Human Mobility Patterns Research Articles

Identifying human mobility patterns using smart card data

ABSTRACT Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research.

Influence of residential built environment on human mobility in Xining: A mobile phone data perspective

The emergence of large-scale spatiotemporal trajectory data offers an excellent opportunity to characterize collective human mobility patterns and their relationship with the urban built environment. Such an approach can generate a complementary understanding of traditional individual travel behavior based on travel survey data. Using mobile phone data, this study aims to investigate how residential built environments affect residents’ mobility at an aggregated level. Specifically, three indicators (movement distance, activity space, and the number of stops) were derived from raw mobile phone data to characterize human mobility. The decay coefficients and average values were then employed to reveal the aggregated characteristics of movement distance, activity space, and the number of stops. Furthermore, linear regression was applied to examine the relationship between human mobility indicators and residential built environments. The results indicate that the living-built environment could better explain the activity space than the movement distance and number of stops. In addition, some differences in the relationship between human mobility and residential built environments are identified between weekdays and weekends. These findings could provide new insights into human mobility and its interaction with the built environment, thus advancing our understanding of human travel behavior from both individual and collective perspectives.

A data fusion approach with mobile phone data for updating travel survey-based mode split estimates

Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorisation techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit (all public transportation), motorised (cars and similar vehicles), active (pedestrian and cycle trips), and taxi (traditional taxi and ride-hailing services). Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. Findings also point to a rise in the share of taxi usage, and a drop in active mobility, suggesting a modal shift towards less sustainable modes of travel. We validate our findings comparing our updated estimates with official smart card transaction data. The consistency of findings with expert domain knowledge from the literature and historical transport usage trends further support the robustness of our approach.

Exploring intra-urban human mobility and daily activity patterns from the lens of dockless bike-sharing: A case study of Beijing, China

In recent years, the emergence of dockless bike-sharing has brought new ways of transportation, while also providing support for observing urban dynamics at a finer granularity. This study uses dockless bike-sharing data, combined with point of interest (POI) data, to explore intra-urban human mobility and daily activity patterns, using Beijing as a case study. Here, we employ spatial statistics and graph network to quantify the characteristics of travel behavior. Firstly, spatiotemporal statistics analysis was conducted to investigate the travel patterns and spatiotemporal differences of bike-sharing in Beijing. It was found that bike trips have clear peak hours and highly overlap with the subway lines, indicating that the bike-sharing system effectively completes the ‘last mile’ of travel in Beijing. Secondly, graph networks were used to reveal the core patterns and community structures of human mobility in Beijing and their connections to daily activities. Our findings indicate that there are different core structures of human mobility in Beijing, some dominated by a single core node, while others are formed by the aggregation of multiple cores. Finally, POI data was used to analyze human daily activity patterns. We analyzed the temporal characteristics and the hotspot areas of each daily activity in Beijing and found that transferring to other transportation modes is the most popular activity for people riding bikes, followed by shopping, working, and eating out. These findings provide a reference for understanding urban human dynamics and urban structure from the perspective of dockless bike-sharing.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study.

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue. Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Sensing Mobility and Routine Locations through Mobile Phone and Crowdsourced Data: Analyzing Travel and Behavior during COVID-19

The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic. Therefore, in this article we study human mobility patterns within Portugal, using mobile phone and crowdsourced data to compare the population’s mobility and routine locations after the pandemic’s peak. We use clustering algorithms to identify citizens’ stops and routine locations, at an antenna level, during the following months after the pandemic’s first wave and the same period of the following year. Results based on two mobile phone datasets showed a significant difference in mobility in the two periods. Nevertheless, routine locations slightly differ.

COVID-19 is linked to changes in the time-space dimension of human mobility.

Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.

Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income.

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 -August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June-August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism

Predicting the next location of human mobility and its semantic information can support recommendations for location-based services and trajectory mining, such as human mobility pattern recognition and sequential anomaly detection. Previous studies have ignored the implicit correlation between location and spatiotemporal information thereby limiting the model performance in terms of location prediction accuracy. In this study, we propose a GEMA-BiLSTM (Geographical Embedding and Multilayer Attention -Bidirectional Long Short-Term Memory) model to predict the next-location in users' mobility. The model combines location and spatiotemporal information to extract the semantics of human mobility. The results show that the model can accurately predict the next location with a high accuracy of 87.63%. Compared with BiLSTM-CNN, LSTM, CNN, and Markov, the location prediction accuracy of the model improved by 2.28%, 9.72%, 11.53%, and 17.64%, respectively. In addition, the model has the highest semantic prediction accuracy (75.35%). Compared with the BiLSTM-CNN model, the our method improves the semantic prediction accuracy for residential and industrial function areas by 4.79% and 5.37%, respectively. The accuracy of location prediction for different time periods indicates that the next location of human activity during morning rush and evening rush hours is the most difficult to predict, which corresponds to the increase in human travel demand. Moreover, weekday human activity patterns indicate that the commercial area is still very active at night, which may be linked to nighttime economic policies. This study could improve the accuracy of recommendations for location-based service applications.

The Bench - exploring exclusionary design through arts practices

ABSTRACT This paper takes point of departure in ongoing research into exclusionary design. Through design interventions such as leaning benches, steel spikes, water sprinklers, and barbed wire socially vulnerable and homeless people are kept from occupying and staying in public spaces in the cities around the world. Such exclusion by design contributes to “atmospheres of rejection” and affords new patterns of human mobility in the city. As the leaning (or otherwise modified) bench is one of the most predominant dark design interventions, this paper will single out the bench as one exemplary artefact to be explored. The bench carries both symbolic meaning, as well as it is a material artefact affording rest and occupancy. The design of benches and the positioning (or removal) of them are micro-installations that together with other urban furniture, makes-up the urban spaces as sites of congregation, exchange, and experience. The paper explores the bench on a background of three key terms: affordance, atmosphere, and material interpellation. The paper then turns to art practices in public spaces and illustrate how the socio-cultural significance of benches has been made visible by art interventions such as the “socially modified” benches by the artist Jeppe Hein. In the conclusion the paper draws out lines of enquiry for further research.

Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics

Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.

Non-linear impacts of COVID-19 pandemic on human mobility: Lessons from its variations across three pandemic waves

This study sought to understand how the COVID-19 pandemic influenced human mobility patterns in a city, how the influences varied across the pandemic wave phases, and how the influences differed between car and bus trips. Data for the pandemic and individual-level trips were collected from Daejeon Metropolitan City in South Korea. Three indices of trip demand and trip randomness were used to represent mobility patterns and were compared with the number of confirmed COVID-19 cases. The results demonstrated different responses to the pandemic by transportation mode and revealed non-linear impacts of the COVID-19 pandemic on mobility patterns. Car dependency intensified and car trip randomness more sensitively fluctuated as the pandemic spread. The COVID-19 impacts on mobility follow a power-law relationship that revealed the existence of lower bounds of citizens’ travel-related responses to the pandemic, which generally increased over time, but its trend differed according to trip demand and randomness as the pandemic continued. The findings demonstrate variations in mobility patterns due to the long-lasting pandemic, which could not be explained solely by trip demand. This information can assist in the formulation of flexible policies that adapt to changes in travel behaviors.

Convergence in Mobility Data Sets From Apple, Google, and Meta.

The higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people's movements, and consequently, the spread of the disease. During the onset of the pandemic, the technology companies Apple, Google, and Meta used their infrastructure to anonymously gather mobility reports from their users. This study aims to compare mobility data reports collected by Apple, Google, and Meta (formerly Facebook) during the COVID-19 pandemic and a major winter storm in Texas in 2021. We aim to explore the hypothesis that different people exhibit similar mobility trends during dramatic events and to emphasize the importance of this type of data for public health measures. The study also aims to promote evidence for companies to continue releasing mobility trends data, given that all 3 companies have discontinued these services. In this study, we collected mobility data spanning from 2020 to 2022 from 3 major tech companies: Apple, Google, and Meta. Our analysis focused on 58 countries that are common to all 3 databases, enabling us to conduct a comprehensive global-scale analysis. By using the winter storm that occurred in Texas in 20201 as a benchmark, we were able to assess the robustness of the mobility data obtained from the 3 companies and ensure the integrity of our findings. Our study revealed convergence in the mobility trends observed across different companies during the onset of significant disasters, such as the first year of the COVID-19 pandemic and the winter storm that impacted Texas in 2021. Specifically, we observed strong positive correlations (r=0.96) in the mobility data collected from different tech companies during the first year of the pandemic. Furthermore, our analysis of mobility data during the 2021 winter storm in Texas showed a similar convergence of trends. Additionally, we found that periods of stay-at-home orders were reflected in the data, with record-low mobility and record-high stay-at-home figures. Our findings provide valuable insights into the ways in which major disruptive events can impact patterns of human mobility; moreover, the convergence of data across distinct methodologies highlights the potential value of leveraging mobility data from multiple sources for informing public health decision-making. Therefore, we conclude that the use of mobility data is an asset for health authorities to consider during natural disasters, as we determined that the data sets from 3 companies yielded convergent mobility patterns. Comparatively, data obtained from a single source would be limited, and therefore, more difficult to interpret, requiring careful analysis.

Climate change adaptation responses and human mobility in the Mekong Delta: local perspectives from rural households in An Giang Province, Vietnam

Climate change influences the adaptation responses and mobility patterns of smallholder farmers across multiple scales. This study employed an inductive approach to observe smallholder farmers in An Giang Province in the Vietnamese Mekong Delta to compare the effects of various environmental and climate-related stressors on households with and without contributing migrant household members and on households of different income levels in two rural communes. We looked into the roles that adaptation responses and human mobility patterns play in the daily livelihoods of (translocal) households. We adopted a mixed-methods approach, which involved the administration of a livelihood survey among households in two rural communes (N = 106) and, subsequently, two focus group discussions, unstructured in-depth interviews, and secondary data analysis. We discovered that human mobility, adaptation responses, and climate change are interwoven in a web of complex relationships. No clear differences in effects and climate adaptation responses were discovered between emigrant and nonemigrant households. Hence, paradigms that either portray migration as a failure to adapt or as a form of adaptation in the context of climate change do not adequately explain the findings of this study. Differences between income groups were, however, observed. Relative to other income groups, middle-income farmers were disproportionally affected by climate-related disasters. Additionally, out-migration, aging, upstream hydropower development, and COVID-19 lockdowns posed significant challenges to the livelihoods of smallholder farmers. The compound effects of these multiple stressors indicate that human mobility, climate change and adaptation patterns should be best approached as ‘wicked’ problems.

Human mobility patterns are associated with experienced partisan segregation in US metropolitan areas

Partisan sorting in residential environments is an enduring feature of contemporary American politics, but little research has examined partisan segregation individuals experience in activity spaces through their daily activities. Relying on advances in spatial computation and global positioning system data on everyday mobility flows collected from smartphones, we measure experienced partisan segregation in two ways: place-level partisan segregation based on the partisan composition of its daily visitors and community-level experienced partisan segregation based on the segregation level of places visited by its residents. We find that partisan segregation experienced in places varies across different geographic areas, location types, and time periods. Moreover, partisan segregation is distinct from experienced segregation by race and income. We also find that partisan segregation individuals experience is relatively lower when they visit places beyond their residential areas, but partisan segregation in residential space and activity space is strongly correlated. Residents living in predominantly black, liberal, low-income, non-immigrant, more public transit-dependent, and central city communities tend to experience a higher level of partisan segregation.

Efficient and scalable DBSCAN framework for clustering continuous trajectories in road networks

Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate ε -neighborhood queries that directly retrieve the ε -neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms.

Using call detail records to determine mobility patterns of different socio-demographic groups in the western area of Sierra Leone during early COVID-19 crisis.

Human mobility patterns created from mobile phone call detail records (CDRs) can provide an essential resource in data-poor environments to monitor the effects of health outbreaks. Analysis of this data can be instrumental for understanding the movement pattern of populations allowing governments to set and refine policies to respond to community health risks. Building on CDR mobility analysis techniques, this research set out to test whether combining CDR mobility indicators with socio-economic information can illustrate differences between different socio-economic groups' exposure risks to COVID-19. The work focuses on the Western Area of Sierra Leone which houses the capital Freetown because it lacks existing mobility data and therefore can be a great example of how CDR can be transformed for this use. To determine mobility patterns, we applied the radius of gyration, regularity of movement, and motif types analytics commonly used in CDR research. We then applied a clustering algorithm to these results to understand user trends. Then we compared the results of the three methods with socio-economic status determined from census data in the same geography. The results show the daily movement of cell phone users of lower socio-economic status covered greater distances in the Western Area before and after lockdown, thereby showing a greater risk to COVID-19. The research also shows that groups of higher social status decreased mobility significantly after lockdown and did not return to pre-COVID-19 levels, unlike lower-social status groups.

Exploring Crowd Travel Demands Based on the Characteristics of Spatiotemporal Interaction between Urban Functional Zones

As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization.

Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach

We employed emerging smartphone-based location data and produced daily human mobility measurements using Nigeria as an application site. A data-driven analytical framework was developed for rigorously producing such measures using proven location intelligence and data-mining algorithms. Our study demonstrates the framework at the beginning of the SARS-CoV-2 pandemic and successfully quantifies human mobility patterns and trends in response to the unprecedented public health event. Another highlight of the paper is the assessment of the effectiveness of mobility-restricting policies as key lessons learned from the pandemic. We found that travel bans and federal lockdown policies failed to restrict trip-making behaviour, but had a significant impact on distance travelled. This paper contributes a first attempt to quantify daily human travel behaviour, such as trip-making behaviour and travelling distances, and how mobility-restricting policies took effect in sub-Saharan Africa during the pandemic. This study has the potential to enable a wide spectrum of quantitative studies on human mobility and health in sub-Saharan Africa using well-controlled, publicly available large data sets. Significance: The mobility measurements in this study are new and have filled a major data gap in understanding the change in travel behaviour during the SARS-CoV-2 pandemic in Nigeria. These measurements are derived from high-quality data samples by state-of-the-art data-driven methodologies and could be further adopted by other quantitative research related to human mobility. Additionally, this study evaluates the impact of mobility-restricting policies and the heterogeneous effects of socio-economic and socio-demographic factors by a time-dependent random effect model on human mobility. The quantitative model provides a decision-making basis for the Nigerian government to provide travel-related guidance and make decisions in future public health events. Open data set: https://github.com/villanova-transportation/Nigeria-mobility-COVID19-SAJS

Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation

Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity.