Spatial-temporal Mobility Research Articles

Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

AI-Enabled Spatial-Temporal Mobility Awareness Service Migration for Connected Vehicles

In the future 6 G intelligent transportation system, the edge server will bring great convenience to the timely computing service for connected vehicles. To guarantee the quality of service, the time-critical services need to be migrated according to the future location of the vehicle. However, predicting vehicle mobility is challenging due to the time-varying of road traffic and the complex mobility patterns of vehicles. To address this issue, a spatial-temporal awareness proactive service migration strategy is proposed in this paper. First, a spatial-temporal neural network is designed to obtain accurate mobility by using gated recurrent units and graph convolutional layers extracting features from spatial road traffic and multi-time scales driving data. Then a proactive migration method is proposed to guarantee the reliability of services and reduce energy consumption. Considering the reliability of services and the real-time workload of servers, the migration problem is modeled as a multi-objective optimization problem, and the Lyapunov optimization method is utilized to obtain utility-optimal migration decisions. Extensive simulations based on real-world datasets are performed to validate the performance of the proposed method. The results show that the proposed method achieved 6% higher prediction accuracy, 10% lower dropping rate, and 10% lower energy consumption compared to state-of-the-art methods.

Spatial–Temporal Urban Mobility Pattern Analysis During COVID-19 Pandemic

In response to the repeated outbreaks of the COVID-19, many countries implement the region-specific, multilevel epidemic prevention and control policies. To fully understand the impact of these interventions on urban mobility, it is urgent to analyze spatial–temporal mobility pattern at the neighborhood level and structural changes in urban mobility networks. Here, we construct urban mobility networks among points of interest (POIs), using large-scale anonymous mobility data from de-identified mobile phone users. We comprehensively investigate the changes of urban mobility networks during two waves of the COVID-19 pandemic in Beijing from both graph and subgraph perspectives. Beyond an overall mobility reduction in Beijing, we find that the mobility change is spatially and temporally heterogeneous among different urban regions. We uncover a disproportionately large reduction in long-distance, nighttime, and non-essential travel. This results in a more geographically fragmented, local, and regional network in the pandemic. We demonstrate that these structural changes slow down the spatial spread of the COVID-19 in the mobility network.

Map-Driven Mmwave Link Quality Prediction With Spatial-Temporal Mobility Awareness

Map-Driven Mmwave Link Quality Prediction With Spatial-Temporal Mobility Awareness

Contrastive Pre-training with Adversarial Perturbations for Check-In Sequence Representation Learning

A core step of mining human mobility data is to learn accurate representations for user-generated check-in sequences. The learned representations should be able to fully describe the spatial-temporal mobility patterns of users and the high-level semantics of traveling. However, existing check-in sequence representation learning is usually implicitly achieved by end-to-end models designed for specific downstream tasks, resulting in unsatisfactory generalizable abilities and poor performance. Besides, although the sequence representation learning models that follow the contrastive learning pre-training paradigm have achieved breakthroughs in many fields like NLP, they fail to simultaneously consider the unique spatial-temporal characteristics of check-in sequences and need manual adjustments on the data augmentation strategies. So, directly applying them to check-in sequences cannot yield a meaningful pretext task. To this end, in this paper we propose a contrastive pre-training model with adversarial perturbations for check-in sequence representation learning (CACSR). Firstly, we design a novel spatial-temporal augmentation block for disturbing the spatial-temporal features of check-in sequences in the latent space to relieve the stress of designing manual data augmentation strategies. Secondly, to construct an effective contrastive pretext task, we generate “hard” positive and negative pairs for the check-in sequence by adversarial training. These two designs encourage the model to capture the high-level spatial-temporal patterns and semantics of check-in sequences while ignoring the noisy and unimportant details. We demonstrate the effectiveness and versatility of CACSR on two kinds of downstream tasks using three real-world datasets. The results show that our model outperforms both the state-of-the-art pre-training methods and the end-to-end models.

Digital borders in spatial-temporal mobility: Social inclusion and exclusion of Chinese migrant students in Macao

While mobile social media has increasingly become embedded in migrants’ daily lives, how mobile social media affects migrants’ cross-border experiences remains under-researched. Based on a 2-year ethnographic study, this article demonstrates a more complex relationship among mobile social media, migrants’ spatial-temporal mobility, and their subjective experience of social inclusion and exclusion. Situated in a Chinese cross-border context (Macao Special Administrative Region), this article elaborates on how mobile social media leads to heterogeneous and synchronous spatial-temporal mobility in a homogeneous time-narrative. This article further explains how and why mainland students’ social exclusion transforms into digital inclusion, where the blurring boundaries create the possibility of digital and social inclusion but also risk deeper exclusion and internal borders. The article argues for a new epistemology of the border, which is complicated, heterogeneous, and paradoxical, while mobile social media reinvigorates the border concept in how it constructs and deconstructs territorial/internal boundaries and inclusion/exclusion dynamics.

A Methodology to Estimate Functional Vulnerability Using Floating Car Data

In this work, a new methodology to estimate the functional vulnerability of the road network of the city of Catania (Italy) is developed with the purpose to improve the resilience of urban transport during critical events. While the traditional approach for the estimation of vulnerability is based on topological data, the proposed methodology is based on spatial-temporal mobility profiles obtained with floating car data (FCD). The algorithm developed for the estimation of vulnerability combines topological properties of the road network with mobility patterns obtained from FCD to evaluate the consequences of failure events on trajectories and their associated travel times. The core operation of the algorithm is based on the computation of all possible travel paths within their assigned geographical zone every time a road link is disrupted. The procedure may prove useful to evaluate wide failure events and to facilitate emergency plans.

CRSM: Computation Reloading Driven by Spatial-Temporal Mobility in Edge-Assisted Automated Industrial Cyber-Physical Systems

Edge Computing, as an emerging computing mode, transfers computing capacity from the cloud center to the edge of new generation automation sensor networks. However, the surge in the number of sensor devices has resulted in the edge server overload. Fortunately, with the development of hardware technology, the computing capacity of sensor devices has improved. Therefore, we introduce a new concept of computation reloading, that is, resource-rich servers allocate tasks to sensor devices with stronger computing capacity. Most previous real-time EC researches have not considered sensor devices’ higher spatial–temporal mobility, which results in continuous computation migration and high delay. In this article, we expose a computation reloading scheme driven by spatial–temporal mobility (CRSM), for single time slice and multiple time slices cases, computation tasks are allocated in advance by predicting sensor devices locations. The experiments demonstrate that our scheme outperforms other methods in terms of shorting industrial cyber-physical systems delays.

A long-term perspective on the COVID-19: The bike sharing system resilience under the epidemic environment

IntroductionThe sudden COVID-19 pandemic poses a fresh and tough challenge to bike sharing systems (BSS). With this epidemic as a shock event, this paper aspires to shed light on the phenomenon of changing demand and usage regularity in New York City's BSS under the epidemic environment, spanning a period of 18 months. MethodsTechnically, BSS's normal performance and the timely responses to the outbreak could be conceptualized as having four different stages. One provides a comparative analysis of bike sharing spatial-temporal mobility patterns and connectivity of the bike sharing usage network, before and during the public health crisis with a macroscopic perspective. Also, a multivariate investigation of user and trip characteristics on BSS is conducted to uncover the difference in the frequency of outdoor and sojourn time between various user communities. ResultsDue to the impact of the outbreak, BSS registered severe ridership drops, yet it quickly recovered to the pre-pandemic levels within months. The decline of bike sharing usage was felt throughout all the areas during the outbreak. However, there were places where BSS ridership actually increased, particularly in the areas near supermarkets, parks and hospitals. The less densely connected network of the bike sharing usage has also resulted in a reduction in users’ destination heterogeneity. This study also finds evidence of the significant gender, age and cycling pattern gaps in response to potential risk. ConclusionsInvestigating the dynamics of bike sharing usage will help to comprehend how the serious pandemic caused by COVID-19 impacts people's daily mobility. Practically, this work hopes to provide insights into adapting this unprecedented pandemic so as to respond to similar events in the future.

Understanding the determinants of spatial-temporal mobility patterns based on multi-source heterogeneous data

With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DAS), it is possible to explore the determinants of urban spatial-temporal mobility patterns using multi-source heterogeneous data. This study aims to use the points-of-interests (POIs) data, house-price data, and floating car data to identify the factors influencing urban mobility in Shanghai. Within a scale of 0.5 km grid, trip production and attraction were stratified according to the traveling intensity, and the critical information related to economy, intermodal connection, land use, and time were also obtained through the multi-source data. The experiment results from an ordinal logistic regression (OLR) analysis show that average house price has a dominating and positive effect on the traveling intensity for both trip production and attraction, followed by land-use factors. However, the effect of scenic spots is found significant only on trip attraction. In addition, shopping is found to insignificantly affect the traveling intensity for both trip production and attraction. Unexpectedly, time factors also have diverse impacts. These findings are expected to help better understand the relationship between urban mobility and built environment factors, providing passengers with better services, and offering useful insights into future urban and transportation planning.

Inferring gender and age of customers in shopping malls via indoor positioning data

Customer profiles that include gender and age information are important to businesses and can be used to promote sales and provide personalized services. This information is gathered in e-commerce by analyzing customer visit records in virtual web space. However, such practice is difficult in brick-and-mortar businesses because the data that can be utilized to infer customer profiles are limited in physical spaces. In this paper, we attempt to infer the gender and age of customers using indoor positioning data generated by the Wi-Fi engine. To achieve this, we first construct a synthesized features vector to distinguish different profiles. This vector contains both customer spatial–temporal mobility characteristics and interest preferences. A hidden Markov model group detection method is then applied to detect customers who shop together because they usually show the same shopping behavior and it is difficult to distinguish their profiles. Finally, a random forest inference model is proposed to infer profiles of customers who shop alone. The indoor positioning data collected in the Longhu Tianjie Plaza in Chongqing were used as a case study. The result shows that customer profiles are indeed inferable from indoor positioning data. The accuracy of the gender inference model reaches 73.9%, while that of the age inference model is 67.9%. This demonstrates the potential value of new “big data” for promoting precision marketing and customer management in brick-and-mortar businesses.

Learning Urban Community Structures

Learning urban community structures refers to the efforts of quantifying, summarizing, and representing an urban community’s (i) static structures, e.g., Point-Of-Interests (POIs) buildings and corresponding geographic allocations, and (ii) dynamic structures, e.g., human mobility patterns among POIs. By learning the community structures, we can better quantitatively represent urban communities and understand their evolutions in the development of cities. This can help us boost commercial activities, enhance public security, foster social interactions, and, ultimately, yield livable, sustainable, and viable environments. However, due to the complex nature of urban systems, it is traditionally challenging to learn the structures of urban communities. To address this problem, in this article, we propose a collective embedding framework to learn the community structure from multiple periodic spatial-temporal graphs of human mobility. Specifically, we first exploit a probabilistic propagation-based approach to create a set of mobility graphs from periodic human mobility records. In these mobility graphs, the static POIs are regarded as vertexes, the dynamic mobility connectivities between POI pairs are regarded as edges, and the edge weights periodically evolve over time. A collective deep auto-encoder method is then developed to collaboratively learn the embeddings of POIs from multiple spatial-temporal mobility graphs. In addition, we develop a Unsupervised Graph based Weighted Aggregation method to align and aggregate the POI embeddings into the representation of the community structures. We apply the proposed embedding framework to two applications (i.e., spotting vibrant communities and predicting housing price return rates) to evaluate the performance of our proposed method. Extensive experimental results on real-world urban communities and human mobility data demonstrate the effectiveness of the proposed collective embedding framework.

Complexity and Uncertainty in Geography of Health Research: Incorporating Life-Course Perspectives

Geographers, including those interested in the relationships between health and place, have made important contributions to how, when, and where humans are exposed to and influenced by different spatial contexts. Using detailed and sometimes real-time spatial and temporal data, geographers have enhanced our understanding of how people move within and between different social and physical environments and the implications for health outcomes and behaviors. Yet almost all of this work focuses on spatial–temporal mobility over short time periods (e.g., day, week), and there has been little effort to understand the extent to which people are exposed to different types of places and environments over their full life span. This article examines the analytic possibilities of, and technical challenges to, incorporating this uncertainty into a life-course framework to better understand (1) the accumulation of environmental circumstances over life and (2) whether there are critical periods during life when aspects of place are particularly pertinent in understanding health. It is argued that this approach not only offers opportunities to better understand the complex relationships between health and place (and other social outcomes) but can strengthen the evidence for causal relationships between the environment and health. Finally, there is a brief discussion of some of our own nascent work considering these issues using longitudinal data collected in the United Kingdom.

Understanding urban mobility patterns with a probabilistic tensor factorization framework

The rapid developments of ubiquitous mobile computing provide planners and researchers with new opportunities to understand and build smart cities by mining the massive spatial-temporal mobility data. However, given the increasing complexity and volume of the emerging mobility datasets, it also becomes challenging to build novel analytical framework that is capable of understanding the structural properties and critical features. In this paper, we introduce an analytical framework to deal with high-dimensional human mobility data. To this end, we formulate mobility data in a probabilistic setting and consider each record a multivariate observation sampled from an underlying distribution. In order to characterize this distribution, we use a multi-way probabilistic factorization model based on the concept of tensor decomposition and probabilistic latent semantic analysis (PLSA). The model provides us with a flexible approach to understand multi-way mobility involving higher-order interactions—which are difficult to characterize with conventional approaches—using simple latent structures. The model can be efficiently estimated using the expectation maximization (EM) algorithm. As a numerical example, this model is applied on a four-way dataset recording 14 million public transport journeys extracted from smart card transactions in Singapore. This framework can shed light on the modeling of urban structure by understanding mobility flows in both spatial and temporal dimensions.

Optimal Path Planning With Spatial-Temporal Mobility Modeling for Individual-Based Emergency Guiding

This paper proposes an individual-based framework for emergency guiding. The spatial-temporal mobility of all people is modeled to determine a dedicated path that provides the shortest evacuation time for each person. According to our review of relevant research, this is the first optimal solution without using time-expanded graphs, and corridor capacities and lengths, exit capacities, concurrent motion, and distribution of people are considered to minimize evacuation time. We prove that the proposed path planning algorithm is optimal and analyze its time and space complexity. The proposed framework can be used to estimate the evacuation time for each person accurately and evenly distribute evacuation load among exits to achieve the most efficient load balance. In the proposed framework, the congestion in all corridors and exits can be alleviated to maximally reduce the total evacuation time. Simulation results show that our approach outperforms existing schemes, and can be used to determine an optimal escape path for each person and, thus, achieve the shortest total evacuation time.

Spatial and Temporal Characteristics of Pastoral Mobility in the Far North Region, Cameroon: Data Analysis and Modeling.

Modeling the movements of humans and animals is critical to understanding the transmission of infectious diseases in complex social and ecological systems. In this paper, we focus on the movements of pastoralists in the Far North Region of Cameroon, who follow an annual transhumance by moving between rainy and dry season pastures. Describing, summarizing, and modeling the transhumance movements in the region are important steps for understanding the role these movements may play in the transmission of infectious diseases affecting humans and animals. We collected data on this transhumance system for four years using a combination of surveys and GPS mapping. An analysis on the spatial and temporal characteristics of pastoral mobility suggests four transhumance modes, each with its own properties. Modes M1 and M2 represent the type of transhumance movements where pastoralists settle in a campsite for a relatively long period of time (≥20 days) and then move around the area without specific directions within a seasonal grazing area. Modes M3 and M4 on the other hand are the situations when pastoralists stay in a campsite for a relatively short period of time (<20 days) when moving between seasonal grazing areas. These four modes are used to develop a spatial-temporal mobility (STM) model that can be used to estimate the probability of a mobile pastoralist residing at a location at any time. We compare the STM model with two reference models and the experiments suggest that the STM model can effectively capture and predict the space-time dynamics of pastoral mobility in our study area.

John Gabriel Borkman’s Avant-Garde Continuity

Henrik Ibsen, filmmaker. A pause is necessary after that particular juxtaposition, if only to sort out the immediate clash of impressions: Ibsen, the culminating nineteenth-century dramatist, on the one hand; film, the nascent twentieth-century popular visual medium, on the other. Ibsen’s plays, among the most renowned dramas of entrapment, interiority, and inertia, do not resonate easily with the spatial–temporal mobility of the cinema. The fact that Ibsen was no Eisenstein evokes little surprise; other writers of the late nineteenth and early twentieth centuries had a much more visible interest in the new medium (Maxim Gorky and Franz Kafka come to mind).1 Ibsen, sixty-eight years old at the time of the cinematograph’s debut in Paris, lived out most of his creative life before regular public cinematic projection became a truly viable possibility in the early twentieth century and before film could possibly have a significant cultural impact. Given that Ibsen was incapacitated by the series of strokes in 1900–03 that put an end to his writing and led to his death in 1906, he simply did not have the historical opportunity to explore the formal possibilities of the new medium directly. Case closed?