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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.

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.

No abstract available.

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.

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.

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.

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.

In an earlier paper a method for computing static profiles of link travel times was given. In this paper, the centrality of such profiles for ATIS is examined and the methods given in the earlier paper are applied to actual data. Except for a minor, easily correctable problem, the methods are shown to work very well under real-life conditions.

In this paper, a probabilistic linkage method is explored in the context of linking databases in the Commercial Motor Vehicle and Carrier (CMVC) sector as a potential solution to overcome data quality problems. An application of this method is demonstrated by linking commercial motor vehicle inspection files kept by the Illinois State Police (ISP) and the inspection files available from the Illinois portion of the Motor Carrier Management Information System (MCMIS). Since one of the files to be matched is a subset of the other, the application allows us to validate the methodology. The results show 6,228 correct identifications of true matched record pairs out of 6,335 actual true matches (more than 99%) between the two files. The number of erroneously identified record pairs is 690 (about 11% of the actual true matched pairs.) Sensitivity analysis is conducted of error rates with respect to variations in the optimal thresholds for merging the databases. A simple analysis also shows how much of a clerical examination for unclear record pairs would have to be tolerated for a reduction in dollar expenditure.