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In Sweden, responsibility for the public care of the frail elderly rests with three authorities acting at different levels. At national level, the Riksdag and the Government realize policy goals through legislation and financial control measures. At regional level, 18 county councils and two regions are responsible for the provision of health and medical care. At local level, Sweden's 290 municipalities have a statutory duty to meet the social service and housing needs of the elderly. Sweden's municipalities and county council have a high level of autonomy by international standards. Activities in caring services are ultimately controlled by politicians appointed to policy-making assemblies in municipalities and county councils through general elections. The decentralization of responsibility for elderly care makes it possible for local and regional conditions to be taken into account when policies for the elderly are formulated. The national authorities – the National Board of Health and Welfare and the 20 county/region administrative boards – are responsible for supervision, follow-up, and evaluation of municipal and county council caring services.

Purpose — This paper describes what the authors believe to be the first GPS-only full-scale household travel survey.

Design/methodology — The survey commenced in early 2009 with the conduct of a pilot survey to help establish various parameters and procedures for the main survey. The main survey commenced in August 2009 and was completed in August 2010. It was designed as a household travel survey to be collected steadily over a 12 month period. The target sample size was originally set at over 3500 households, although this target was reduced downwards during the course of the survey. Each household member over the age of 12 was asked to carry a GPS device with them everywhere they went for a period of 3 days. After the 3-day collection period was completed, GPS devices were retrieved from households, the data were downloaded and processing of the data commenced. The study also involved a PR survey performed on the Internet.

Findings — The paper concludes with lessons learnt from this GPS-only survey and suggestions for how future GPS-only surveys might be conducted.

Originality/value of the paper — The paper describes the first GPS-only household travel survey and concludes that it is now feasible to conduct household travel surveys by GPS.

This chapter summarizes some of the results from the Working Group “National Travel Surveys” in COST Action 355. All 50 presentations could not be reviewed here; thus, we focus on three crucial topics:

• the periodicity of data collection, but also its longitudinal aspects (advantages of continuous surveying, repeated cross-sections vs. panel surveys, etc.),

• new technologies for improving the efficiency and accuracy of mobility surveys (computer-assisted telephone, Web-based, interviews, GPS, GSM, RDS, etc.),

• innovative approaches, exemplified by qualitative surveys combined with conventional quantitative ones, and by biographical approaches.

the periodicity of data collection, but also its longitudinal aspects (advantages of continuous surveying, repeated cross-sections vs. panel surveys, etc.),

new technologies for improving the efficiency and accuracy of mobility surveys (computer-assisted telephone, Web-based, interviews, GPS, GSM, RDS, etc.),

innovative approaches, exemplified by qualitative surveys combined with conventional quantitative ones, and by biographical approaches.

Transport policy is constrained by the dilemma of the positive impacts of lowering the generalised costs of travel and their today non-ignorable negative externalities. This chapter details this dilemma and discusses current policy ideas to manage and overcome it against the impacts of the COVID-19 pandemic on travel habits and work behaviour. The impacts are presented for Switzerland for which a large-scale GPS tracking survey spanning the autumn 2019 to winter 2021 period is available. The chapter concludes by highlighting the dilemma of transport policy by discussing a number of potential solutions for the future.

The disproportionate identification of learning disabilities among certain sociodemographic subgroups, typically groups who are already disadvantaged, is perceived as a persistent problem within the education system. The academic and social experiences of students who are misidentified with a learning disability may be severely restricted, while students with a learning disability who are never identified are less likely to receive the accommodations and modifications necessary to learn at their maximum potential. In addition to inconsistent definitions of and criteria for diagnosing students with learning disabilities that may result in misdiagnoses, it is feared that discrimination also plays a role. We use the Education Longitudinal Study (ELS) of 2002 to describe national patterns in learning disability identification by individual- and school-level characteristics. Our results indicate that sociodemographic characteristics are predictive of being identified with a learning disability. Whereas some conventional areas of disproportionality are confirmed (males and language minorities are more likely to be identified), differences in social class entirely account for black and Hispanic disproportionality. Discrepancy between the results of bivariate and multivariate analyses reaffirms the importance of employing sophisticated methodology in explorations of disproportionality.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.