners, decision-makers and policymakers.

Human Mobility Patterns during the 2024 Total Solar Eclipse in Canada

This study explores human mobility patterns during the 2024 total solar eclipse in Canada, leveraging de-identified network mobility data from TELUS Communications. We compare travel patterns during the total solar eclipse with a baseline period by averaging the visitor counts from April 15th to 19th, then calculate the change in visitor counts during the total solar eclipse relative to this baseline period (hereafter adjusted visitor counts). Using these adjusted visitor counts, we estimate that 589,290 Canadians traveled to areas within the path of totality to observe the eclipse. The findings highlight significant inter-provincial travel, with major influxes of visitors to Ontario, particularly near Lake Erie. We found significant evidence of a distance decay effect in the adjusted traveller counts to the path of totality. This study demonstrates the utility of de-identified network mobility data in understanding the dynamics of human mobility during once-in-a-lifetime events.

An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis

ABSTRACT A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips’ destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips’ origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips’ geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.

A visit generation process for human mobility random graphs with location-specific latent-variables: From land use to travel demand

This research introduces a novel mathematical framework for understanding collective human mobility patterns, integrating mathematical modeling and data analysis. It focuses on latent-variable networks to investigate the dynamics of human mobility using stochastic models. By analyzing origin–destination data, the study uncovers scaling relations and explores the economic implications of mobility patterns, particularly regarding the income elasticity of travel demand. The mathematical analysis begins with the development of a stochastic model based on inhomogeneous random graphs, constructing a visitation model with multipurpose drivers for travel demand. Through this model, the study gains insights into the structural properties and dynamic correlations of human mobility networks, deriving analytical solutions for key network metrics: visit distribution, assortativity behavior and clustering coefficient. Empirically, the study validates the model’s assumptions and reveals scaling behaviors in origin–destination flows within a region, reproducing statistical regularities observed in real-world cases. Notably, the model’s application to estimating income elasticity of travel demand provides significant implications for urban and transport economics. Overall, this research contributes to a deeper understanding of the interplay between human mobility and regional demographics and economics. It sheds light on critical scaling relations across various aspects of collective human mobility and underscores the importance of incorporating latent-variable structures into mobility modeling for accurate economic analysis and decision-making in urban and transportation planning.