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From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

Production-related industrial zones, super structures and infrastructures are constructed by the construction industry. Nearly all industries and their environmental emissions are influenced by the construction industry including its sub-industries, companies and their supply chains. Furthermore, cities play an important role in economic growth. Cities are hubs for productivity, production, supply and demand, and innovation with the help of their human capital and built environment (e.g. offices, factories, industrial zones, infrastructures, etc.).

Industrial growth fosters urbanisation which is vital for the supply side in the economy to reach to the human resources. Urbanisation which supports industrial growth obstacles industries’ efficiency due to urbanisation problems (e.g. traffic, air and water pollution, health problems).

Construction industry and its sub-industries affect total factor productivity growth in nearly all industries. Construction industry can be a facilitator industry for economic growth and industrial growth considering total factor productivity growth and environment aspects. All industries’ green and sustainable total factor productivity growth can be supported by rethinking construction industry, its sub-industries and their outputs (e.g. construction materials, built environment, cities) as well as construction project management processes.

This chapter aims to introduce carbon capturing smart construction industry model to foster green and sustainable total factor productivity growth of industries. This chapter emphasises current and potential roles of construction industry, its sub-industries and their outputs in fostering other industries’ growth through green and sustainable total factor productivity growth. It focusses on carbon capturing technologies and design at different levels. Furthermore, this chapter emphasises cities’ role in green and sustainable total factor productivity growth. This chapter provides recommendations for construction industry policies and carbon capturing cities/built environment model to solve urbanisation problems and to foster industrial growth and green and sustainable total factor productivity growth. This chapter is expected to be useful to all stakeholders of the construction industry, policy makers, and researchers in the relevant field.

Purpose — Fare validation data from transit smart card automatic fare collection (AFC) systems have properties that align with the direction of large-scale mobility surveys and the evermore demanding data needs of the transit industry. In addition to applications in transit planning and service monitoring, travel patterns and behaviour can effectively be studied by exploiting the continuous stream of observations from the same card. The paper proposes a methodology to enrich fare validation data in order to generate information that is hard to obtain with traditional travel surveys.

Methodology/approach — The methodology aims to synthesize individual-level attributes by summarizing multi-day validation records from each card. These new dimensions are then transposed to various levels of aggregation and studied simultaneously in multivariate analysis. The methodology can also be applied to synthesize other multi-day attributes and is transferable to other modes and other travel behaviour studies.

Findings — Results show that validation data can effectively be used to measure the distribution of travel patterns in time and space as well as the variation of those phenomena over time. The paper provides several examples based on millions of validation records from the metro sub-network of Montréal, along with interpretations and some practical implications.

Research limitations/implications — Limitations and bias regarding the data and the methodology as well as the strategies to handle them are discussed within the context of passive travel survey and travel behaviour studies.

Practical implications — Practitioners in transit planning, operations, marketing and modelling can benefit from studying the increasingly accessible and massive smart card datasets through a deeper understanding of multi-day travel patterns and behaviour of transit users.

Originality/value — This paper outlines a data modelling approach and simple-to-implement methodology which exploit the multi-day property of fare validation data from a smart card AFC. The concept of multi-day attributes is introduced. The analyses show that the approach is effective for extracting information on travel behaviour and its variation which would otherwise be hard to obtain through traditional travel surveys, opening up another dimension of this data source for practitioners and transport modellers alike.

Purpose: The purpose of this chapter is to review and critically evaluate robots, artificial intelligence and service automation (RAISA) applications in the restaurant industry to educate professors, graduate students, and industry professionals.

Design/methodology/approach: This chapter is a survey of applications of RAISA in restaurants. The chapter is based on the review of professional and peer-reviewed academic literature, and the industry insight section was prepared based on a 50-minute interview with Mr. Juan Higueros, Chief Operations Officer of Bear Robotics.

Findings: Various case studies presented in this chapter illustrate numerous possibilities for automation: from automating a specific function to complete automation of the front of the house (e.g., Eatsa) or back of the house (e.g., Spyce robotic kitchen). The restaurant industry has already adopted chatbots; voice-activated and biometric technologies; robots as hosts, food runners, chefs, and bartenders; tableside ordering; conveyors; and robotic food delivery.

Practical implications: The chapter presents professors and students with a detailed overview of RAISA in the restaurant industry that will be useful for educational and research purposes. Restaurant owners and managers may also benefit from reading this chapter as they will learn about the current state of technology and opportunities for RAISA implementation.

Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.

Purpose — Automated fare collection systems implemented in public transportation systems in the last decade have provided a massive, continuous and low-cost source of reliable travel information. A direct and useful application of these data is the estimation of highly representative, although not bias-free, origin-destination (OD) matrices.

Methodology/approach — We discuss several issues with current OD matrix estimation methodologies, such as fare evasion and group travel, and their derived biases, specifically focusing on the Santiago (Chile) case. We also propose and apply two methods of validation: endogenous and exogenous validation. We elaborate on some methodological improvements that could be implemented to upgrade the activity estimation mechanics.

Findings — Several sources of bias in the estimation of OD matrix estimation from passive data are pointed and some solutions proposed. We apply improvements to existing methodologies and increase the success rate of trip estimations.

Practical implications — The reliable estimation of public transport OD matrices from passive data results in a valuable planning tool for both transit authorities and operators, much more representative and with less errors and biases that conventional data collecting techniques.

Originality/value of paper — This paper is one of the first works to deal with the subject.

There is a conflation of Mexican origin with the category “undocumented immigrant” that targets and stigmatizes undocumented Mexicans – I call this Mexican illegality stigma. I assess whether Mexican illegality stigma negatively affects the psychological well-being of Mexican-origin individuals in the US, distinguishing between undocumented Mexicans and citizen Mexican Americans. I draw from the stress process model and 52 in-depth interviews – 30 with undocumented young adults from Mexico and 22 with US-born young adults of Mexican descent – to evaluate how undocumented Mexicans and citizen Mexican Americans experience Mexican illegality stigma and to determine whether it affects the psychological well-being of undocumented Mexicans in a distinct manner. I found that all respondents experienced social rejection and discrimination when they were assumed or perceived as undocumented Mexicans. While few of the US-born respondents were affected by these incidents, most undocumented young adults found these incidents stressful because they were humiliating, excluded them from valuable resources and opportunities, and forced them to incur financial burden (e.g., unfair fines), which disrupted their transition to adulthood processes such as parenthood and labor market advancement. This study found evidence that Mexican illegality stigma is a stressor and source of distress for undocumented young adults from Mexico. As opposition to undocumented immigration from Mexico intensifies, the hostile context may further strain the psychological well-being of undocumented Mexicans.

This book provides an international perspective on improving information to support transportation decision making. It comprises a selection of papers plus workshop syntheses from the 9th International Conference on Transport Survey Methods in Chile in November 2011. The conference was organized into 14 workshops with both paper presentations and discussions in the workshops forming the majority of the conference activity. The papers reported primarily on research pertaining to continuous improvement in transport survey methods — the backbone of the transportation data pipeline in most countries. But some papers also addressed the new ways in which innovation — notably technological innovation — is being applied to the capture and analysis of data to produce necessary information faster, better, and less expensively. The conference program built on a rich legacy of intellectual pursuits spanning the past two decades, and it is anticipated that the conference will continue into the future. Thus, the contents of this book represent a 5–10 year view through a moving window on the international state of the practice and concerns in transport survey methods.

Purpose — To assess how cell phone technology might impact the collection of travel data in the future.

Design/methodology/approach — Two different types of cell phone enabled studies are considered. First, we examine how the text feature of phones can be used for person-to-person surveys, and second, we explore an aggregate level survey enabled by an anonymous and passive GPS trace.

Findings — This study explores the types of travel information that are likely to be inferred from text surveys and cell phone traces. It recognizes that a passive GPS trace might change the level of measurement and the inferences we make about travel behaviors.

Research limitations/implications — The study is prospective. It anticipates that over the next 10–15 years cell phone tracking technology will improve, as well as the speed and capability of algorithms for post-processing the information.

Practical implications — Cell phone enabled studies may provide a new tool and new level of measurement, as traditional survey response rates decline, and it becomes more difficult and expensive to conduct conventional travel surveys. The capacity of cell phones for travel survey work is improving, but it is not fully realizable today (2012).

Originality/value — This study provides a context to understand how the technology of the cell phone might be integrated with more traditional travel surveys to streamline data collection, and produce new types of spatial detection, measurement, and tracking.