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The general transit feed specification is becoming a popular data format for the publication of public transport schedules, making possible the collection of a nation-wide public transport schedule dataset, which enables monitoring of transit supply at an up-to-date and more precise level across a country than previously possible. In this paper, we use general transit feed specification data to measure local-scale public transport availability across England based on service frequency and spatial proximity to public transport stops/stations. Moreover, to demonstrate the usefulness of public transport availability measures, we examine inequalities of public transport provision and identify areas at risk of transport poverty across England. Furthermore, we estimate population (number of households) who are likely to suffer from transport poverty, accounting for public transport availability, time-based job accessibility by public transport or walking, household income and car ownership levels. Based on the criteria, we have used to identify public transport risk, we find that investment in the development of public transport services should prioritise West Midlands, East of England, South East and South West as those regions have more households who are likely to suffer from transport poverty. This paper contributes by (1) defining more comprehensive transit availability measures than existing measures at a variety of geography levels and (2) integrating fours aspects (i.e. public transport availability, job accessibility by public transport or walking, household income and car availability) to analyse transport poverty comprehensively.

The present research examines the role of maths-related literacies, or competencies, in influencing the relationship between ICTs and travel behaviour. We adopted a Bayesian approach to jointly model the frequency of different types of internet use, and total travel distance per traveller, with respect to measures of lifewide literacies (other than reading), specifically in the form of numeracy and financial literacy questions. Our findings revealed that participants with higher levels of these literacies used the internet more frequently, and travelled further than those with fewer skills. These literacies were directly associated with total travel distance, as well as indirectly associated through internet use. Our results therefore imply that a strong policy aim to improve maths-related literacies could have implications for mitigating the effects of social exclusion in the digital age.

The Spatial Urban Data System (SUDS) is a spatial big data infrastructure to support UK-wide analytics of the social and economic aspects of cities and city-regions. It utilises data generated from traditional as well as new and emerging sources of urban data. The SUDS deploys geospatial technology, synthetic small area urban metrics, and cloud computing to enable urban analytics, and geovisualization with the goal of deriving actionable knowledge for better urban management and data-driven urban decision making. At the core of the system is a programme of urban indicators generated by using novel forms of data and urban modelling and simulation programme. SUDS differs from other similar systems by its emphasis on the generation and use of regularly updated spatially-activated urban area metrics from real or near-real time data sources, to enhance understanding of intra-city interactions and dynamics. By deploying public transport, labour market accessibility and housing advertisement data in the system, we were able to identify spatial variations of key urban services at intra-city levels as well as social and economically-marginalised output areas in major cities across the UK. This paper discusses the design and implementation of SUDS, the challenges and limitations encountered, and considerations made during its development. The innovative approach adopted in the design of SUDS will enable it to support research and analysis of urban areas, policy and city administration, business decision-making, private sector innovation, and public engagement. Having been tested with housing, transport and employment metrics, efforts are ongoing to integrate information from other sources such as IoT, and User Generated Content into the system to enable urban predictive analytics.

The amount of time we spend online has been increasing dramatically, influencing our daily travel and activity patterns. However, empirical studies on changes in the extent to which the amount of time spent online are related to changes in our activity and travel patterns are scarce, mainly due to a lack of available longitudinal or quasi-longitudinal data. This paper explores how the relationships between the time spent using the Internet, and the time spent on non-mandatory maintenance and leisure activities, have evolved over a decade. Maintenance activities include out-of-home activities such as shopping, banking, and doctor visits, while leisure activities include entertainment activities, visiting friends, sporting activities, and so forth. Our approach uses two datasets from two major cross-sectional surveys in Scotland, i.e. the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and formed. The multiple discrete–continuous extreme value (MDCEV) model and difference-in-differences (DD) estimation are applied and integrated to examine how the relationships between the time spent on the Internet and travel have changed over time and the direction and magnitude of the changes. Our findings suggest that the complementary associations between Internet use and individuals’ non-mandatory activity-travel time use are diminishing over time, whereas their substitutive associations are increasing. We additionally find that such temporal changes are significant in the case of those who spent moderate to high levels of time on the Internet (5 h or more online) per week.

We describe the Integrated Multimedia City Data (iMCD), a data platform involving detailed person-level self-reported and sensed information, with additional Internet, remote sensing, crowdsourced and environmental data sources that measure the wider social, economic and physical context of the participant. Selected aspects of the platform, which covers the Glasgow, UK, city-region, are available to other researchers, and allows knowledge discovery on critical urban living themes, for example in transportation, lifelong learning, sustainable behavior, social cohesion, ways of being in a digital age, and other topics. It further allows research into the technological and methodological aspects of emerging forms of urban data. Key highlights of the platform include a multi-topic household and person-level survey; travel and activity diaries; a privacy and personal device sensitivity survey; a rich set of GPS trajectory data; accelerometer, light intensity and other personal environment sensor data from wearable devices; an image data collection at approximately 5-second resolution of participants’ daily lives; multiple forms of text-based and multimedia Internet data; high resolution satellite and LiDAR data; and data from transportation, weather and air quality sensors. We demonstrate the power of the platform in understanding personal behavior and urban patterns by means of three examples: an examination of the links between mobility and literacy/learning using the household survey, a social media analysis of urban activity patterns, and finally, the degree of physical isolation levels using deep learning algorithms on image data. The analysis highlights the importance of purposefully designed multi-construct and multi-instrument data collection approaches that are driven by theoretical frameworks underpinning complex urban challenges, and the need to link to policy frameworks (e.g., Smart Cities, Future Cities, UNESCO Learning Cities agendas) that have the potential to translate data to impactful decision-making.

This paper provides a brief description of three new forms of key datasets relevant to urban analytics studies namely: Transport, Housing and Employment Accessibility, covering Great Britain, developed by the Urban Big Data Centre (UBDC). Full details of the research related to this paper are contained in “Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics” [1]. The transport Dataset contains public transport availability (PTA) indicators at both the stop/station and small-area levels (lower layer super output area (LSOA) and middle layer super output area (MSOA)). The employment dataset provides information on the number of people with access to employment within specific distances from each output area. The housing datasets contains quarterly house rent and sales prices aggregated at output area level (MSOA). The theoretical background for measuring the datasets at small area levels is also presented in this paper. Additionally, a variety of raw data used to produce some of the datasets (e.g. PTA) is also included to enable interested readers to reproduce them. Keywords: Urban area analytics, Public transport accessibility, Housing datasets, Employment and labour market, Small area assessment

Although young people’s mobility behaviors and their association with information and communications technologies (ICT) usage have been extensively researched, few studies have considered the relationship between changes in the use of ICT over time and young people’s travel patterns. This paper explores how use of the Internet during adolescence/late childhood and the degree of change while transitioning from late childhood to adulthood is related to sustainable travel patterns in young adults. We are particularly interested in the mediating role that attitudes towards the environment have on the relationship between early age Internet use and sustainable travel in young adults. The use of rich, longitudinal datasets, the 2004 British Household Panel Survey (BHPS) and the Understanding Society Survey (Wave 4, 2012/14), allow an investigation of these attitudes and relationships for the same people from childhood to adulthood. We use structural equation modeling (SEM) to examine the complex interrelationships between young adults’ Internet use over time, their travel modes and attitudes towards the environment, and other related behaviors. Our key finding is that consistently high levels of Internet use between adolescence and young adulthood is associated with the formation of environmental attitudes. While other factors not considered in this study might also have an effect, we find that these attitudes are indirectly but significantly associated with young adults’ sustainable travel patterns and behaviors.

Job Posting Sites, such as Indeed and Monster, are specifically designed to help users obtain information from the market. However, at the moment, only approximately half of the UK job postings have a salary publicly displayed. Therefore, the aim of this research is to model and predict the salary of a new job, so as to improve the performance of job search and help a vast amount of job seekers better understand the market worth of their desirable positions. In order to effectively estimate the salary of a given job, we construct a graph database based on job profiles of each posting and build a predictive model through machine learning based on both metadata features and relational features. Our results reveal that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in graph convolutional network (GCN), a semi-supervised learning framework, to make use of a large amount of unlabelled data, in addition to the set of labelled ones, for enhanced salary classification. The preliminary experimental results show that GCN outperforms the existing ones that simply pool these two types of features together.

This research employs novel techniques to examine older learners’ journeys, educationally and physically, in order to gain a ‘three-dimensional’ picture of lifelong learning in the modern urban context of Glasgow. The data offers preliminary analyses of an ongoing 1500 household survey by the Urban Big Data Centre (UBDC). A sample of 1037, with 377 older adults aged 60+, was examined to understand older learner engagement in formal, in-formal, non-formal and family-learning contexts. Preliminary findings indicate that all forms of older learning participation are lower than younger and middle-age counterparts. However, there is a subset of ‘actively ageing’, socially and technologically engaged older adult ‘learner-citizens’, participating in educational, physical, cultural, civic and online activities (including online political discussions and boycotts). These older learners were more likely to be working, caretakers and report better health overall. Long-term disabilities were associated with less engagement in non-formal learning activities. Additionally, engaged older learners’ GPS trails show more city activity than their matched non-learning-engaged counterparts. Place-based variables, such as feeling safe and belonging to the local area, moderated adult participation in learning activities. The full data-set will be accessible to researchers and the general public via UBDC, providing a complex data source to explore demographically diverse learners’ within an urban context.

Recent increases in the use of and applications for wearable technology has opened up many new avenues of research. In this paper, we consider the use of lifelogging and GPS data to extend fine-grained movement analysis for improving applications in health and safety. We first design a framework to solve the problem of indoor and outdoor movement detection from sensor readings associated with images captured by a lifelogging wearable device. Second we propose a set of measures related with hazard on the road network derived from the combination of GPS movement data, road network data and the sensor readings from a wearable device. Third, we identify the relationship between different socio-demographic groups and the patterns of indoor physical activity and sedentary behaviour routines as well as disturbance levels on different road settings.

The application of location-based social media big data in urban contexts offers new and alternative strategies for understanding city liveliness in developing countries where traditional census data are poor. This paper demonstrates how the spatial-temporal distribution of China's Tencent social media usage intensities can be effectively used as a proxy for modelling the geographic patterns of human activity at fine scales. Our results suggest that the spatially-temporally contextualized nature of human activity is dependent upon land use mixing characteristics. With billions of social media data being collected in the virtual world, findings of this study suggest that land use policies to delineating the density, orderly or disorderly geographic patterns of human activity are important for city liveliness.

Researchers in multiple disciplines have used Twitter to study various mobility patterns and “live” aspects of cities. In the field of transportation planning, one major area of interest has been to use Twitter data to infer movement patterns and origins and destinations of trip-makers. In the area of transportation operations, researchers have been interested in automated incident detection or event detection. Because the number of geotagged tweets pinpointing the location of the user at the time of tweeting tends to be sparse for transportation applications, there is a need to consider expanding and geolocalising the sample of non-geotagged tweets that can be associated with locations. We call this process “geolocalisation”. While geolocalisation is an active area of research associated with the geospatial semantic Web and Geographic Information Retrieval, much of the work has focused on geolocalisation of users, or on geolocalisation of tweeting activity to fairly coarse geographical levels, whereas our work relates to street-level or even building-level geolocalisation. We will consider two different approaches to geolocalisation that make use of Points of Interest databases and a second information retrieval-based approach that trains on geotagged tweets. Our objective is to make a comprehensive assessment of the differences in spatial and content coverage between non-geotagged tweets geolocalised using different approaches compared to using geotagged tweets alone. We find that using geolocalised tweets allows discovery of a larger number of incidents and socioeconomic patterns that are not evident from using geotagged data alone, including activity throughout the metropolitan area, including deprived “Environmental Justice” (EJ) areas where the degree of social media activity detected is usually low. Conclusions are drawn on the relative usefulness of the alternative approaches.

No abstract available.

The effects of information and communications technologies (ICT) on mobility have been investigated for several decades; however, few studies have focused on the amount of time spent using ICT, its implications on travel behavior, and how such relationships may vary with the characteristics of residential location. This study focuses on the Internet use for nonwork purposes and two research questions are examined: Does the amount of time spent on the Internet affect motorized trip generation and motorized travel distance? How do these effects vary according to residential location such as urban, town, and rural areas where the levels of accessibility are different? We find there is a nonlinear relationship between the amount of time spent on the Internet for personal purposes and motorized mobility (i.e., auto and public transit trips), with both very low-end as well as very high-end Internet users having lower levels of motorized mobility, while moderate-intensity users having higher levels of mo...

In this article, we expand upon earlier analysis of location-service-related privacy policies in the public and private sector. Our intent is to identify key privacy components in an effort to more clearly and consistently address the privacy expectations of the public through the protections provided them by service and product providers. Through use of content analysis, we aim to determine how understandable current privacy policies are to the average consumer, how comprehensively they address identified components of privacy, and how consistently privacy is treated in the location arena. It is hoped that this analysis will assist with the development of practical and comprehensive approaches to privacy preservation in the area of intelligent transportation systems and location-based services.

Weather information is frequently requested by travelers. Prior literature indicates that inclement weather is one of the most important factors contributing to traffic congestion and crashes. This paper proposes a methodology to use real-time weather information to predict future speeds. The reason for doing so is to ultimately have the capability to disseminate weather-responsive travel time estimates to those requesting information. Using a stratified sampling technique, cases with different weather conditions (precipitation levels) were selected and a linear regression model (called the base model) and a statistical learning model [using support vector machines for regression (SVR)] were used to predict 30-min-ahead speeds. One of the major inputs into a weather-responsive short-term speed prediction method is weather forecasts; however, weather forecasts may themselves be inaccurate. The effects of such inaccuracies are assessed by means of simulations. The predictive accuracy of the SVR models show that statistical learning methods may be useful in bringing together streaming forecasted weather data and real-time information on downstream traffic conditions to enable travelers to make informed choices.

This paper assesses non-traditional urban digital infomediaries who are pushing the agenda of urban Big Data and Open Data. Our analysis identified a mix of private, public, non-profit and informal infomediaries, ranging from very large organizations to independent developers. Using a mixed-methods approach, we identified four major groups of organizations within this dynamic and diverse sector: general-purpose ICT providers, urban information service providers, open and civic data infomediaries, and independent and open source developers. A total of nine types of organizations are identified within these four groups.

Big Data is the term being used to describe a wide spectrum of observational or “naturally-occurring” data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, their use for research and analysis becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data.

The chapters in this book were first presented in a 2-day workshop on Big Data and Urban Informatics held at the University of Illinois at Chicago in 2014. The workshop, sponsored by the National Science Foundation, brought together approximately 150 educators, practitioners and students from 91 different institutions in 11 countries. Participants represented a variety of academic disciplines including Urban Planning, Computer Science, Civil Engineering, Economics, Statistics, and Geography and provided a unique opportunity for discussions by urban social scientists and data scientists interested in the use of Big Data to address urban challenges. The papers in this volume are a selected subset of those presented at the workshop and have gone through a peer-review process.

Trajectory datasets are being generated in great volumes due to high levels of Global Positioning System (GPS) and Location-Based Services (LBS) use. Such data are increasingly being collected for a variety of academic, industrial and recreational reasons, sometimes together with other strands of personal data such as socio-demographic, social survey and other sensor data carried/worn by the person. In such cases, not only are movement data of a person available but also data on potentially a wide variety of other personal and household attributes. Making such person-level data available for analytics opens up the possibility of new directions in analysing, studying and understanding human behaviour, which is typically not possible with GPS trajectory datasets alone. At the same time, the GPS data should be released in a privacy-preserving way that takes into account the possibility of re-identification of individuals from quasi-identifiers available from other data strands. De-identification in these strands may be risked due to uniquely identifiable information on significant locations and other spatial behaviours and choices detected from GPS trajectories. Using a multimodal dataset that includes a GPS archive from 358 individuals, and by considering a number of alternative privacy-enhancing approaches, we look at the potential for privacy preservation when personally-identifiable data are available from multiple data strands, for the specific purpose of data to be released for transport research.

This paper examines the role that public transport last mile problems play in mode choice decisions of commuters, while controlling for trip, built environment, and decision maker related variables. Last-mile problems arise due to lack of adequate connectivity between transit stops and trip origin or termination points. The paper is motivated by previous literature which has pointed out that high-quality public transit needs to consider end-to-end connectivity from trip origins to destinations. In contrast to previous work on transit last mile problems, which has focused on physical distance and sidewalks to transit stops, we consider a wider range of area factors including transit availability, job accessibility, parking costs, the quality of the pedestrian environment and risks to pedestrians from vehicular traffic, and social characteristics such as street-level crime. Using a discrete choice model, our goal is to unpack ways in which such factors contribute to the last-mile problem in home-based work trips, while controlling for these wider range of factors as well as the usual variables such as cost and trip time that inform mode choice. We find that the prevalence of non-domestic violent crimes reduces the odds of using all types of non-motorized alternatives as well as transit that is accessed either by walking or driving. Using compensating variation to measure welfare changes, we show that there are significant benefits that could be brought to transit service users through increasing safety in the transit access trip. By separately controlling for origin and destination transit accessibility, we show that improved destination accessibility significantly boosts transit use to a greater degree than increases in origin level accessibility. These findings argue for improving accessibility and related job densities at employment centers.

In this paper, using longitudinal data on route level monthly average weekday ridership in the entire Chicago Transit Authority (CTA) bus system from January 2002 through December 2010, we evaluate the ridership effects of the CTA real-time bus information system. This bus information system is called CTA Bus Tracker and was incrementally implemented on different CTA bus routes from August 2006 to May 2009. To take account of other factors that might affect bus ridership, we also include data on unemployment levels, gas prices, local weather conditions, transit service attributes, and socioeconomic characteristics during the study period. This combined longitudinal data source enables us to implement a quasi-experimental design with statistical controls to examine changes in monthly average weekday ridership, before and after the Bus Tracker system was implemented, on each bus route. Based on a linear mixed model, we found that the provision of Bus Tracker service does increase CTA bus ridership, although the average increase is modest. Further, the study findings suggest that there are temporal variations of the ridership effects among the routes, with the “winning” routes more likely to have the technology implemented in the later phases of the overall “roll-out” period. However, the results are less conclusive regarding geographical variations in the effects of Bus Tracker.

The US federal Congestion Mitigation and Air Quality program was created to fund transportation projects that have the potential to contribute to the attainment or maintenance of the National Ambient Air Quality Standards. This study evaluates a random sample of eight suburban bicycle and pedestrian facilities funded by the Congestion Mitigation and Air Quality Program in the Chicago metro area. Users of these facilities were randomly sampled using a "time-based" user sampling approach and surveyed in intercept mode to implement a recall-based evaluation design. A random intercept logit model was used to explain variations in the propensity to switch from single occupant vehicle use to bicycling or walking, using attributes of users and sites. The analysis shows relatively low usage levels but that substitution of motorized modes have resulted, potentially leading to improved air quality outcomes.

The paper examines two labour market outcomes experienced by users of federally-funded transportation services for low-wage workers in the United States, using primary data from 23 locations. The propensity of users to be unemployed prior to using the service is found to be related to the type of service (fixed-route/demand-response) and location type (urban/rural) but not to aggregate local unemployment, variability in local unemployment rates or local welfare policies. The propensity to earn higher wages is related to the type of employment transportation service and location, and local unemployment levels. Results imply a need for locally-derived, coordinated employment transportation plans. Resumen Este articulo examina dos consecuencias del mercado laboral sufridas por los usuarios de servicios de transporte subvencionados por el gobierno federal para trabajadores de bajos ingresos en los Estados Unidos, haciendo uso de datos primarios de 23 localizaciones. Se ha encontrado que la propension de los usuarios a estar desempleados antes de utilizar el servicio esta relacionada con el tipo de servicio (ruta fija/demanda-respuesta) y el tipo de localizacion (urbana/rural), pero no con el desempleo local agregado, la variabilidad de las tasas de desempleo local o las politicas locales de asistencia social. La propension a ganar salarios mas altos esta relacionada con el tipo de servicio de transporte del empleo y la localizacion, y las tasas de desempleo locales. Los resultados implican la necesidad de planes coordinados de transporte del empleo basados en las condiciones locales.

The author examined cost-effectiveness of employment transportation (ET) services funded by the Job Access and Reverse Commute program of FTA in 2002. These services transport low-wage workers to jobs. The author presented multiple sources of primary and administrative data on users and programs in 23 ET sites. ET cost per ride measures were compared with cost per ride measures of transit services that were operating in the same area (called peer transit services). A cost per user per year was estimated to determine the level of transportation investments on the mobility of low-wage workers. On a per ride basis, ET services cost more to deliver than did peer transit services, but a wide variation existed in the level of investments and subsidies among different types of users, areas, and types of service. The median estimated cost per ET service user per year was similar to the annual investment per recipient by several social and human services and workforce development programs that supported low-income individuals to achieve improved economic outcomes. ET annual expenditures were the greatest for low-wage individuals who were traveling to work and less so for low-income individuals who were traveling to job training, to look for jobs, or to shop and make other discretionary trips. Implications are drawn for human services transportation coordination and for data and reporting requirements.

This paper examines whether the psychological effects of real-time transit information on commuters will lead to a gain in transit ridership. A conceptual model, which posits a simultaneous structure among psychological and behavioral constructs, was developed on the basis of cognitive models of behavior. Path analysis was used to analyze such a process. A detailed stated preference survey for Chicago commuters composed the data-gathering approach. The analysis results showed that real-time transit information systems might achieve the goal of increasing transit ridership through their psychological effects on commuters. The results indicated that the provision of real-time transit information might serve as an intervention to break current transit nonusers’ travel habits and in consequence increase the mode share of transit use. Moreover, the results of this study suggest that real-time transit information may be more successful in increasing transit ridership if combined with facilitating programs that enhance commuters’ opportunities to be exposed to such systems before using them.

The aging of the American population has raised a number of concerns about senior mobility. The New Freedom (NF) program, which was designed to go above and beyond the transportation requirements of the Americans with Disabilities Act, has been used to fund transportation for seniors. The primary purpose of this paper is to present the results of an exploratory analysis of the social, physical, and functional health and mobility outcomes experienced by seniors who are clients of NF services and the associated Coordinated Human Services Transportation Plan (CHSTP) processes. The paper uses data from primary surveys of the following groups from seven urban, suburban, and rural locations: (a) NF-funded transportation service users, (b) program managers operating the services, and (c) locally selected lead and partner organizations involved in developing the CHSTPs. Results show that concerns about senior mobility vary significantly by a number of functional, social, and physical disability indicators, including instrumental activities of daily living. These indicators are substantially more complex than a chronological accounting of age. A principal component analysis yielded two constructs underlying seniors’ mobility: a transportation deprivation component and an independence and health deprivation component. Each component has implications for different types of services. To the extent that the NF program has supported transportation services that are supplemental to integrated care for seniors and thereby enable a continuum of care, the program has enhanced community-based interventions to meet the complex mobility needs of seniors in the seven locations.

With the growing trend toward location-based services in transportation, the collection and the use of traveler data have grown rapidly. With the potential for multiagency sharing of data and an increased public-private partnerships, inconsistencies in how data are collected and shared may become of growing concern for users, particularly given the potential sensitivity of locational data. An overall picture of policies relevant to data collection and use in the private locational environment highlights the gaps that may be evident between the privacy recommendations of regulatory and industry guidance agencies and individual company policies. The approach combines policy and content analysis to determine how well identified privacy considerations are reflected in user-oriented privacy policies.

An individual-level mixed longitudinal data set with three separate cohorts representing three generations in U.S. society is used to examine how car ownership trends have changed among young adults (18 to 24 years of age) over a 40-year period, from the mid-1960s to the mid-2000s. A multilevel, mixed model for binary car-ownership outcomes was estimated by using maximum likelihood. After controlling for factors relating to sociodemographics, personal life, location, and labor market, individuals in the past generation considered (who were young adults in the late 1990s and early 2000s) were found to be significantly more likely to enter the car market at an earlier age. In contrast to earlier generations, by the time individuals in the last generation cleared the 18- to 24-year window, they were found to have reached car ownership levels that had been predicted by earlier studies to be the saturation levels of car ownership in a society. Minorities made greater progress in rates of car ownership between the late 1960s and the early 1980s compared with nonminorities; however, increases in the rates of car ownership slowed for minorities in the most recent generation. Young adults in urban areas led those in rural areas in the earlier generations on car ownership rates but fell behind their rural counterparts in the last generation considered. Across all three generations, vehicle ownership was lower among the unemployed compared with the employed. The differential between employed and unemployed increased with each successive generation and was almost 30% in the last generation considered.

Operating public buses on urban highway shoulders is a strategy being explored as a means to fight congestion and attract more riders to public transit. However, little information is available about the institutional and legal issues, project development process, costs, and performance of such operations. Results are presented of a study undertaken in northeastern Illinois to examine these preliminary but important issues, which must be addressed to the satisfaction of a diverse group of stakeholders. Findings from a survey of core stakeholders are reported. Concerns and possible resolutions that can be used to engage a larger group of stakeholders at a later time in the planning phase are documented.

A variety of transit decision support tools have been developed in the Chicago, Illinois, metropolitan area in recent years, including the Regional Transportation Asset Management System of the Regional Transit Authority and the Spatial Decision Support System of the Urban Transportation Center at the University of Illinois at Chicago. Although the Chicago metropolitan area has a variety of public transportation services, the quality of service available in an area and the extent to which transit allows area residents to access employment opportunities spread out across the six-county region vary substantially. This paper focuses on a spatial analysis of the variations of local transit service quality indicators as well as a composite regional employment accessibility measure. It explores the quality of the transit system in the Chicago region through a set of supply- and demand-side indicators at the census tract level. The supply-side indicators include a composite index of transit availability and frequency and transit station asset information. The demand-side measures include the computation of the regional employment accessibility index using a gravity model and transit travel times from travel demand models. A series of these indicators is mapped over the Chicago region at the census tract level. The paper also ranks census tracts on the local transit measures, as well as on the regional transit-based employment accessibility measures, and identifies those areas that are well off and worst off in regard to both types of indexes. The paper draws policy conclusions emanating from each of these categories.

Having the ability to detect emerging patterns in cities is crucial for efficient management of urban resources. Patterns that are useful in identifying and addressing future resource consumption needs include spatial changes in urban form and structure as well as temporal changes in human concentrations and activity patterns during the course of a day. Other patterns of interest are characteristics of local populations in dynamically changing neighborhoods and social-functional spaces. In this paper, we use the Integrated Multimedia City Data (iMCD) platform which brings together multiple strands of structured and unstructured data, to examine such trends in the Greater Glasgow region. We present an approach to, first, understand spatial and time-dependent changes that capture the flow of resources needed to meet demands of residents and businesses at different times and locations, and second, generate hypotheses regarding urban engagement, activity patterns and travel behaviour. We use social media data, GPS trajectories, and background data from the UK Population Census for this purpose. The approach identifies the “roughness” in activity patterns across the urban space that are indicative of different concentrations of social and functional activities. When the time dimension is added to the mix, we are able to uncover time-varying transitions from one type of use pattern into another in different parts of the region. Such transitions, particularly in mixed-use areas, allow early detection of points of excess urban metabolism, with implications for traffic congestion, waste production, energy and other resource consumption patterns. Finally, the ability to detect what citizens talk about socially may provide a way to understand whether or not the language patterns detected in different parts of the city reflect underlying uses and concerns. A preliminary step to evaluate this idea is explored by extracting context-awareness and semantic enrichment to socially-generated data.

Urban Informatics aims to exploit the large quantities of information produced by modern cities in order to gain insights into how they function. These insights lay the foundation for improving the lives of citizens, by improving the efficacy and efficiency of public services, and satisfying complex information needs arising within this context. The goal of the workshop is to provide a multidisciplinary forum which brings together researchers in Big Data (BD), Information Retrieval (IR), Data Mining, and Urban Studies, to explore novel solutions to the numerous theoretical, practical and ethical challenges arising in this context. These include difficulties in collecting city data, creating data management infrastructures, and providing new effective and efficient information access techniques to as many users as possible in the context of a smart city. To foster the development of new BD and IR approaches in Urban Informatics, the workshop makes available a representative dataset of city data, including Internet-based visual (Flickr) and textual (Tweets and News) media collections. The workshop provides enormous opportunities for data scientists who wish to understand the complexities of working with city data, conduct innovative research within Urban Informatics, and build a long-term community in this emerging research area.

Data at several levels of aggregation and spatial resolution show that mobility (measured by different outcomes such as vehicle miles traveled and automobile ownership) increases with income. It is equally likely that, in turn, investments in mobility (purchase and operation of private automobiles, consumption of public transportation services) allow greater quality of life in general and the ability to increase one's income. This possibly circular relationship is explored by using the Consumer Expenditure Survey (CEX) data set. Single-equation Tobit models of annual household transportation expenditures are posited with, in the first case, annual before-tax income and, in the second case, annual total household-level expenditures as a proxy for permanent income. With a variety of demographic, community and spatial, economic, family, and life-cycle conditions of the household controlled, it is found that permanent incomes explain mobility investments better than annual incomes. However, mobility investments and ability to pay are likely to be endogenous since it seems that a greater investment in mobility would lead to positive economic outcomes such as the ability to travel to distant but higher-paying jobs, better and more productive integration of social and household activities into one's work life, and flexibility in job search and ability to change employers to obtain higher wages and benefits without having to make residential location changes. This potential reverse causality is explored by means of a two-stage model in which the censored mobility investment variable and the household's ability to pay are jointly modeled.

Alternative work arrangements and unconventional shift schedules are increasingly becoming a part of the economy. Temporal mismatch problems occur when individuals who are traditionally transit dependent do not have access to potential job locations during off-peak times. The Current Population Survey is used to examine the relationships between job start times and a set of Sociodemographic, occupational, and industrial characteristics. A binary response variable, conventional and unconventional start times, gives preliminary insight into the nature of these relationships. A detailed multinomial logit model gives more in-depth insight by analyzing the discrete choice of job start times during unconventional hours, such as the p.m. hours, night hours, or midday, relative to a baseline daytime shift. In addition, probabilities of working in jobs with alternative start times are developed for a set of hypothetical workers with key sociodemographic profiles. It is concluded that workers are self-selected into certain occupations in specific industries by their skills, education, and other workforce factors. The nature of jobs has implications for work schedules and alternative work arrangements. The ability to select certain schedules and the work arrangement of choice therefore is conditional on being employed in those types of jobs. Thus, there appears to be some type of simultaneity between the type of occupation and work schedules. It appears that policies to address the temporal mismatch problem are partly an issue of workforce development (so that workers can seek jobs in more work-arrangement-friendly occupations) and partly a transportation problem.

A linked database comprising state and federal roadside inspection, crash, and firm records of commercial motor vehicles is described. This combined database links information on vehicles inspected in the state of Illinois to the crashes that these vehicles may have been involved in anywhere within the United States. Two issues are addressed: determination of the components of an inspection-violation detection rate and the relationship between inspections and crashes. The first issue involves an attempt to quantify the components of a violation detection rate. For the second issue, four separate research questions are posed. The results of the second component indicate that there is no evidence that a significant difference exists in the expected number of crashes in which vehicles incurring zero violations were involved compared with the expected number in which vehicles incurring one or more violations were involved. About 2 percent of all inspected vehicles, which incurred zero violations, were subsequently involved in crashes. With every increase in the number of violations, the percent of vehicles involved in crashes increases by about 0.04. Further, a smaller percentage of vehicles that incur zero violations in Level I inspections are subsequently involved in crashes, compared with vehicles incurring zero violations under all Level II and Level V inspections. Moreover, the rate of increase in the percent involvement in crashes is greatest for vehicles incurring a positive number of violations in Level I inspections compared with vehicles subject to other types of inspection.

Women who have been on public assistance need to obtain and maintain steady employment because they stand to lose their public benefits and also because it is the only way out of poverty. Although the sociodemographic and general economic influences on job retention have been examined in the literature, the effects of transportation and of place of residence in a metropolitan area vis-à-vis entry-level job locations have not been studied systematically. Four sets of factors—transportation, location, sociodemographic, and family effects—are examined for their effect on job retention. In particular, it was found that employment security for female welfare clients or former clients does not come from job retention (i.e., tenure with the same employer) but from “employment retention” (i.e., jobs with different employers, possibly with a trend toward upward mobility). The effects of transportation and location on job and employment retention are complex. Although access to a vehicle is important for increasing employment retention, even more important is the number of job opportunities accessible by private vehicle or public transit within a tolerable travel time. Female welfare clients who retain a job longer and hold more jobs within a 2-year period are more likely to live in subareas of the metropolitan area with greater access to jobs within reasonable travel times; the competition for those jobs from other low-income individuals is low. Furthermore, female welfare clients with a high school diploma, when given the appropriate accessibility and location opportunities, enjoy increased job retention.