Understanding Of Mobility Patterns Research Articles

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.

Roles of accessibility and air-rail intermodality in shaping mobility patterns in mega-city regions: Behavioural insights from China

This paper aims to understand mobility patterns in mega-city regions from perspectives of accessibility and air-rail intermodality. Travellers' preferences for intercity mode and its access and egress modes were simultaneously collected in the context of the Beijing-Tianjin-Hebei mega-city region. The accessibility of transport hubs and destinations was measured to enable interpreting mobility behaviour in three dimensions, i.e., travel mode decisions, departure transport hub decisions, and transport service decisions. The calibrated behavioural model highlights a series of determinants regarding level-of-service attributes, travel characteristics, accessibility, socio-demographics, and their interaction. Based on the behavioural insights, twenty domestic cities were selected as destinations for analysing the dynamics of mobility patterns in terms of the competition within the multi-airport system and air-rail cooperation potential in the long-haul market. The elasticity of shifts in transport demand between the primary and secondary airports in the Beijing multi-airport system was assessed under various pricing strategies, highlighting the vital role of the most accessible airport in air traveller decision-making. Moreover, the results underline the effect of air-rail intermodal service upgrades, e.g., offering baggage handling systems and flexible tickets, on stimulating air-rail intermodal mobility in the mega-city region.

Social networks, mobility, and HIV risk among women in the fishing communities of Lake Victoria

BackgroundPopulation mobility is a demonstrated barrier to reducing HIV incidence. A clear understanding of social networks and their influence on mobility among women in the fishing communities of Lake Victoria may contribute to tailoring effective interventions that suit the needs of these mobile women.MethodsA cross-sectional qualitative methods study was conducted to understand mobility patterns among women resident and or working in fishing communities of Lake Victoria in Kenya, Tanzania, and Uganda. The study was conducted in six fishing communities from March 2018 to June 2019. The communities were purposively selected, based on population size (1000 people or more) and HIV prevalence of > 15% among women aged 18 years or older who had lived in the fishing community for at least six months. In-depth interviews were conducted with 24 key informants and 72 women from the sites in the three countries. Questions focused on women’s social networks and other factors that fuelled or facilitated women’s mobility as well as challenges they faced due to mobility. Data analysis followed a thematic framework approach.ResultsDifferent social groupings/networks existed among women in the fishing communities of Lake Victoria. These included female sex workers, women fish processors/traders, women bar workers/owners, restaurant workers, and family networks. Networks encouraged mobility, supporting finding work opportunities, but also increased sexual risks through partner changes. The benefits of networks included information sharing, financial support, and group protection, especially against violence.ConclusionSocial networks and groupings among women in the fishing communities of Lake Victoria could be useful in tailoring HIV prevention and HIV care interventions to suit the needs of these highly mobile populations.

A hierarchical graph-based model for mobility data representation and analysis

Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. This paper introduces a hierarchical graph-based model for representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. This formal model is implemented in a graph database and experimented with historical maritime data. Several experimental analyses explore and extract knowledge patterns from the hierarchical graph database. A series of queries applied to an European maritime network derive mobility patterns and highlight network structures.

Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities.

Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.

On the relation between COVID-19, mobility, and the stock market.

The Covid-19 pandemic has brought forth a major landscape shock in the mobility sector. Due to its recentness, researchers have just started studying and understanding the implications of this crisis on mobility. We contribute by combining mobility data from various sources to bring a novel angle to understanding mobility patterns during Covid-19. The goal is to expose relations between mobility and Covid-19 variables and understand them by using our data. This is crucial information for governments to understand and address the underlying root causes of the impact.

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning.

While non-essential travel was canceled during the coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential. The objectives of this study were to: 1) examine how grocery store visits changed during the early outbreak of COVID-19, and 2) estimate a model to predict the change of grocery store visits in the future, within the same phase of the pandemic. The study period (February 15-May 31, 2020) covered the outbreak and phase-one re-opening. Six counties/states in the United States were examined. Grocery store visits (in-store or curbside pickup) increased over 20% when the national emergency was declared on March 13 and then decreased below the baseline within a week. Grocery store visits on weekends were affected more significantly than those on workdays before late April. Grocery store visits in some states (including California, Louisiana, New York, and Texas) started returning to normal by the end of May, but that was not the case for some of the counties (including those with the cities of Los Angeles and New Orleans). With data from Google Mobility Reports, this study used a long short-term memory network to predict the change of grocery store visits from the baseline in the future. The networks trained with the national data or the county data performed well in predicting the general trend of each county. The results from this study could help understand mobility patterns of grocery store visits during the pandemic and predict the process of returning to normal.

Towards the Future of Sustainable Mobility: Results from a European Survey on (Electric) Powered-Two Wheelers

Personal transport is of high importance in our society and the 2020 pandemic situation has reinforced this situation. At the same time, transport contributes to local emissions, which need to be reduced in the face of climate change. Changing from vehicles with internal combustion engines to light electric vehicles could be one promising approach. Therefore, we need to understand mobility patterns and attitudes towards E-mobility to create sustainable transport solutions that will be broadly accepted. An online survey with N = 432 participants across Europe was conducted. The majority of respondents came from Germany, followed by Italy, Austria and Sweden. Generally, cars are the main vehicle for personal transport. PTWs are used for commuting as well as leisure activity. Driving experience, easier parking and lower maintenance compared to cars are major reasons to choose a PTW. No differences between younger and elderly participants were observed. E-PTWs are primarily avoided due to high costs, range anxiety and expected problems with the charging infrastructure. To support sustainable mobility, these obstacles need to be overcome. One aspect is definitely the provision of better charging infrastructure or electric vehicles with increased range. Hence, given typical trip lengths and purposes, it might seem equally important to tackle prejudices and increase the knowledge about E-mobility with all its potential benefits in the population.

SPATIOTEMPORAL DYNAMICS OF PUBLIC TRANSPORT DEMAND: A CASE STUDY OF RIGA

Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.

Understanding mobility patterns and user activities from geo-tagged social networks data

Social networks are strongly present in the daily life of modern society. Most people use these social networks to share information about their lives, their opinions, places they visit and their state of mind. Generally, these posts are composed of various information, being the location of the users location part of the data. The purpose of this work is to obtain the location of the posts and observe the users mobility pattern in the city of Porto, Portugal. This paper discusses the technologies available for obtaining the data, the social networks currently worth studying and their respective restrictions. It also explores new approaches to collect the data from the desired social networks, respecting all restrictions currently applied. The different software solutions developed for the social networks interactions are explored and described in depth. Subsequently, the necessary software for social networks is reviewed, the possible algorithms for data mining are discussed and its implementation is presented. Finally, the results obtained are interpreted and studied according to the characteristics of the city, tourism promotions and transport routes.

The use of lithic raw materials at the Early Mesolithic open-air site Feuersteinacker (Vogelsbergkreis, Germany).

The open‐air site Feuersteinacker near Stumpertenrod has yielded one of the largest lithic assemblages in Central Germany. It repeatedly served as a workshop for the production of stone tools during an early phase of the Mesolithic. The range of lithic raw materials is extremely diverse, but until today, there is only a limited number of archaeological studies on the occurrence of lithic resources in the area. The following study presents the first in‐depth investigation of the use of different rock types by Mesolithic hunter–fisher–gatherers at the site. Provenance analyses using petrographic methods permit raw materials to be assigned to a specific source and provide new insights into their formation. Furthermore, this study explores the way in which the materials were processed throughout the reduction sequence. A comparison of topographic parameters suggests that the location was situated on an important transit route during prehistoric times. The presented results contribute to a better understanding of mobility patterns and subsistence strategies of Early Mesolithic groups in Central Germany.

Road Network Design in a Developing Country Using Mobile Phone Data: An Application to Senegal

This study uses mobile phone data to understand mobility patterns in a country, with limited mobility data, in order to give advice about decisions on how to design the national and regional road network. Our method consists of three parts: (1) filtering mobile phone traces to derive mobility patterns, (2) building an adapted formulation of the gravity-based trip distribution model, which considers telecommunication intensity (i.e., aggregate number of calls and text messages) and travel time as input to forecast the influence of road improvements on country-wide mobility, and (3) optimizing the road network investment based on the adapted trip distribution model by using a local search algorithm. The method was applied to the case study country of Senegal. The mobile phone data was transformed to support informed decisions on road network development in that country given different objectives, namely accessibility and equity. We believe that the methodology is valuable and reproducible to other countries where traditional mobility data is scarce but mobile phone data is available to transport planners.

Wandering through the Mesolithic. An archaeostatistical approach to explore the mobility patterns in eastern Iberia

Tracing hunter-gatherer's mobility has been a recurring topic both in anthropological and archaeological literature. Following Binford's approach (1980), ethnographic comparisons have been brought out in order to better understand mobility patterns among Palaeolithic and Mesolithic groups, and how they relate with their environment, thus formulating a system where a main difference in mobility structure is pronounced on the distinction between residential and logistical camps.After some efforts made in order to relate the lithic record with such model (Clark and Barton, 2017), in this work we explore how lithic industry can be a reliable proxy for understanding the mobility patterns of the last hunter-gatherers of the Eastern Iberian Peninsula by studying a number of Late Mesolithic lithic collections. We try to bring a new insight into Clark and Barton's analysis, both by combining different sites -implementing geographical variability- and by taking into account functionality and its possible statistical traces, as shown by blades, bladelets and geometric microliths. We focus on the differences found at each site and how they relate with lithic industry in order to test hypotheses regarding mobility patterns.

Gravity model explained by the radiation model on a population landscape.

Understanding the mechanisms behind human mobility patterns is crucial to improve our ability to optimize and predict traffic flows. Two representative mobility models, i.e., radiation and gravity models, have been extensively compared to each other against various empirical data sets, while their fundamental relation is far from being fully understood. In order to study such a relation, we first model the heterogeneous population landscape by generating a fractal geometry of sites and then by assigning to each site a population independently drawn from a power-law distribution. Then the radiation model on this population landscape, which we call the radiation-on-landscape (RoL) model, is compared to the gravity model to derive the distance exponent in the gravity model in terms of the properties of the population landscape, which is confirmed by the numerical simulations. Consequently, we provide a possible explanation for the origin of the distance exponent in terms of the properties of the heterogeneous population landscape, enabling us to better understand mobility patterns constrained by the travel distance.

Interdisciplinary examinations carried out on heterogeneous coarse ceramics from Neolithic lakeside settlements in the Northern Alpine Foreland (3900–3500 BCE): Analysis strategy and preliminary results from a test series using pXRF

In recent decades numerous Neolithic lakeside and wetland settlements have been investigated in Switzerland, southern Germany and eastern France. The finds from these settlements indicate that complex relationships and close contacts existed between the inhabitants of different settlements and regions. This can be seen in ceramic vessels that exhibit similarities to those from other regions based on their style, manufacturing technique and raw materials. Although the use of chemical and archaeometric analyses to determine the origin of pottery has been well established for years and despite promising investigations, such analyses have hardly ever been carried out in the study region; as a result, the topic of mobility has rarely been examined. In order to better understand mobility patterns in Neolithic societies by combining stylistic analyses with a chemical characterisation of the potter's clay used, this paper explores the use of portable energy dispersive X-ray fluorescence (pXRF) analysis as a means of chemically characterising ceramics. Since studies on the use of pXRF to examine handmade, heterogeneous and coarsely tempered ceramics are rare, it seems necessary to evaluate the method. This paper presents the development of a strategy for the analysis of large series of handmade coarse ceramics from prehistoric settlements. Two examples illustrate the possibilities of and limitations to pXRF analysis on pottery for the detection of mobility patterns of Neolithic societies in the Northern Alpine Foreland.

A Semantic Model for Human Mobility in an Urban Region

The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Very large trajectory datasets are often publicly generated thanks to the availability of multiple positioning sensors and location-based services. However, a successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; secondly, significant patterns can be derived at the database manipulation level using specifically developed query functions.

Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities

City governments all over the world face challenges understanding mobility patterns within dense urban environments at high spatial and temporal resolution. While such measures are important to provide insights into the functional patterns of a city, novel quantitative methods, derived from ubiquitous mobile connectivity, are needed to provide policy-makers with better insights to improve urban management and planning decisions. In this paper, we develop a model that uses large-scale WiFi probe request data to model urban mobility trajectories in dense urban environments. We collect probe request data from a public Wifi network with 54 access points in the Lower Manhattan section of New York City over one week, accounting for more than 30 million observations and over 800,000 unique devices. First, we aggregate unique entries per access point and per hour, demonstrating the potential to use WiFi data to approximate local population counts by type of user. We then use a spatial network analysis to identify edge frequencies and directions of journeys between the network nodes, and apply the results to the road and pedestrian sidewalk network to identify usage intensity levels and trajectories for individual street segments. We demonstrate the significant potential in the use of WiFi probe request data for understanding mobility patterns in cities, while highlighting non-trivial issues in data privacy raised by the growing availability of public WiFi networks.

The identification of pathways on harra surfaces in north-eastern Jordan and their relation to ancient human mobility

This paper calls attention to the presence of countless ancient paths on the basalt-covered surfaces characteristic of harra landscapes in north-eastern Jordan. These paths have developed over the course of at least the last two millennia, and potentially prior to that, by trafficking of nomadic peoples and animals. These paths facilitated movements through terrains that were otherwise difficult to traverse. Paths can be recognised on high-resolution satellite imagery, which allows for systematic documentation and the reconstruction of potential routes through the landscape. The identification and mapping of these paths is important for better understanding mobility patterns of nomadic peoples who inhabited these desert landscapes in antiquity.

Strontium isotopes are consistent with low‐elevation foraging limits for Henst's goshawk

ABSTRACTApex predators play critical roles in the ecosystems they inhabit. Unfortunately, little is known about movement patterns for many species. This information is critical for evaluating vulnerability to habitat loss and the adequacy of existing or proposed protected areas. Strontium isotope ratios (87Sr/86Sr) in prey remains reflect the geology where individuals lived and were killed, and can be used to identify foraging ranges for predators. We tested the degree to which 87Sr/86Sr in consumed lemurs (Lemuroidea) can be used to track foraging ranges for Henst's goshawk (Accipiter henstii) at Ranomafana National Park (RNP), eastern Madagascar, Africa. This large‐bodied endemic accipiter is sparsely distributed and rarely observed outside of forest or at elevations &gt;1,200 m. A geologic boundary divides lower and higher elevations at RNP: Precambrian migmatite is at lower elevations within and to the east of RNP and Precambrian metasediments are limited to higher elevations in southwestern RNP. We collected foliage from trees and understory plants to establish 87Sr/86Sr for both geologies, and remains from 19 depredated lemurs from 4 hawk nests located along the eastern edge of RNP. Leaves from metasediments have greater 87Sr/86Sr than leaves from migmatite. 87Sr/86Sr for lemur bones suggests that 18 of the 19 predated individuals came from forests underlain by migmatite. Thus, strontium isotope data suggest that Henst's goshawk primarily hunted on migmatite at RNP, which supports a growing body of evidence that this species forages at elevations below approximately 1,200 m. Most reserves in eastern Madagascar protect forest at higher elevations. Conserving remaining forest and revegetating denuded land at lower elevations will likely be critical for survival of this species. Understanding mobility patterns is crucial for managing predator populations. Isotopic analysis of prey is a noninvasive method for monitoring foraging ranges that complements existing approaches. This geochemical tool is readily adaptable to other systems and species. © 2017 The Wildlife Society.