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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.

Purpose — The introduction of new technology to public transport systems has provided an excellent opportunity for passive data collection. In this paper, we explore the possibility of automatically generating level of service indicators that could be used for operation planning and monitoring of Transantiago, the public transport system of Santiago, Chile.

Design/methodology/approach — After basic processing of the raw automatic vehicle location (AVL) and automatic fare collection (AFC) data, we were able to generate bus speed indicators, travel time measurements and waiting time estimates using data from 1week. The results were compared with manual measures when available.

Findings — The advantage is that these measurements and estimates are reliable because they are obtained from large samples and at nearly no cost. Moreover, they can be applied to any set of data with a selected periodicity.

Research limitations — The scope of this research is limited to what can be observed with AVL and AFC data. Additional information is required to incorporate other dimensions, such as personal characteristics and/or more detail in the origin/destination (OD) of the trips.

Practical implications — Nevertheless, these results are valuable for the planning and operation management of public transport systems because they provide large amounts of information that is difficult and expensive to obtain from direct measurements.

Originality/value — This paper proposes tools to obtain valuable information at a low cost. These tools can be implemented in many cities that have certain technological devices incorporated into their public transport systems.

Psychometric analyses of self-administered questionnaire data tend to focus on items and instruments as a whole. The purpose of this paper is to investigate the functioning of the response scale and its impact on measurement precision. In terms of the response scale direction, existing evidence is mixed and inconclusive.

Three experiments are conducted to examine the functioning of response scales of different direction, ranging from agree to disagree versus from disagree to agree. The response scale direction effect is exemplified by two different latent constructs by applying the Rasch model for measurement.

The agree-to-disagree format generally performs better than the disagree-to-agree variant with spatial proximity between the statement and the agree-pole of the scale appearing to drive the effect. The difference is essentially related to the unit of measurement.

A careful investigation of the functioning of the response scale should be part of every psychometric assessment. The framework of Rasch measurement theory offers unique opportunities in this regard.

Besides content, validity and reliability, academics and practitioners utilising published measurement instruments are advised to consider any evidence on the response scale functioning that is available.

The study exemplifies the application of the Rasch model to assess measurement precision as a function of the design of the response scale. The methodology raises the awareness for the unit of measurement, which typically remains hidden.

Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level.

This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection.

The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning.

The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

The purpose of this paper is to validate the learning screening tool (LST) and the adapted functioning checklist-revised (AFC-R) as screening tools to aid programme allocation, and to investigate whether programme decision makers were using the tools as per the guidance provided by HMPPS Interventions Services.

LST and AFC-R data were gathered for 555 men who had been assessed for programmes between 2015 and 2018 across eight prisons and one probation area. WAIS-IV IQ data were also gathered if completed.

The findings provide support for the use of the LST, and AFC-R in helping to make decisions about programme allocation. The LST and AFC-R correlate well with each other, and a measure of intellectual functioning (WAIS-IV). Those who were allocated to learning disability or challenges (LDC) programmes scored higher on the LST (greater problems) and lower on the AFC-R (lower functioning) compared to those allocated to mainstream programmes. The LST had adequate predictive validity. In the majority of cases, the correct procedures were followed in terms of using the tools for programme allocation.

The sample size for examining the relationships between all three tools was limited. The research was also unable to take into consideration the clinical decision making involved in how the tools were interpreted.

This research contributes to the growing evidence about the effective use of LDC screening tools in forensic settings.

The purpose of this paper was to establish the psychometric properties of the Asian Family Characteristics Scale (AFCS) in the Thai population.

The 30-item AFCS originally developed in the Malay language was translated into Thai. Thai (n = 384) and Malay (n = 500) participants in Study 1 responded to the AFCS in their respective languages. The data were subjected to a confirmatory factor analysis with a measurement invariance test. In Study 2, Thai participants (n = 495) filled out the AFCS and Chulalongkorn Family Index, International Personality Item Pool-NEO, Self-Compassion Scale, Depression, Anxiety, and Stress Scale (DASS-21) and Satisfaction with Life Scale.

Study 1 showed that the measurement model of the Thai AFCS fit the data from the Thai population. The measurement invariance test confirmed that the structure and meaning of the AFCS are equivalent across the Thai and Malay samples. Study 2 demonstrated the AFCS's convergent validity by showing that the AFCS score had a positive correlation with the Chulalongkorn Family Inventory, self-compassion, agreeableness, conscientiousness, satisfaction with life and a negative correlation with neuroticism, depression, anxiety and stress. The AFCS's discriminant validity was supported by nonsignificant correlations with extraversion and openness to experience.

This paper is an attempt to develop a family characteristic measure specifically for the Asian population. The results provide empirical evidence for measurement invariance and validity of the scale in another Asian language, enhancing its cross-cultural generalizability.

China is experiencing tremendous changes of rapid urbanization and aging society. The development of age-friendly communities (AFCs) has been encouraged for improving health and well-being of older adults. Hence, this study aimed to deepen the understanding of AFCs in China and to investigate the integrated relationships between AFCs and the quality of life (QoL) of older adults, using a large-scale questionnaire survey.

A questionnaire survey was conducted in Hefei, China, to investigate the complicated relationships between the components of AFCs and the QoL of older adults. Ultimately, 1,383 valid questionnaires were collected from senior respondents aged more than 60 years. Several statistical methods, including reliability analysis, correlation analysis and structural equation modeling (SEM), were adopted to develop an integrated model for AFC components and the QoL of older adults.

Six AFC components and four older adults' QoL factors were identified. The SEM results revealed integrated relationships between specific AFC components and the QoL of older adults: (1) physical QoL was affected by outdoor spaces, public transportation, housing and community and health services; (2) psychological QoL was predicted by most of the AFC components except community and health services; and (3) environmental QoL and social QoL were both influenced by outdoor spaces, communication and information and community and health services.

In order to enhance the QoL of older adults, it is suggested that outdoor spaces need to be enlarged by fully using the facilities and playgrounds of middle schools and renovating the older buildings. The locations of public transportation stations are recommended to be revised to be within a 5-minute walking distance of senior residents. Improvements to the social environment of AFCs, by increasing the coverage of medical services and creating multiple approaches to recreational activities, are encouraged.

These findings have empirical significance for urban planners and policy-makers in regard to identifying major components of AFCs and understanding the effect of those components on the QoL of older adults.

During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.

First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.

The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.

First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

This research study aims to minimize autonomous flight cost and maximize autonomous flight performance of a slung load carrying rotary wing mini unmanned aerial vehicle (i.e. UAV) by stochastically optimizing autonomous flight control system (AFCS) parameters. For minimizing autonomous flight cost and maximizing autonomous flight performance, a stochastic design approach is benefitted over certain parameters (i.e. gains of longitudinal PID controller of a hierarchical autopilot system) meanwhile lower and upper constraints exist on these design parameters.

A rotary wing mini UAV is produced in drone Laboratory of Iskenderun Technical University. This rotary wing UAV has three blades main rotor, fuselage, landing gear and tail rotor. It is also able to carry slung loads. AFCS variables (i.e. gains of longitudinal PID controller of hierarchical autopilot system) are stochastically optimized to minimize autonomous flight cost capturing rise time, settling time and overshoot during longitudinal flight and to maximize autonomous flight performance. Found outcomes are applied during composing rotary wing mini UAV autonomous flight simulations.

By using stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads over previously mentioned gains longitudinal PID controller when there are lower and upper constraints on these variables, a high autonomous performance having rotary wing mini UAV is obtained.

Approval of Directorate General of Civil Aviation in Republic of Türkiye is essential for real-time rotary wing mini UAV autonomous flights.

Stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads is properly valuable for recovering autonomous flight performance cost of any rotary wing mini UAV.

Establishing a novel procedure for improving autonomous flight performance cost of a rotary wing mini UAV carrying slung loads and introducing a new process performing stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads meanwhile there exists upper and lower bounds on design variables.

The purpose of this paper is to provide a comprehensive initial evaluation of the Super Bowl Indicator (SBI) from 1966 to 2015.

The authors evaluate the predictive ability of the SBI over two different time periods on four stock market indexes. Also, the authors compare the SBI predictive ability with other alternative indicators based on Super Bowl results as well as that of the January barometer (JB). As a robustness check, the authors examine whether the JB can predict Super Bowl outcomes. The authors use Granger causality to reduce the threat of spurious correlation.

The SBI surpasses the competition in both time periods, but it is evident that its predictive powers have waned since 1989. The authors find that the pre-Super Bowl January performance of the New York Stock Exchange is an impressive predictor of whether a team from the original National Football Conference won the big game between 1967 and 1988. Also, for the 1989-2016 period, the authors observe that the JB is a significant predictor whether the pre-game favorite wins or loses.

This study makes several contributions to the literature. The authors examine the SBI against four market indexes (Dow Jones Industrial Average, Standard and Poor’s 500 Index, and NASDAQ) with raw and point spread-adjusted scores. Testing a corollary to the SBI, this study is the first to examine the influence of the JB (sometimes called the January effect) on Super Bowl results.