Daily Mobility Patterns Research Articles

Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis

Big Data sources offer new possibilities for urban mobility and accessibility studies. As people carry out their activities in a city, they leave behind a digital fingerprint that can be used to analyze the population’s daily mobility patterns and determine the exact times of travel between points of origin and destination at different times of the day. These data present high spatial and temporal resolution, and enable accurate and dynamic analysis of accessibility. The objective of this study was to conduct a dynamic analysis of urban accessibility considering its two main components: travel times and the attractiveness of destinations. To this end, we calculated travel times between transport zones using the Google Maps API and constructed origin and destination (OD) travel matrices from mobile phone records. Several scenarios were generated to analyze dynamic accessibility and the separate influence of its two components. We also conducted a cluster analysis to characterize transport zones according to their accessibility in each of the scenarios and times of day considered. Our results indicate that these new sources of geolocated data show considerable potential for use in time-sensitive accessibility studies, since they yield more accurate and realistic information than static or partially dynamic analyses. Such information could help politicians take better decisions concerning transport and land use.

Urban Mobility Analysis With Mobile Network Data: A Visual Analytics Approach

Urban planning and intelligent transportation management are facing key challenges in today’s ever more urbanized world. Providing the right tools to city planners is crucial to cope with these challenges. Data collected from citizens’ mobile communication can be used as the foundation for such tools. These kinds of data can facilitate various analysis tasks, such as the extraction of human movement patterns or determining the urban dynamics of a city. City planners can closely monitor such patterns based on which strategic decisions can be taken to improve a city’s infrastructure. In this paper, we introduce a novel visual analytics approach for pattern exploration and search in global system for mobile communications mobile networks. We define geospatial and matrix representations of data, which can be interactively navigated. The approach integrates data visualization with suitable data analysis algorithms, allowing to spatially and temporally compare mobile usage, identify regularities, as well as anomalies in daily mobility patterns across regions and user groups. As an extension to our visual analytics approach, we further introduce space-time prisms with uncertain markers to visually analyze the uncertainty of urban mobility patterns.

Understanding the effects of transit benefits on employees’ travel behavior: Evidence from the New York-New Jersey region

Implementing effective travel demand management measures provides an opportunity to reduce transport dependence on the private car. There is growing acknowledgement that the strategy of implementing transit benefits may boost transit ridership and reduce personal vehicle use. This research contributes to the understanding of this issue by examining the relationship between commuter benefits and mode choice for commuting trips in the states of New York and New Jersey (US). Based on individual data from the Regional Household Travel Survey conducted by the New York Metropolitan Transportation Council and North Jersey Transportation Planning Authority, we adopted a multinomial logit model to identify the extent to which transport benefits to employees – including public transport-related, private transport-related and benefits for walking and cycling – promote changes in commuters’ modal split. The analysis shows that commuter benefits play a significant role in explaining observed travel patterns. Benefit programs that pay for auto expenses (e.g. toll payments, mileage reimbursement, free parking) are negatively correlated with transit, biking, and walking, while employer-funded benefit programs for transit passes and bike reimbursements increase their respective mode shares. This result confirms that promoting these types of measures is an effective policy to encourage the use of public transport modes, thus increasing efficiency and sustainability in daily mobility patterns.

Daily Mobility in the Black-White Segregated City: Linking Material Realities and Repertoires of Meaning

As the overt institutional maintenance of black-white residential segregation declines, we can investigate its persistence by analyzing the racial projects of everyday life. Daily mobility—movement from place to place for the sake of routine activities—represents one such project. Daily mobility patterns reflect the material realities of uneven resource distribution across neighborhoods and unequal means of access across residents. Yet, daily mobility patterns additionally reflect residents’ subjective interpretations and agentic navigations of space. Various repertoires of meaning that evoke racial dynamics can maintain or challenge the ideational foundations of the racial order. In turn, they can deter or encourage the crossing of segregation boundaries. The daily mobility framework acknowledges that experiences of segregation can vary considerably, as they are shaped by both spatial-opportunity distance and social-psychological distance. As a means of illustration, this project presents divergent daily mobility accounts drawn from interviews with 12 residents of Milwaukee, Wisconsin.

SimMobility Short-Term: An Integrated Microscopic Mobility Simulator

This paper presents the development of an integrated microscopic mobility simulator, SimMobility Short-Term (ST). The simulator is integrated because its models, inputs and outputs, simulated components, and code base are integrated within a multiscale agent- and activity-based simulation platform capable of simulating different spatiotemporal resolutions and accounting for different levels of travelers’ decision making. The simulator is microscopic because both the demand (agents and its trips) and the supply (trip realization and movements on the network) are microscopic (i.e., modeled individually). Finally, the simulator has mobility because it copes with the multimodal nature of urban networks and the need for the flexible simulation of innovative transportation services, such as on-demand and smart mobility solutions. This paper follows previous publications that describe SimMobility’s overall framework and models. SimMobility is an open-source, multiscale platform that considers land use, transportation, and mobility-sensitive behavioral models. SimMobility ST aims at simulating the high-resolution movement of agents (traffic, transit, pedestrians, and goods) and the operation of different mobility services and control and information systems. This paper presents the SimMobility ST modeling framework and system architecture and reports on its successful calibration for Singapore and its use in several scenarios of innovative mobility applications. The paper also shows how detailed performance measures from SimMobility ST can be integrated with a daily activity and mobility patterns simulator. Such integration is crucial to model accurately the effect of different technologies and service operations at the urban level, as the identity and preferences of simulated agents are maintained across temporal decision scales, ensuring the consistency and accuracy of simulated accessibility and performance measures of each scenario.

Assessment of Smartphone Positioning Data Quality in the Scope of Citizen Science Contributions

Human travel behaviour has been addressed in many transport studies, where travel survey methods have been widely used to collect self-reported insights of daily mobility patterns. However, since the introduction of Global Navigation Satellite Systems (GNSS) and more recently smartphones with built-in GNSS, researchers have adopted these ubiquitous devices as tools for collecting mobility behaviour data. Although most studies recognize the applicability of this technology, it still has limitations. These are rarely addressed in a quantified manner. Often the quality of the collected data tends to be overestimated and these errors propagate into the aggregated results providing incomplete knowledge of the levels of confidence of the results and conclusions. In this study, we focus on the completeness aspects of data quality using GNSS data from four campaigns in the Flanders region of Belgium. The empirical results are based on mobility behaviour data collected through smartphones and include more than 450 participants over a period of twenty-nine months. Our findings show which transport mode is affected the most and how land use affects the quality of the collected data. In addition, we provide insights into the time to first fix that can be used for a better estimation of travel patterns.

Contribution of geotagged Twitter data in the study of a social group’s activity space

Since 2009, the social media Twitter provides free access to localization data consisting of geotagged messages sent on the platform. We used this data to highlight their potential contributions to the study of social groups’ activity space. Using a sample of luxury shoppers, we analyzed the daily activity and mobility patterns of the upper middle class in Delhi, India. We intersected these data with the results of an ethnographic study carried out in two luxury shopping places in Delhi and found that the lower middle class was overrepresented in the Twitter sample. We argue that the varying motivation to post on social media tend to skew the representativeness of social groups sampled on a spatial criterion. We eventually advocate the complementary use of geotagged social media data and qualitative data based on ethnographic research.

Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands

Demographic ageing is a key societal challenge in Europe as well as in many other western and non-western societies. A crucial dimension concerns elderly daily mobility patterns. While still partaking fewer and shorter trips than younger generations, today’s elderly have been found increasingly (auto)mobile. Although the elderly benefit from the independence, freedom of movement, and social inclusion, concerns may rise regarding the environmental and accessibility impacts of this induced mobility. The present study adds to the expanding literature on elderly mobility, an integrated analysis of the effects of socio-demographic, health, trip, spatial and weather attributes on elderly mobility. Utilizing travel diary data for Greater Rotterdam, The Netherlands, trip frequencies and transport mode choices of the elderly are analysed by means of zero-inflated negative binomial models as well as multinomial logit regression models, and contrasted to the non-elderly subpopulation to explore (dis)similarities. While the results show common determinants, the models also highlight important differences in the magnitude of the estimated coefficients and factors only influencing transport patterns for the elderly. Embedded in the context of an aging population, the empirical findings assist policy-makers and planners in several respects: For transportation plans and programs it is critical to recognize mobility needs of the elderly. As the seniors are becoming increasingly automobile, the results call for strategies to encourage older people to use more physically active and environmentally friendly transport modes such as public transport, walking and cycling.

Geo-social media data analytic for user modeling and location-based services

More and more geo-tagged social media data is generated, nowadays, from the geo-tagged tweets, geo-tagged photos to check-ins. Analyzing this flourish data enables the possibility for us to discover users daily mobility patterns, profiles and preferences. As a result, based on the analyzed results, new types of location-based services emerge. In this article, we first introduce the recent advances in location-based user preferences modeling, which includes: 1) inferring users demographics, 2) identifying users novelty-seeking characteristics and 3) discovering users shopping impulsiveness. After that, we present a comprehensive summary on the state-of-arts of the location-based services, which take advantage of the geo-social media, including: 1) location-based recommendations, 2) location-based predication.

Mobility Research in the Age of the Smartphone

At the end of 2014, over 93 percent of the world's population owned cellular phones, with penetration rates that exceeded 100 percent in most developed countries. Mobile phones have become an integral part of our lives and have had a significant impact on society—including on individuals' daily movement and mobility patterns. Cellular phones have also been used for research and have been employed to collect time–space data about the mobility of relatively large populations. In this article, we provide a comprehensive review of the potential of advanced mobile phones—known as smartphones—in the investigation of the geographies of mobility. We discuss how these devices can be employed in research, tracking individuals in time and space and functioning as location-aware survey tools in real time, among other things. We also engage in a debate over the advantages, disadvantages, and limitations of smartphones in this context and highlight new research trends that are beginning to appear following the introduction of smartphones.

Heat Exposure Assessment Based on Individual’s Daily Mobility Data in Dhaka, Bangladesh

Heat Exposure Assessment Based on Individual’s Daily Mobility Data in Dhaka, BangladeshAbstract Number:2621 SHINYA YASUMOTO*, Chiho Watanabe, Andrew Jones, Kei Oyoshi, Toru Fukuda, Hiroshi Kanasugi, Yoshihide Sekimoto, and Ryosuke Shibazaki SHINYA YASUMOTO* Department of Human Ecology, Graduate School of Medicine, The University of Tokyo , Japan, E-mail Address: [email protected] Search for more papers by this author , Chiho Watanabe Department of Human Ecology, Graduate School of Medicine, The University of Tokyo, Japan, E-mail Address: [email protected] Search for more papers by this author , Andrew Jones Norwich Medical School, University of East Anglia Search for more papers by this author , Kei Oyoshi Japan Aerospace Exploration Agency (JAXA) Search for more papers by this author , Toru Fukuda Japan Aerospace Exploration Agency (JAXA) Search for more papers by this author , Hiroshi Kanasugi The Earth Observation Data Integration and Fusion Research Initiative, The University of Tokyo Search for more papers by this author , Yoshihide Sekimoto Institute of Industrial Science, The University of Tokyo Search for more papers by this author , and Ryosuke Shibazaki Center for Spatial Information Science, The University of Tokyo Search for more papers by this author AbstractDespite a growing body of evidence of the health effect of heat exposure, health impact assessments of population exposure to heat typically suffer from two limitations. Firstly, much evidence focuses on urban areas in developed countries, and hence little is known about the situation in developing nations. Secondly, and more importantly, in previous studies the typical assessments of human exposure to heat are based on only the residential locations of individuals. These assessments may lack precision because they do not reflect the impact of people’s daily mobility patterns on exposure. Using the case study of Dhaka, Bangladesh, this research conducted heat exposure assessment integrating people’s mobility pattern modelled through questionnaire survey and Geographic Information Systems. Magnitude of heat exposure was assessed using remotely sensed land surface temperature derived from satellites, and population-based exposures to heat using only residential location were compared to a dynamic exposure model that incorporated mobility to better explore public health implications of exposure. We found that, mainly for people in suburban areas, the traditional assessment method based on the static residential model underestimates the exposure level compared to the dynamic model. This is largely because many individuals living in the suburbs migrate daily into the city centre for work or study where they are more likely to experience higher land surface temperatures. We believe our findings have particular public health implications including, for example, potential need to redistribute health resources between urban and suburban areas. This research has also demonstrated the importance of integrating mobility patterns to reduce biases when comparing environmental exposure levels between urban, suburban, and rural populations.

Resettlement in Lao PDR: mobility, resistance and gendered impacts

Villagers in northern Laos have been on the move for generations. Recent changes, however, in the location of their village and their daily mobility patterns differ from what they have experienced before; the government's resettlement programme has changed their livelihoods and made them more socially and economically vulnerable. The ethnic groups we studied have decided to use mobility to resist state control and seek livelihood security for themselves. By using the concept of motility, this article analyses how this household and community choices have a gender-differentiated impact. The mobility patterns of men and women have changed. While men attend to long-term investments, women are forced to make ends meet on a day-to-day basis. Men visit the market and public places more frequently, while women spend more time looking for non-timber forest products and working as hired labour. Although women now support the family and their mobility has increased, their say in the household seems to be on the decline, resulting in weakening women's motility.

Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study)

Prior epidemiological studies have mainly focused on local residential neighborhoods to assess environmental exposures. However, individual spatial behavior may modify residential neighborhood influences, with weaker health effects expected for mobile populations. By examining individual patterns of daily mobility and associated socio-demographic profiles and transportation modes, this article seeks to develop innovative methods to account for daily mobility in health studies. We used data from the RECORD Cohort Study collected in 2011–2012 in the Paris metropolitan area, France. A sample of 2062 individuals was investigated. Participants' perceived residential neighborhood boundaries and regular activity locations were geocoded using the VERITAS application. Twenty-four indicators were created to qualify individual space-time patterns, using spatial analysis methods and a geographic information system. Three domains of indicators were considered: lifestyle indicators, indicators related to the geometry of the activity space, and indicators related to the importance of the residential neighborhood in the overall activity space. Principal component analysis was used to identify main dimensions of spatial behavior. Multilevel linear regression was used to determine which individual characteristics were associated with each spatial behavior dimension. The factor analysis generated five dimensions of spatial behavior: importance of the residential neighborhood in the activity space, volume of activities, and size, eccentricity, and specialization of the activity space. Age, socioeconomic status, and location of the household in the region were the main predictors of daily mobility patterns. Activity spaces of small sizes centered on the residential neighborhood and implying a large volume of activities were associated with walking and/or biking as a transportation mode. Examination of patterns of spatial behavior by individual socio-demographic characteristics and in relation to transportation modes is useful to identify populations with specific mobility/accessibility needs and has implications for investigating transportation-related physical activity and assessing environmental exposures and their effects on health.

Mobility in the transition to retirement – the intertwining of transportation and everyday projects

Through travel diaries and interviews with newly retired urban residents in Sweden our aim was to explore (1) mobility patterns in the transition to retirement, (2) the influence of space–time restrictions and resources on mobility and (3) the meaning and embodied experience of mobility. This time-geographic study contributes with knowledge on how mobility is influenced by individual, social and geographical contexts. Illustrated by four cases, our result show that retirement changes the preconditions for mobility and creates new space–time restrictions. To spend more time on projects that were previously carried out outside working time, such as caring for grandchildren, volunteer work and household responsibilities, influenced the informants’ demands for mobility and choice of transport mode. However, the informants have resources that can be seen as strategies to overcome space–time restrictions. Most of the informants found it important to structure the day, to some it was vital to have something to do during the day while others enjoyed the possibility to take each day as it comes. Everyday mobility was a way of forming a structure by getting out of the house, either just for a walk or for making errands. The informants’ embodied experiences of mobility influenced their choice to walk and cycle for transport for the reasons of comfort, get fresh air, or simply to get out of the house. The daily mobility pattern that was established was a result of individual preferences and resources as well as negotiations with family members. We conclude that the transition to retirement is a period when new mobility patterns are considered, evaluated and practiced.

Daily mobility patterns of an urban population and their relationship to overweight and obesity

BackgroundGeneral mobility is a daily behaviour that could result in a positive contribution to overall physical activity through walking for transportation or a negative contribution resulting in sedentarism caused by driving. The objective of this study is to examine the relationship between mobility and overweight/obesity in an urban context, while considering physical activity in leisure time, socioeconomic and lifestyle characteristics, and individual health status. MethodsCross-sectional study based on the 2011 Barcelona Health Survey. We selected individuals who reported no physical limitations (N=2312). The dependent variable was “Body Mass Index” calculated from self-reported weight and height measures (low/normal or overweight/obese). The main explanatory variable was “mobility” (Participant walked≥30min; walked<30min; travelled by public transport only; travelled in private motorised transport only). Prevalence ratios (PR) were calculated to study the relationship between mobility and overweight/obesity while also accounting for other variables. ResultsIn Barcelona, 47.8% of men and 28.1% of women aged 15–64 years were overweight or obese. Compared to individuals who used motorised transport only, lower risk of overweight/obesity was found in men travelling by public transport only [PR=0.75 (0.64–0.90)], walking<30min [PR=0.81 (0.70–0.93)] or ≥30min [PR=0.81 (0.73–0.90)]. We did not observe a significant association among women. ConclusionBehavioural changes toward more active mobility could reduce the likelihood of being overweight or obese, mainly in men. This reinforces the idea of active transport as a public health strategy.

Spatial Analysis of Transportation-related Social Exclusion in the Lisbon Metropolitan Area

Social exclusion (SE) is a multi-faceted issue that is often conflated with economic poverty, but the poor are not always excluded, and the rich can also be excluded. In addition to the more commonly acknowledged socio-demographic and socio- economic dimensions, SE has temporal and contextual components that are especially relevant to daily mobility patterns. The goal was to identify socially excluded individuals and any potential areas of the Lisbon metropolitan area where accessibility and mobility restrictions could potentially exacerbate the issue. A cluster analysis to identify population segments with common accessibility patterns was performed using information from the latest mobility survey. In addition to this, the complexity of average daily trip chains was characterized in order to better understand the relationship between the identified clusters and their social and economic activity (under the assumption that more complex trip chains are correlated with more intense social and economic activity). Finally, within-neighborhood variability of certain relevant mobility variables was explored by computing bivariate local indicators of spatial association (LISA) to identify areas with individuals at-risk for SE.

Unravelling daily human mobility motifs

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient.

Urban sprawl and social interaction potential: an empirical analysis of large metropolitan regions in the United States

This paper investigates the impact of urban spatial structure on the opportunities for people to participate in face-to-face activities. We make use of the Social Interaction Potential (SIP) metric, a tool to measure the average level of opportunity for people to engage in face-to-face activities given some basic constraints on their daily mobility patterns. Generally, this opportunity is a reflection of whether the urban spatial structure – a term that broadly applies to the spatial arrangement of land-uses and the interactions between them – constrains or permits the ability for potential activity partners to be at the same place and time. In this paper, the SIP metric is applied to 42 metropolitan regions in the United States with populations over 1,000,000 people. These measurements are regressed against a set of indicators of urban sprawl to expose the relationship between spatial structure and SIP. The indicators are generated by a factor analysis of a large set of variables describing the scale, centrality and dispersion of land-uses in addition to several other structural and infrastructure-related variables. Cluster analysis is also used to organize the regions into similar groupings with respect to their structural characteristics and the level of SIP they provide. The findings indicate that social interaction potential is hampered by decentralization, fragmentation, and longer commutes in the largest metropolitan regions in the country. Interestingly, the negative effect of decentralization on SIP Efficiency is found to be nearly ten times stronger than that of fragmentation and nearly 20 times stronger than the effect of mean commuting duration.

Mapping different forms of mobility in the Milan urban region

The increase in urban mobility is one of the key issues of contemporary cities. The need for new types of data and representations useful to describe the new forms of daily urban mobility is widely known. The wider urban scale – named urban region – is the scale at which most of the urban and socio-economical phenomena are visible. Urban growth patterns, settlements and activities distribution, demographics and economics dynamics can be fully understood and interpreted at this macro scale, which is not recognizable on the administrative boundaries. The aim of this paper is to present three approaches to mobility mapping based on different data sources, both traditional and innovative, for the Milan urban region (Northern Italy). Traditional sources for the analysis of daily mobility are Census data or surveys based on interviews to mobile populations. They provide a very partial picture of the mobility practices in urban areas, because they collect only flows for job and study purposes. Innovative sources of data are mobile phone activity data that have been used for building a sequence of mobility maps in a typical working day. The Main Map is therefore composed of two parts: a representation of systematic and non-systematic mobility in the Milan urban region; and a sequence of maps created by using telephone traffic data showing daily mobility patterns. These maps can provide useful information for understanding the recent changes that had occurred in the Milan urban region, but they can also offer a methodological reference for the analysis of mobility in general.

Enhancing early warning in the light of migration and environmental shocks

Early Warning Systems (EWS) are key elements of risk reduction and adaptation strategies to extreme events. Until now, most EWS have focused mainly on hazard detection and the immediate warning and evacuation processes with an effort of saving lives in the context of an extreme event; however, in the light of climate change and migration more attention must also be paid to the question of how EWS can improve livelihood security. On the one hand, migration patterns, urbanization and population growth are important factors that will modify and in most cases increase exposure, and in some cases increase vulnerability. On the other hand, migration in the context of extreme events and sea level rise might also be an important adaptation strategy. The paper deals with the concept of people-centred early warning and examines how aspects of the so-called Last Mile can be better taken into account. In this regard, the framework conditions and the governance context of early warning are discussed and analysed. Strategies on how to enhance EWS are outlined, particularly regarding the nexus between migration, urbanization, daily mobility and early warning. Up to now, most EWS do not sufficiently consider migration and daily mobility patterns within the information or warning chain. Therefore, the paper examines the importance of migration, urbanization and daily mobility patterns for effective early warning. On the basis of this discussion, the paper identifies new challenges for early warning in the context of climate change and deals with the potential constraints of integrative EWS. Finally, the paper derives conclusions and recommendations on not only how to better account for migration in EWS, but also how to improve the consideration of early warning in migration and adaptation strategies.