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The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems.

The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice.

The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation.

This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.

The topic of parent migration and its effects on the family environment has become a focus of moral dilemmas in East Europe for the last three decades. Children have been portrayed as social orphans and parents working abroad as neglectful parents. Today, with more evidence from research and experience, the impact of parental migration is much more comprehensive and nuanced, recognising its noxious or even harmful but also possibly empowering effects. This chapter reflects on the involvement of left-behind adolescents as co-researchers in a study of transnational families. It acknowledges the agentic role of children (often automatically labelled as victims of neglect), amplifies their voices to inform existing data on the impact of parents' departure to work abroad and identifies directions for intervention that might strengthen families.

The research is an integral part of CASTLE – Children Left Behind by Labour Migration, an ongoing project (June 2021–December 2023). ¹ This chapter presents the research collaboration experience with 12 co-researcher adolescents with previous left-behind experiences, originating from Moldova and currently residing in Romania. The co-researchers participated in all stages of the research process: training, design of data collection, recruitment of research participants, data analysis and dissemination of results. Taking co-researcher roles had an empowering effect on adolescents, who learnt how to express their views on the topic, voiced their experiences about the emotional costs of being left behind by their parents and reflected on sensitive issues like separation of family members and violence in the family.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

This study aims to facilitate consistency of information in building information modelling (BIM) and address the current BIM gaps through the perspectives of the productization concept and product structure (PS).

The study follows a conceptual research approach in conjunction with a single case study. First, the previous studies on BIM implementation, productization and PS are reviewed. Further, a case study is used to analyse the current state of productization in the construction sector and develop a functional PS for construction.

A Part-Phase-Elements Matrix is proposed as a construction-specific PS to facilitate consistency in information and to enhance BIM. The proposed matrix provides new avenues to facilitate consistent information exchange through the interconnection between conceptual PS and standard building objects library, and encourage collaborative communication between stakeholders.

This study explores the core of the productization concept and PS as means to facilitate consistency of information and thus address the current gaps in BIM. This as building projects progressively move towards systematic modular and prefabricated construction where the flow of reliable information about product and construction offerings becomes increasingly important.

The purpose of this article is to identify design guidelines for online resources based on the subjective assessment criteria used by individuals to assess and process information resources. This method of creating design guidelines targeted at precise user groups has the potential to aid designers and developers to create more user-centred information resources.

The authors gathered data using a prospective longitudinal study investigating the information behaviour of expectant and new mothers. Women were asked to report on their information-seeking activities in a series of semi-structured interviews covering pregnancy and early motherhood.

This research identified 15 assessment criteria that were utilised by women to assess and process information resources. The most popular resource criteria amongst participants were credibility and convenience, while completeness and relevance were the most popular information content criteria. The authors found that assessment criteria were not considered in isolation, with criteria such as formatting and search engine ranking impacting on participants' perception of other criteria.

This research demonstrates the potential of linking a user groups subjective assessment criterion to design guidelines. The authors propose that these guidelines could be used to help design an online information resource. They could also be used to assess if an existing online resource met the needs of a user group. The methodology used in this study could be leveraged to create design guidelines for user groups.

This research uses subjective assessment criteria as a means of understanding how expectant new mothers process information resources. People use subjective judgements when processing information resources, and this should be incorporated into the design of information resources. Analysing longitudinal data allowed the authors to build a rich picture of how participants evaluated and compared different information resources.

Applying three psychological theories, this study aims to attempt to investigate the role of consumer psychology, specifically the factors of trust in vaccination, threat severity, fear, anxiety, risk and hygiene, and safety, on intention to resume hotel consumption. The authors also tested the mediation effect of anxiety among psychological constructs: perceived threat, fear and risk with the intention to resume hotel consumption.

Using purposive sampling, data were collected from 470 respondents from four cities in Malaysia and analysed by applying analysis of moment structures (AMOS) structural equation model technique. The respondents for this study were frequent travellers meaning the leisure tourists who at least travel twice a year or travel when getting the occasion to explore new things. In this study, an online survey was employed to ensure easy accessibility and to enhance the number of replies.

The results of this study confirmed that perceived severity, risk and fear influence travellers' anxiety. This study further confirms that trust in vaccination and hygiene & safety provided by the hotelier reduces anxiety levels. Anxiety is found one of the most important predictors of intention to resume hotel consumption, which further mediates the relationship between other psychological variables: perceived severity, risk, fear and intention to resume hotel consumption. Anxiety mediates the relationship between perceived severity, fear and intention to resume hotel consumption and partially mediates the association between risk and intention to resume hotel consumption.

This study examined three psychological theories and extended them by including the trust in vaccination and the hygiene and safety constructs. Anxiety was investigated as a mediator.

Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction.

Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes.

It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios.

This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

Steer-by-wire (SBW) system mainly relies on sensors, controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions. However, the sensors in the SBW system are particularly vulnerable to external influences, which can cause systemic faults, leading to poor steering performance and even system instability. Therefore, this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure.

This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer, fault estimator, fault reconstructor. Firstly, the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output. And then judge whether the SBW system fails according to the residual. Secondly, dependent on the residual obtained by the fault observer, a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor. Eventually, a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor.

The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs. Moreover, the estimation accuracy of the fault estimator can reach to 98%, and the fault reconstructor can make the faulty SBW system to retain the steering characteristics, comparing to those of the fault-free SBW system. In addition, it was verified for the feasibility and effectiveness of the proposed control framework.

As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink, the subsequent hardware in the loop test is needed for further verification.

Aiming at the SBW system with parameter perturbation and sensors failure, this paper proposes an active fault-tolerant control framework, which integrates fault observer, fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system.

Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).

The ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.

The analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.

The analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.

The novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.