Inferring land use from mobile phone activity

Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.Comment: To be presented at ACM UrbComp201

Coupling Human Mobility and Social Ties

Studies using massive, passively data collected from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion, and organizational dynamics. More recently, these data have come tagged with geographic information, enabling studies of human mobility patterns and the science of cities. We combine these two pursuits and uncover reproducible mobility patterns amongst social contacts. First, we introduce measures of mobility similarity and predictability and measure them for populations of users in three large urban areas. We find individuals' visitations patterns are far more similar to and predictable by social contacts than strangers and that these measures are positively correlated with tie strength. Unsupervised clustering of hourly variations in mobility similarity identifies three categories of social ties and suggests geography is an important feature to contextualize social relationships. We find that the composition of a user's ego network in terms of the type of contacts they keep is correlated with mobility behavior. Finally, we extend a popular mobility model to include movement choices based on social contacts and compare it's ability to reproduce empirical measurements with two additional models of mobility

Cost-Effective Control of Infectious Disease Outbreaks Accounting for Societal Reaction

Background Studies of cost-effective disease prevention have typically focused on the tradeoff between the cost of disease transmission and the cost of applying control measures. We present a novel approach that also accounts for the cost of social disruptions resulting from the spread of disease. These disruptions, which we call social response, can include heightened anxiety, strain on healthcare infrastructure, economic losses, or violence. Methodology The spread of disease and social response are simulated under several different intervention strategies. The modeled social response depends upon the perceived risk of the disease, the extent of disease spread, and the media involvement. Using Monte Carlo simulation, we estimate the total number of infections and total social response for each strategy. We then identify the strategy that minimizes the expected total cost of the disease, which includes the cost of the disease itself, the cost of control measures, and the cost of social response. Conclusions The model-based simulations suggest that the least-cost disease control strategy depends upon the perceived risk of the disease, as well as media intervention. The most cost-effective solution for diseases with low perceived risk was to implement moderate control measures. For diseases with higher perceived severity, such as SARS or Ebola, the most cost-effective strategy shifted toward intervening earlier in the outbreak, with greater resources. When intervention elicited increased media involvement, it remained important to control high severity diseases quickly. For moderate severity diseases, however, it became most cost-effective to implement no intervention and allow the disease to run its course. Our simulation results imply that, when diseases are perceived as severe, the costs of social response have a significant influence on selecting the most cost-effective strategy.United States. Defense Threat Reduction Agency (Contract HDTRA1-12-C-0061

Understanding the Link between Urban Activity Destinations and Human Travel Pattern

URL to abstract on conference site. You have to be a conference participant to access papers.In the urban transportation field, planners and engineers have explored the relationship between urban destinations and travel behavior for more than half a century. However, we still have only a preliminary understanding about how the spatial arrangement of different types of urban activity destinations influence human travel, and how urban development policies influence travel patterns. Recent developments in urban sensing and cell phone technologies have enabled spatially-detailed and massive GIS-based datasets on land use, points of interest (POIs), cell phone and GPS-based tracking, etc. These new datasets provide rich possibilities for better understanding and modeling of urban activity patterns and travel behavior. In this study, we utilize such spatially-detailed data—POI data and large-scale travel tracker data—to explore the link between urban activity destinations and human travel patterns. First, we employ the timely and large-scale urban activity-based travel survey for the Chicago Metropolitan Area, implemented from January 2007 to February 2008 (Chicago Metropolitan Agency for Planning 2008). We derive travel patterns for both commute and non-commute activities in the Chicago Metropolitan Area for individuals and groups of individuals with various socioeconomic characteristics. The Chicago activity-based travel survey includes 10,552 households (23,452 individuals) who participated in either a one-day or two-day survey, providing detailed travel information for each household member for a particular assigned travel day(s). Second, we combine a spatially-detailed business establishment dataset (the InfoUSA 2008 data) included in the ESRI Business Analyst Package (ESRI 2009) and the employment data in the U.S. census to analyze the spatial distribution of urban activity destinations in the Chicago Metropolitan Area. Finally, we examine the linkage between these two aspects, and demonstrate the impacts of spatial arrangement of urban activity destinations on human travel patterns in the urban settings. This new study is crucial to understanding how the spatial patterns of urban activity destinations influence individuals’ and groups of individuals’ travel patterns at both individual and aggregated level. It is also important for policy making in the fields of urban development and transportation planning.MIT-Portugal ProgramMassachusetts Institute of Technology. Dept. of Urban Studies and Plannin

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.Singapore. National Research Foundation (through the Singapore-MIT Alliance for Research and Technology (SMART) Center for Future Urban Mobility (FM))Center for Complex Engineering Systems at MIT and KACS

Discovering urban spatial-temporal structure from human activity patterns

Urban geographers, planners, and economists have long been studying urban spatial structure to understand the development of cities. Statistical and data mining techniques, as proposed in this paper, go a long way in improving our knowledge about human activities extracted from travel surveys. As of today, most urban simulators have not yet incorporated the various types of individuals by their daily activities. In this work, we detect clusters of individuals by daily activity patterns, integrated with their usage of space and time, and show that daily routines can be highly predictable, with clear differences depending on the group, e.g. students vs. part time workers. This analysis presents the basis to capture collective activities at large scales and expand our perception of urban structure from the spatial dimension to spatial-temporal dimension. It will be helpful for planers to understand how individuals utilize time and interact with urban space in metropolitan areas and crucial for the design of sustainable cities in the future.Massachusetts Institute of Technology. Dept. of Urban Studies and PlanningUnited States. Dept. of TransportationSingapore-MIT Alliance for Research and Technology Cente

Demand and Congestion in Multiplex Transportation Networks

Urban transportation systems are multimodal, sociotechnical systems; however, while their multimodal aspect has received extensive attention in recent literature on multiplex networks, their sociotechnical aspect has been largely neglected. We present the first study of an urban transportation system using multiplex network analysis and validated Origin-Destination travel demand, with Riyadh’s planned metro as a case study. We develop methods for analyzing the impact of additional transportation layers on existing dynamics, and show that demand structure plays key quantitative and qualitative roles. There exist fundamental geometrical limits to the metro’s impact on traffic dynamics, and the bulk of environmental accrue at metro speeds only slightly faster than those planned. We develop a simple model for informing the use of additional, “feeder” layers to maximize reductions in global congestion. Our techniques are computationally practical, easily extensible to arbitrary transportation layers with complex transfer logic, and implementable in open-source software