暴雨影响人流出行

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

In 2020, Nigeria suffered from both intensified flooding seasons due to climate change and the onset of the COVID-19 pandemic. This study investigates the interconnected dynamics of flooding, human mobility, and COVID-19 transmission in Nigeria during the 2020 flooding season. Utilizing a two-stage spatial panel econometric model, the study incorporates data from satellite imagery, mobility tracking, and regional COVID-19 case reports. The results show that flooding severity, along with heavy rainfall, significantly impacts daily human mobility, which in turn influences the spread of COVID-19. These findings highlight the complex challenges at the intersection of natural disasters and public health, underscoring the need for a national-scale flood management plan in developing countries such as Nigeria.

Abstract Understanding changes in mobility patterns during extreme weather is crucial for urban resilience. Existing studies often overlook the transitions between different transportation modes. This study develops a framework that measures the spatiotemporal anomalies of mobilities and builds transition paths across multiple modes to reveal how people adapt to extreme weather. Analyzing four extreme rainfall events in New York City, we find that Citibike riders are most sensitive to rainfall. In the absence of subway disruptions, they tend to switch to the subway. When the subway system is paralyzed, indicating flooding of the system by heavy rainfall, riders shift to For-Hire Vehicles, followed by taxis. Both demonstrate the value of flexible service in urban resilience. The paralysis-prone subway and the uneven distribution of flexible service indicate that the current transit infrastructure lacks coordination and is unprepared for climate change. Recommendations for enhancing urban resilience include upgrading and maintaining the subway system; enhancing inter-transportation-modal coordination; introducing amphibious transportation modes; improving pre-disaster awareness of inland populations; encouraging safety shared ride; and connecting affordable transition paths for underprivileged groups.

Background Precipitation anomalies are associated with a number of poor health outcomes. One potential consequence of precipitation extremes is human geographic mobility. We evaluated the associations between precipitation anomalies (droughts and heavy rains) and short-term mobility in 23 sub-Saharan African countries by linking satellite data on precipitation to cross-sectional representative surveys. Methods Using data from 23 Demographic and Health Surveys from 2011 to 2017, we estimated the associations between deviations in long-term rainfall trends and short-term mobility among 294,539 women and 136,415 men over 15 years of age. We fit multivariable logistic regression models to assess potential non-linear relationships between rainfall deviations and short-term mobility, adjusting for survey month and socio-demographic covariates, and stratified by participant gender. Furthermore, we assessed whether these associations differed by marital status. Results Rainfall deviations were associated with short-term mobility among women, but not men. The relationship between rainfall deviations and mobility among women was U-shaped, such that women had increased marginal probabilities of mobility in instances of both lower and heavier precipitation. Differences between married and unmarried women were also revealed: among married women, we found positive associations between both rainfall deviation extremes (drought and heavy rains) and mobility; however, among unmarried women, there was only a positive association for heavy rains. Conclusion Precipitation anomalies were associated with short-term mobility among women, which may be in turn associated with poor health outcomes. More research with longitudinal data is needed to elaborate the associations between weather shocks, mobility, and downstream health impacts.

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Understanding human mobility’s resilience during extreme rainfall is paramount for enhancing disaster response and urban resilience. Most studies, however, have overlooked the complexity of resilience patterns across scales, missing out on the varied spatial anomalies and their underlying causes. To bridge this gap, we propose a framework using massive individual trajectory data to dissect resilience patterns of human mobility across scales. By leveraging a dynamic network model, we quantify human mobility flows and employ resilience curves to determine resilience patterns at urban-agglomeration and regional scales. Our study, centering on the extreme rainfall from Typhoon Mawar, covers Osaka and Nagoya in Japan. The findings reveal a marked reduction in human movement, although the structure of mobility networks remains relatively unchanged. Based on the quadrant distribution of inflows and outflows, we reveal that the ratio of abnormal to normal resilience patterns in human mobility stands at approximately 3:2, a consistency maintained across both scales. Interestingly, abnormal resilience patterns are intricately linked to local geographical settings of the built environment, revealing disparities based on income, gender, and age. These insights are invaluable for policymakers to improve postdisaster recovery efforts and guide future urban infrastructure development toward greater resilience.

No abstract available

ABSTRACT Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyse human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighbourhoods. The analysis includes neighbourhood clustering to identify population characteristics and a comparative analysis of mobility patterns. Our findings show that taxi data tends to capture human mobility to and from locations such as Lower Manhattan, where taxi demand is consistently high, while often underestimating the volume of trips originating from areas with lower taxi demand, particularly in the suburbs of NYC. In contrast, SafeGraph data excels in capturing trips to and from areas where commuting by driving one’s own car is common, but underestimates trips in pedestrian-heavy areas. The comparative analysis also sheds new light on transportation mode choices for trips across various neighbourhoods. The results of this study underscore the importance of understanding the representativeness of human mobility big data and highlight the necessity for careful consideration when selecting the most suitable dataset for human mobility research.

Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyze human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. The analysis includes neighborhood clustering to identify population characteristics and a comparative analysis of mobility patterns. Our findings show that taxi data tends to capture human mobility to and from locations such as Lower Manhattan, where taxi demand is consistently high, while often underestimating the volume of trips originating from areas with lower taxi demand, particularly in the suburbs of NYC. In contrast, SafeGraph data excels in capturing trips to and from areas where commuting by driving one's own car is common, but underestimates trips in pedestrian-heavy areas. The comparative analysis also sheds new light on transportation mode choices for trips across various neighborhoods. The results of this study underscore the importance of understanding the representativeness of human mobility big data and highlight the necessity for careful consideration when selecting the most suitable dataset for human mobility research.

This article analyses the dynamics between rainfall variability, food insecurity and human mobility in eight case studies, namely Ghana, Tanzania, Guatemala, Peru, Bangladesh, India, Thailand and Vietnam. It covers a large spectrum of rainfall-related climatic events, including floods, drought, seasonal shifts and dry spells, and their impact on food insecurity and in turn on human mobility in approximately 1300 households in the eight case studies. It also summarizes the outcomes of focus group discussions and participatory research approach sessions held with communities in the villages that are affected by rainfall variability. The article compares the outcomes of the case studies and identifies the similarities and areas of overlap. It concludes that for some households – regardless of the case study – there is high potential for migration to be a successful adaptation strategy. Some other households rather find it hard to adapt to the situation in situ; among them, some cannot afford moving to other areas to improve their livelihoods and remain ‘trapped’ while others do move, but barely survive or are even subject to worse conditions. The article provides policy recommendations for policy-makers and practitioners that might be applicable for these, and also other countries exposed to the same climatic issues. Finally, the article provides an outlook with lessons learned for the benefit of future research.

One of the most urgent types of disaster risk for cities is rainfall flooding—a risk triggered by climate change and urbanization. Urban actors worldwide are working on a variety of strategies and actions to create flood resilience in cities. With the reality of flood-risk incidents on the increase globally, studies such as Ref. [1] are critical. More mobility research is needed to strengthen our understanding of flood responses, particularly in larger cities. Indeed, we do not currently have sufficient knowledge about actual mobility resilience through the phases of disasters incidents (pre-, during andpost-flood).Thepaper by Tang et al. [1] ismethodologically innovative in their focus, data use (mobile phone data) and mapping exercise. Studies such as these providehighly useful analysis and legitimate policy suggestions for human mobility in their response to the realities of an extreme urban flood. However, this kind of work will need to explore further ways to provide clarity on the actual appearance of floods in an urbanized environment. Clarity is crucial for understanding the 2021 Zhengzhou disaster and any other recent and future flood disasters, including for example Bangkok [2]. Assumptions tend to lean strongly on a set of assumed general characteristics of an urban flood. While our understanding of urban population and their capacities and mobilities is relatively strong (e.g. Ref. [3]), detailed insight into actual floodwater behavior in cities will need strong further research. A more realistic assessment of the level to which for example a pluvial flood impedes mobility requires attention on the following characteristics: floodwater depth (the level to which floodwaters exceed land levels, e.g. in meters), the emerging speed of floodwaters over an urban area (meters per time unit), the timing of water arrival (minutes or hours after a breach or storm), the duration of high water presence (e.g. in hours, days) and exposure (values and types of urban environments and infrastructures affected) (see e.g. Ref. [4]). Also detailed coping behaviors and socio-spatial inequalities related to mobility play a crucial role [5]. The Zhengzhou case [1] suggests that on a generic level floodwaters do not necessarily reduce human mobility fundamentally over an urban region. Evidently, flood-reduced mobility in some areas will be much more severe than elsewhere. It would be helpful to be clearer about when and where certain flood characteristics are seen to reduce mobility more or less strongly. It will be useful, therefore, for further research to evaluate flood disasters by time, magnitude and geography in much moredetail.Our understanding andpolicy decisionswould then acknowledge any variation in both flood types and characteristics of urban spaces much more convincingly.

Abstract Understanding the response of human mobility to disruptive weather events is beneficial for the development of urban risk mitigation and emergency response policies, thus enhancing urban resilience. Most human mobility studies relying on aggregate flow data inevitably neglect the heterogeneity of disaggregate travel patterns with distinctive spatiotemporal characteristics, causing the uncertainty problem for identifying meaningful travel behaviors. Moreover, there is a lack of robust methodological approaches to extracting stable and genuine travel patterns under normal or disruptive situations. To address these issues, this study proposes a data-driven approach to spatiotemporal flow decomposition based on non-negative matrix factorization. With sparseness factored in the decomposition, stable disaggregate travel patterns can be extracted from origin-destination mobility flows. By combining temporal, spatial, and urban functional perspectives, heterogeneous travel behaviors can be analyzed and inferred. With a case study of the Zhengzhou ‘7.20’ heavy rainfall in 2021, the most extreme rainfall ever recorded in China, this study validated the effectiveness of the proposed approach and managed to identify representative and interesting travel patterns and behaviors, facilitating a better understanding of human travel behaviors under external impacts. In practice, this study can provide valuable insights for coping strategies in the face of increasingly frequent disruptive events.

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

Abstract Widespread availability of geospatial data on movement and context presents opportunities for applying new methods to investigate the interactions between humans and weather conditions. Understanding the influence of weather on human behaviour is of interest for diverse applications, such as urban planning and traffic engineering. The effect of weather on movement behaviour can be explored through Context-Aware Movement Analysis (CAMA), which integrates movement geometry with its context. More specifically, we use multi-channel sequence analysis (MCSA) to represent a person's movement as a multi-dimensional sequence of states, describing either the type of movement or the state of the environment throughout time. Similar movement patterns can then be identified by comparing and aligning mobility sequences. In this paper we apply CAMA and MCSA to explore weather effects on human movement patterns. Data from a GPS tracking study in a Scottish town of Dunfermline are linked to weather data and converted into multi-channel sequences which are clustered into groups of similar behaviours under specific weather typologies. Our findings show that the CAMA + MCSA method can successfully identify the response of commuters to variations in environmental conditions. We also discuss our findings on how travel modes and time spent at different places are affected by meteorological conditions, mainly wind, but also rainfall, daylight duration, temperature, comfort and relative humidity.

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collected from mobile devices have enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting fair performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (90 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency. To promote the use of the dataset, we will host a human mobility prediction data challenge (`HuMob Challenge 2023') using the human mobility dataset, which will be held in conjunction with ACM SIGSPATIAL 2023.

Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility. Our proposed model is rigorously tested in the context of the HuMob Challenge 2023 -- a competition designed to evaluate the performance of prediction models on standardized datasets to predict human mobility. The challenge leverages two datasets encompassing urban-scale data of 25,000 and 100,000 individuals over a longitudinal period of 75 days. GeoFormer stands out as a top performer in the competition, securing a place in the top-3 ranking. Its success is underscored by performing well on both performance metrics chosen for the competition -- the GEO-BLEU and the Dynamic Time Warping (DTW) measures. The performance of the GeoFormer on the HuMob Challenge 2023 underscores its potential to make substantial contributions to the field of human mobility prediction, with far-reaching implications for disaster preparedness, epidemic control, and beyond.

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control policies, but may be hindered by incomplete data in some regions of the world. Here we explore the opportunity of using proxy data or models for individual mobility to describe commuting movements and predict the diffusion of infectious disease. We consider three European countries and the corresponding commuting networks at different resolution scales obtained from official census surveys, from proxy data for human mobility extracted from mobile phone call records, and from the radiation model calibrated with census data. Metapopulation models defined on the three countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data well capture the empirical commuting patterns, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from both sources of data - mobile phones and census - are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, however preserving the order of infection of newly infected locations. Match in the epidemic invasion pattern is sensitive to initial conditions: the radiation model shows higher accuracy with respect to mobile phone data when the seed is central in the network, while the mobile phone proxy performs better for epidemics seeded in peripheral locations. Results suggest that different proxies can be used to approximate commuting patterns across different resolution scales in spatial epidemic simulations, in light of the desired accuracy in the epidemic outcome under study.

Delay/Disruption-Tolerant Networks (DTNs) have been around for more than a decade and have especially been proposed to be used in scenarios where communication infrastructure is unavailable. In such scenarios, DTNs can offer a best-effort communication service by exploiting user mobility. Natural disasters are an important application scenario for DTNs when the cellular network is destroyed by natural forces. To assess the performance of such networks before deployment, we require appropriate knowledge of human mobility. In this paper, we address this problem by designing, implementing, and evaluating a novel mobility model for large-scale natural disasters. Due to the lack of GPS traces, we reverse-engineer human mobility of past natural disasters (focusing on 2010 Haiti earthquake and 2013 Typhoon Haiyan) by leveraging knowledge of 126 experts from 71 Disaster Response Organizations (DROs). By means of simulation-based experiments, we compare and contrast our mobility model to other well-known models, and evaluate their impact on DTN performance. Finally, we make our source code available to the public.

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling.

Level of emotional arousal of one's body changes in response to external stimuli in an environment. Given the risks involved while crossing streets, particularly at unsignalized mid-block crosswalks, one can expect a change in the stress level of pedestrians. In this study, we investigate the levels and changes in pedestrian stress, under different road crossing scenarios in immersive virtual reality. To measure the stress level of pedestrians, we used Galvanic Skin Response (GSR) sensors. To collect the required data for the model, Virtual Immersive Reality Environment (VIRE) tool is used, which enables us to measure participants' stress levels in a controlled environment. The results suggested that the density of vehicles has a positive effect, meaning as the density of vehicles increases, so does the stress level for pedestrians. It was noted that younger pedestrians have a lower amount of stress when crossing as compared to older pedestrians which have higher amounts of stress. Geometric variables have an impact on the stress level of pedestrians. The greater the number of lanes the greater the observed stress, which is due to the crossing distance increasing, while the walking speed remains the same.