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Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

The purpose of this paper is to raise awareness of the mobilities paradigm by exploring the role of tourist mobilities in destination marketing. This is important as studies that explore the impact of modes of transport on the development of destinations, or compare the transportation experience with the destination experience are lacking.

The study uses the context of the Jacobite steam train, which runs in the Scottish Highlands. It draws on multiple qualitative methods including participant observation, interviews and netnography.

The study explores the spatial, temporal and social mobilities associated with the journey and the destination, reveals how a rail journey becomes a “destination-in-motion” and, in turn, transforms what might otherwise be a neglected destination.

The study demonstrates how modes of transport that offer rich embodied experiences to visitors can present an important differentiation strategy and become core to a destination’s product and service portfolio.

By approaching destination marketing from a mobilities perspective, this paper recognises the significance of human and objects mobility to tourist experiences and offers a new perspective to existing research which biases a geographically bounded understanding of destinations.

This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.

A database comprising over 140,000 hotels with more than 30 rooms was created. The exploratory spatial–temporal data analysis (ESTDA) method, based on space–time cube model, was used to explore and visualize the spatial–temporal pattern of hotels.

The Chinese hotel industry can be divided into two development stages, namely, a large hotel-dominant stage before 2000 and a small–medium-sized hotel-dominant stage after 2000. China’s prefecture-level cities were clustered into four tiers. The higher the tier, the earlier the city will initiate hotel development. The Chinese hotel industry has four continuous hotspots (the Yangtze River Delta, Pearl River Delta, Bohai Rim and Sichuan and Chongqing) and some temporary hotspots.

This study lacks quantitative investigation, which could show the underlying mechanism of the evolution of the Chinese hotel industry.

This study is the first to investigate China’s hotel evolution over 40 years by applying big data and the ESTDA method. The systematic and evolutionary exploration will enable hotel researchers to understand the spatial–temporal nature of hotel distribution better. Introducing the ESTDA method into tourism and hotel research also provides an additional tool to researchers. Hotel investors and operators, city and tourism planners and market regulators can learn from the evolution of location patterns to make better where and when decisions.

On 24 January 2020, France informed WHO of three cases of novel coronavirus, all of whom had travelled from Wuhan, China. These three cases were the first confirmed cases in Europe. By 13 March 2020, Europe had become the epicentre of the pandemic with more reported cases and deaths than the rest of the world combined, apart from the People’s Republic of China. Many European countries like Italy, France and Germany took drastic actions and subsequently announced a lockdown, while other countries like the UK, the Netherlands and Sweden were much more hesitant to introduce such far-reaching safety measures. This chapter provides a literature overview of how the variation in such measures in Europe has ultimately resulted in changes in daily activities and travel behaviour during the pandemic. It focusses on five main themes: (i) reduction in mobility and activities, (ii) spatial-temporal adjustments in out-of-home activities in which people still participated, (iii) modal adjustments especially among people who used to travel by public transport before the pandemic, (iv) new out-of-home activities including new outdoor activities and (v) digital adaptations as several out-of-home activities were replaced by digital activities, with special attention to the experience of teleworking.

Physical gatherings at social events have been found as one of the main causes of COVID-19 transmission all over the world. Smartphone has been used for contact tracing by exchanging messages through Bluetooth signals. However, recent confirmed cases found in venues indicated that indirect transmission of the causative virus occurred, resulting from virus contamination of common objects, virus aerosolization in a confined space or spread from inadequate ventilation environment with no indication of human direct or close contact observed.

This paper presents a novel cyber-physical architecture for spatial temporal analytics (iGather for short). Locations with time windows are modeled as digital chromosomes in cyberspace to represent human activity instances in the physical world.

Results show that the high spatial temporal correlated but indirect tracing can be realized through the deployment of physical hardware and spatial temporal analytics including mobility and traceability analytics. iGather is tested and verified in different spatial temporal correlated cases. From a management perspective of mobilizing social capacity, the venue plays not only a promotion role in boosting the utilization rates but also a supervision-assisted role for keeping the venue in a safe and healthy situation.

This research is of particular significance when physical distancing measures are being relaxed with situations gradually become contained. iGather is able to help the general public to ease open questions: Is a venue safe enough? Is there anyone at a gathering at risk? What should one do when someone gets infected without raising privacy issues?

This study contributes to the existing literature by cyber-physical spatial temporal analytics to trace COVID-19 indirect contacts through digital chromosome, a representation of digital twin technology. Also, the authors have proposed a venue-oriented management perspective to resolve privacy-preserving and unitization rate concerns.

Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.

This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.

Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.

A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.

The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.

This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.

Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.

Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.

Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

In Mexico, wine tourism has become a relevant issue in the past 20 years. Research in this region is in a nascent stage and largely focused on the supply side. Nevertheless, consumer behavior research on wine tourists of the region is needed to improve the wine region positioning. Therefore, the purpose of this paper is to find out the predictors for revisit intention (RI) to the Valle de Guadalupe wine route.

The information was obtained by applying an exit poll survey to a sample of 422 wine tourists at the micro, small and medium wineries in Ensenada, Mexico. The spatial-temporal model was used to predict the wine tourist RI. Three dimensions were used: pre-visit, in situ experience and travel to/from. Multiple linear regressions were carried out to assess the relation between the three dimensions and RI.

The results obtained show that the pre-visit and in situ dimensions have an effect on RI to the wine route.

The generalization of the results may be limited due to fact that only the repeated visitors of the autumn season are included; therefore, it is not applicable to summer (high season of wine tourism) and first-time visitors.

The results provide implications for the owners of the micro, small and medium wineries seeking to improve the experience and increasing the tourist RI to the wine route.

The theoretical added value of this paper is its contribution to the body of knowledge about the wine tourism spatial-temporal model, evaluating the complete wine tourism experience to predict RI.