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The Michelangelo effect is a phenomenon that shows a reduction in perceived effort and an improvement in performance among both healthy subjects and patients when completing a motor task related to artistic stimuli, compared to performing the same task with non-artistic stimuli. It could contribute to the efficacy of art therapy in neurorehabilitation. In this study, the possible occurrence of this effect was tested in a cognitive task by asking 15 healthy subjects and 17 patients with a history of stroke to solve a digital version of the classical memory card game. Three different types of images were used in a randomized order: French cards, artistic portraits, and photos of famous people (to compensate for the possible effects of face recognition). Healthy subjects were involved to test the usability and the load demand of the developed system, reporting no statistically significant differences among the three sessions (p > 0.05). Conversely, patients had a better performance in terms of time (p = 0.014) and the number of attempts (p = 0.007) needed to complete the task in the presence of artistic stimuli, accompanied by a reduction in the perceived effort (p = 0.033). Furthermore, artistic stimuli, with respect to the other two types of images, seemed more associated with visuospatial control than linguistic functions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The behavior of the grain with a specific shape in the flotation process. • The effect of particle size and shape properties on the recovery of valuable metals. • Separation of plastics is easy and applicable with the frother before metal flotation stages. • The coarse and platy particles are mostly observed in the float product. • The sink product has mainly locked and rounder particles. Since Random Access Memory (RAM), one of the main parts of computers contains a remarkable quantity of precious metals, applying flotation at the pre-concentration stage to recycle these metals can result in a more cost-effective, user-friendly, and environmentally friendly process compared to direct chemical methods. While the significance of physical characteristics like particle size and shape in the flotation process is well established, the impact of particle shape in the flotation process utilized in the recycling of end-of-life (EoL) RAMs hasn't yet been thoroughly investigated. To fill this gap, a two-stage coarse flotation approach is used for the selective recovery of plastic and valuable metallic particles for sustainable development. The particle geometry of metallic particles recovered by flotation was characterized by axis measurement on the images by optical microscope that allows us to distinguish particles of different sizes and colors that make up the sample and evaluated in terms of particle size distribution (PSD), elongation (E) and roundness (R) parameters. The results showed that after the plastic fraction is effectively removed, it is possible to produce pre-concentrated products with high metal content (more than 50 % Cu content at the 1st stage and 1800 g/t Au content at the 2nd stage using 900 g/t KAX) in an economical and environmentally friendly way. Thus, it was concluded that the gold and copper metallic particles in the reduced-size EoL RAM cards could be easily floated by attaching them to the air bubble with the help of the collector, thanks to their flat shape. [ABSTRACT FROM AUTHOR]

Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them. This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concept drift, and verification latency problems using machine learning and deep learning. It also provides valuable information for academic and industrial researchers and opens new avenues for research aimed at developing robust fraud detection systems. [ABSTRACT FROM AUTHOR]

Nowadays, smart card and password-based user authentication and key agreement schemes have become an integral mechanism to ensure only legitimate users get access to the data of wireless sensor networks (WSNs). In the recent past, lots of password-based multi-factor user authentication schemes have been developed by the researchers. However, most of them are not secure against various known attacks. Recently, two user authentication and key agreement schemes for WSNs are suggested by Chen and Chen (J Supercomput 77(12):13653–13675, 2021) and Qi and Chen (J Supercomput 77(12):13897–13910, 2021). They claim that their schemes are secure against all known attacks. We analyze the security of these schemes and demonstrate that these schemes are not secure against some common attacks (such as smart card lost attack) and do not provide common security features such as user untraceability and forward secrecy. By keeping all the merits of these schemes, we suggest a new provably secure user authentication and key agreement scheme using physically unclonable functions and elliptic curve cryptography. To verify the security and authentication properties of the suggested scheme, we use the well-known Proverif tool for formal security verification. We further prove its security informally. Finally, performance comparison with some recent schemes in terms of security features and computational cost is performed. Performance comparison results show the superiority of the suggested scheme over the existing schemes. [ABSTRACT FROM AUTHOR]

The emerging cloud infrastructure has escalated number of servers offering flexible and diverse remote services through public channels. However, user authentication in conventional single-server architecture necessitates multiple smart cards maintenance and passwords memorization to access different cloud servers. To address this limitation, researchers devised authentication protocols for multi-server architecture that offers scalable platform wherein users can access multiple servers with single registration. The multi-factor authentication protocols leverage biometric keys to bind users' physical characteristics with their identity, offering higher security than two-factor authentication protocols. However, the existing protocols for multi-server architecture are prone to replay, user impersonation, denial of service, server spoofing attacks and lack security functionalities such as user anonymity and untraceability, backward and forward secrecy, and session key security. Moreover, the incorporation of registration center (RC) to authenticate each pair of user-server in multi-server architecture can lead to computational bottleneck and single-point failure issues on RC. To overcome these security loopholes, we design a novel provably secure multi-factor elliptic curve cryptography (ECC) based authentication protocol for multi-server architecture with offline RC for cloud environment. The formal security analysis under widely accepted real-or-random (ROR) model and informal security analysis of proposed protocol demonstrate provision of security functionalities and resilience against potential security attacks. Furthermore, we adopt Scyther security verification tool to verify our protocol's correctness and security properties. The performance evaluation demonstrates that our protocol offers robust security functionalities with reasonable communication and computation overheads than state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

In the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities. [ABSTRACT FROM AUTHOR]

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand. [ABSTRACT FROM AUTHOR]

Blockchain technology has limitations in terms of scalability and throughput of transactions compared to other payment methods such as VISA and smart cards. But, Off-chain payment solves this issue by performing micro-transactions without communicating with the ledger. Similarly, Ethereum uses smart contracts to complete Off-chain transactions by constructing state and virtual channels. In this model, we develop an n-party payment system that uses state and virtual channels. The proposed model simultaneously executes different contract instances while they belong to other independent channels. Again, a virtual channel having n-intermediaries takes linear time to resolve payment channel disputes by transferring to the Blockchain. We introduce a global contract GSCC that solves the disputes in constant time in the worst case without including Blockchain. The dispute-solving process does not affect any intermediary node and ensures the intermediary is not losing coins. GSCC stores all the prior information of the active participants. But, GSCC has no communication overhead to other parties and ensures the execution of disputes with total conflict amount. The resultant model guarantees the execution of every contract, fair dispute resolution, and security balance. Our proposed theorems and zero-knowledge proof ensure the security of the n-party model. [ABSTRACT FROM AUTHOR]

During the COVID‐19 pandemic, the United States released the CDC‐issued vaccination card to document the status of immunizations. It eventually was used as a verification system to allow patrons to safely access public venues. This is/was apparently a simple printed piece of paper with minimal security features which made it an easy target for counterfeiters with little expertise required. Those unvaccinated who wished to enter areas that required proof of the COVID‐19 vaccine were soon finding alternative illegal methods to do so which led to public health concerns. This research was divided into two parts. Part A aimed to forensically identify the security features with a video spectral comparator and a stereomicroscope. Ten "known" blank cards were collected directly from dispensaries that administered the vaccine and 68 "unknowns" from individuals who received the COVID‐19 vaccine. Part B aimed to evaluate the authentication systems employed by countries outside the United States to propose a model on how the vaccination card could be improved and what security features should be included for future instances requiring proof of immunization. The results revealed the CDC document was produced with minimal security features making the document prone to counterfeiting. Furthermore, based on the information collected from other countries' vaccination certificates, it is recommended to develop a dual authentication system, with digital and printable aspects, that contains a QR code linked to a database. Other security features may include a non‐optical brightener substrate, security fibers, a watermarked logo, offset printing, and a geographical color‐coded system. [ABSTRACT FROM AUTHOR]

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), protocols with authentication and key agreement functions can enhance the security of the interaction between users and sensor nodes, guaranteeing the security of user access and sensor node information. Existing schemes have various security vulnerabilities and are susceptible to security attacks (e.g., masquerading user, password guessing, internal privilege, and MITT attacks), so they cannot meet the anonymity requirements or achieve forward security. To effectively improve the security performance of WSNs, an elliptic curve cryptography (ECC)-based three-factor authentication and key agreement scheme for WSNs is proposed. The scheme is based on the ECC protocol and combines biometrics, smart card and password authentication technology; uses a challenge/response mechanism to complete the authentication between users, gateways, and sensors; and negotiates a secure session key. The Burrows, Abadi and Needham logic for formal security analysis proves the correctness and security of the scheme, and the informal analysis of multiple known attacks proves that the scheme can resist various attacks and has high security characteristics. The feasibility of the scheme has been analysed and verified with the ProVerif tool. The efficiency analysis results show that the scheme is suitable for resource-constrained WSNs. [ABSTRACT FROM AUTHOR]

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020–22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative. [ABSTRACT FROM AUTHOR]

To support more complex and robust online services, enterprise-class applications prefer to interconnect multiple servers as the pedestal to enhance the system's interoperability. However, the multiserver architecture always struggles to reconcile the trade-off between convenience and security, leaving users exposed to a variety of network attack threats. Existing security authentication schemes based on the Chebyshev Chaotic Map for multiserver architectures cannot provide three-factor (including password, biometric feature, and smart card) security. Therefore, we propose a novel Physical-Unclonable-Function-based Lightweight Three-Factor Authentication (PUF-LTA) scheme, which can achieve three-factor security. The PUF-LTA scheme mainly includes two components: (1) PUF-assisted registration and (2) lightweight mutual authentication with one-time interaction. During the PUF-assisted registration process, to defend against side-channel attacks on smart cards, the login credentials of users are XORed with the unique identifier generated by the PUF so that the adversary cannot obtain these secret login credentials. During the lightweight mutual authentication process, we combine the Chebyshev polynomial map and symmetric encryption/decryption to negotiate the session key between users and servers, which only needs one interaction. The security performance of PUF-LTA is theoretically proved by leveraging the random oracle model. In contrast with relevant multiserver authentication schemes, PUF-LTA is more efficient and suitable for resource-constrained multiserver environments because it can ensure secure three-factor authentication and support flexible biometrics and password updates with less computation cost. [ABSTRACT FROM AUTHOR]

A reasonable method for estimating alighting stops can provide a data guarantee for schedule design and vehicle scheduling. However, few studies, to our best knowledge, have addressed this problem using the variable distance threshold for different types of passengers. This paper is based on available smart card data, and a two-stage algorithm is designed to obtain missing alighting stations in the entry-only urban public transportation system. This algorithm divides passengers into seven types, determines varying distance thresholds for different passengers, and includes five assumptions. Based on the first three assumptions, the corresponding subalgorithms are designed, respectively, constituting the first-stage algorithm, and the first match for all passengers' alighting stations is carried out. The second-stage algorithm is designed on the basis of the latter two assumptions. A secondary match is conducted on data that were not successfully matched in the first stage to ensure that alighting stops for all passengers are identified. The proposed methods are verified using urban public transport data from the city of Zhuhai as an example. Research results show that the drop-off stops where approximately 74% of passengers can be determined after matching the first-stage algorithm. The distance thresholds for various passengers are different, and the distance thresholds for the same type of passengers over different days are also different. Compared with the traditional fixed distance threshold, the variable distance threshold for various passengers proposed in this paper can improve the match rate of alighting station estimate, about 9%. By appropriately extending the distance thresholds for different passengers, the match rate for passengers who work or live in the suburbs can be further improved, which is in line with the actual travel status of such passengers. The second-stage algorithm can identify alighting sites for a single or unlinked trip. The efficiency of the methodology proposed in this study can meet the needs for practical applications, which may be applied to urban public transport systems. [ABSTRACT FROM AUTHOR]

Ohmic heating is an emerging technology currently in high demand for application in various processes. In this research, a prototype of a multipurpose ohmic heating apparatus was successfully designed, fabricated, and tested. This apparatus was designed based on low-cost and versatile sensors and components available worldwide. Three independent chambers could be operated parallelly and individually with different treatments. Parameter data, i.e., voltage, electrical current, the temperature of heated material and environmental humidity-temperature, could be recorded by an embedded data logging system. The sensor had been tested and validated by comparing all the sensors used with commercial standard instruments. The result showed that all sensors had high measurement accuracy, indicated by very low mean absolute error (MAE) and mean absolute percentage error (MAPE), with R2 > 0.999. The performance test revealed that product temperature could be accurately maintained according to the set point temperature with a deviation value lower than 0.1 °C. The data logging system was able to record and store the parameter data in SD card memory for up to several days without interruption. The prototype of the ohmic heating apparatus could be an effective alternative to process many purposes such as pasteurisation, cooking, warming, and fermentation based on the ohmic heating principle. [ABSTRACT FROM AUTHOR]

In recent years, flexible and wearable electronics such as smart cards, smart fabrics, bio‐sensors, soft robotics, and internet‐linked electronics have impacted our lives. In order to meet the requirements of more flexible and adaptable paradigm shifts, wearable products may need to be seamlessly integrated. A great deal of effort has been made in the last two decades to develop flexible lithium‐ion batteries (FLIBs). The selection of suitable flexible materials is important for the development of flexible electrolytes self‐supported and supported electrodes. This review is focused on the critical discussion of the factors that evaluate the flexibility of the materials and their potential path toward achieving the FLIBs. Following this analysis, we present how to evaluate the flexibility of the battery materials and FLIBs. We describe the chemistry of carbon‐based materials, covalent‐organic frameworks (COFs), metal‐organic frameworks (MOFs), and MXene‐based materials and their flexible cell design that represented excellent electrochemical performances during bending. Furthermore, the application of state‐of‐the‐art solid polymer and solid electrolytes to accelerate the development of FLIBs is introduced. Analyzing the contributions and developments of different countries has also been highlighted in the past decade. In addition, the prospects and potential of flexible materials and their engineering are also discussed, providing the roadmap for further developments in this fast‐evolving field of FLIB research. [ABSTRACT FROM AUTHOR]

Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research. [ABSTRACT FROM AUTHOR]

A Two-Factor Authentication (2FA) scheme can authenticate a client if the client is able to provide the possession factor (like biometric feature, smart card) and the knowledge factor (like password, secret key) simultaneously. With only one factor, it is hard for an adversary to impersonate the client to pass the authentication, and thus 2FA provides better security than single-factor authentication schemes. However, as far as we know, all existing 2FA schemes do not consider the leakage of server's database, and their authenticity may fail when the database is also compromised (in addition to one factor). Considering numerous reports of database leakage in the real world, it seems imminent to study and design 2FA schemes resilient to database leakage. In this paper, we formalize security models for 2FA schemes by taking database leakage into account. Our security models consider malicious adversaries who can obtain both the client's one authentication factor and the server's database, and have two requirements, authenticity and zero-knowledge. Authenticity ensures that such malicious adversaries cannot impersonate the client to pass the authentication, while zero-knowledge guarantees that such malicious adversaries obtain no information about the client's the other factor. Zero-knowledge is especially important for biometric features (like faces, fingerprints), which are inherent to human beings and can hardly be changed. Then we propose a biometric-based 2FA scheme with biometric feature and secret key serving as the two authentication factors. Our 2FA scheme has three rounds, and we prove its authenticity and zero-knowledge under database leakage in the random oracle model. Notably, our construction makes a novel use of a recent technical advance called robust property-preserving hashing (Boyle et al., ITCS 2019) together with fully homomorphic encryption, to recognize or discern clients by their biometric samplings in a homomorphic and secure way. [ABSTRACT FROM AUTHOR]

Nightlife attractiveness plays a significant role in the local economy as it contributes to various sectors such as hospitality, entertainment, and tourism. Millennials and Generation Z (Gen MZ) are the primary leaders of nightlife activities. This study aims to evaluate the nightlife attractiveness of Seoul focusing on Gen MZ all-nighters. Specifically, the nightlife attractiveness is evaluated based on data envelopment analysis (DEA) using smart card data and open data sources from Seoul. Nightlife attractiveness is defined as the number of Gen MZ floating population and their duration of stay, compared to the number of nightlife venues, i.e., bars, clubs, restaurants, karaokes, and cafes. The DEA model and Tobit regression model are used to evaluate the nightlife attractiveness of 161 Dong unit areas in Seoul and explore Gen MZ's preference for hotspots, respectively. The results show that, on average, the attractiveness score is estimated to be 0.87, suggesting a 0.13 increase in outputs to be the most attractive hotspot. Regarding individual preferences, Gen MZ all-nighters residing in rich suburban areas tend to spend their all-night at these hotspots. These findings provide insights into existing hotspots and areas with the potential to become hotspots in the future. • The nightlife attractiveness score of Seoul was estimated to be 0.87, on average. • Areas with a high Gen MZ population and long durations had high attractiveness scores. • Gen MZ, who resides in areas with good transportation, prefer attractive hotspots despite long travel times and distances. • The nightlife score provides insights into both existing hotspots and areas with the potential to become attractive areas. [ABSTRACT FROM AUTHOR]

Smart chip cards are a promising material in retrospective dosimetry due to their high collectability with low replacement cost. In this study, dose reconstruction using OSL at elevated temperatures for various chip cards was investigated. The approximate spectral emission of the chip cards was determined through a comparison of radiation-induced and intrinsic background TL signals using different detection filters. In the OSL decay curve, a fast component (0.0–0.4 s) was integrated to avoid high intrinsic background signals. To minimize sensitivity changes due to heat treatment, a protocol to measure OSL at 100 °C was developed and the data were compared with those from a similar protocol for OSL measured at room temperature. In the 0.1 - 5 Gy range, linear and power functions were fitted for room temperature and 100 °C OSL protocols, respectively. The minimum detectable dose was evaluated immediately after exposure, with values of 25 mGy for room temperature and 240 mGy for 100 °C OSL being determined. Signal fading was measured up to 30 days after irradiation for the investigated protocols. A dose recovery test was made 10 days after irradiation using various chip cards, and the 100 °C OSL protocol produced more reliable results than the room temperature OSL protocol. As a result, the study emphasizes the necessity of further investigation of OSL at high temperatures using chip cards. • Dose reconstruction using OSL at elevated temperatures for various smart chip cards was investigated. • The correction factors such as sensitivity change, zero dose, and signal stability were estimated. • A dose recovery test showed the necessity of further research into OSL at elevated temperature. [ABSTRACT FROM AUTHOR]

Service bottlenecks are a key barrier to building a resilient public transport system. In this paper, we propose a new approach to automatically extract the role of a station in dynamical public transport flow networks based on the emerging role discovery method in network science. The term "role" in this study refers to the distinctive position or function that a station plays within the public transport flow network. Using smart card data from Nanjing public bikesharing agencies, we first construct dynamical public transport flow networks with notions of dynamical graph and edge. We then develop a dynamical algorithm to recursively compute the structural flow characteristics of nodes in passenger flow networks. Non-negative Matrix Factorization is conducted to extract the role memberships from the derived structural feature matrix and interpret each role in terms of measurements with practical values. The network hubs and potential service bottlenecks are then identified based on their operating characteristics and dynamics. Furthermore, the day-to-day and within-day role dynamics of public transport stations over time are unveiled. The results contribute to a better understanding of the interplay between stations in the network, and the identification of roles provides insight for public transport agencies to improve service resilience. [ABSTRACT FROM AUTHOR]

As population aging has been an issue worldwide, the mobility of older people have attracted the attention of scholars from urban planning, transport geography, and social science. However, few have investigated socio-spatial differentiation among mobility groups, considering their daily needs and activity spaces. To fill this research gap, we conducted a comparative analysis of socio-spatial differentiation, based on individual activity spaces. We used smart card data from Kunming, China, to identify selected individuals' residential locations and travel patterns, and evaluate their accessed activity space. We performed a disaggregated analysis of the individual activity space, and then aggregated the activity counts on each grid. This study found that the residential locations of older metro travelers are significantly different from those of other metro travelers. In addition, socio-spatial differentiation was found to exist due to different daily requirements. The results were confirmed in three LASSO models with built environment variables. These findings are useful in urban and transportation planning to improve elder-friendly services. • We propose an analytical framework to aggregate individual trip records to study socio-spatial differentiation. • We perform a disaggregated analysis of individual activity space, and then design an aggregated study of individual activity. • Socio-spatial differentiation is identified according to spatial heterogeneity of residential locations and activity space. [ABSTRACT FROM AUTHOR]

Travel activities influence individual travel location, time, and frequencies. Understanding individual travel regularity under different activities is crucial for individual mobility prediction and urban facilities planning. Existing studies need further improvement as they have not adequately taken into account the impact of activity factors on individual travel patterns. In this paper, we propose an innovative framework for exploring travel regularity and the correlation with urban region attributes at the individual level. The framework's novelty and uniqueness lie in its three layers: (i) A trip pre-processing layer for extracting every complete public transit trip and trip purposes (e.g. commuting activity and non-commuting activity) by combining individual's travel characteristics and public transport network. (ii) An entropy-based travel regularity measurement and K-means based multiple-view clustering layer to assess the extent of individual travel repetition over time for various travel activity and investigate the similarities and differences among users. (iii) A region dependence correlation analysis layer for exploring the correlation between individual travel regularity and regions attributes of two key locations: home and workplace More importantly, by employing the framework, we gained empirical insights based on a large-scale dataset (covering 0.64 million users) collected from public traffic smart cards. For instance, commuters can be categorized into four groups based on the regularity of their commuting activities: Regular Workers (24%), Flex-time Workers (21%), Overtime Workers (35%), and Other Workers (20%). The distribution of these four groups is associated with workplace's administrative characteristics, with a Pearson correlation of 0.503. In addition, individuals can also be classified into two groups based on their travel regularity of non-commuting activities: Limited Visitors (37%) and Active Explorers (63%). The distribution of these individuals is correlated with the coverage ratio of public transit facility in their home locations, showing a Pearson correlation of 0.604. • A framework exploring individual travel regularity under different purpose. • Entropy-Space K-means for travel activity-based individual classification. • Inter-personal and intra-personal variability of individual travel patterns. • Correlation between individual travel regularity and urban region attributes. [ABSTRACT FROM AUTHOR]

The disparity between rapid urbanization and limited service supplies has raised significant societal concerns, such as overcrowding, caused by a surfeit of individuals traveling at the same time. However, our understanding of how people decide the timing of their trips remains incomplete. Here we use anonymized smart card transaction data from mass transit railway (MTR) systems across three cities to study how commuters schedule travel time to arrive at their workplaces on time. We find two metrics—defined to scale commuters' time scheduling preferences by investigating relationships among MTR station entry, exit time and work start time—can well indicate arrival penalty risks (early arrival, late arrival, and no penalty), and is common among varying work start times across different cities. Additionally, we explore the varying attractiveness of origin–destination (OD) station pairs to commuters with a rank-flow approach and we develop a realistic determinant to measure the penalty risks with the time reserved for the last-mile trip. Our findings verify theoretical bottleneck models, aid in the understanding of distribution of commuting demand and land uses, and support policy making, such as flexible working-hour policies for peak demand managements. • Introduce two key trip metrics to scale commuters' time scheduling preferences. • Investigate the relationship among commuters' mass transit railway (MTR) station entry, exit and work start time. • Understand commuters' aversions to the arrival penalty risks. • Verify the theoretical bottleneck models with smart card transaction data. [ABSTRACT FROM AUTHOR]

Understanding differentiated services is pivotal for enhancing the appeal and diversity of the metro system, yet this facet has received relatively scant attention in existing literature. To bridge this research gap, our analysis delves into the business-class services offered by the metro and compares them with the common-class offerings. First, we illustrate the spatial and temporal patterns of business- and common-class ridership across stations and hours. Second, we construct two-stage geographically weighted regression models to identify key determinants and their spatiotemporally heterogeneous effects, focusing on land-use patterns, demographic considerations, and intermodal transfer modes. Leveraging one-week smart card data collected in Shenzhen from May 13th to 17th, 2019, our findings underscore the following aspects: (1) Spatial and temporal variations in business-class ridership across stations are linked to diverse land-use configurations. (2) Bus-metro transfers, business establishments, medical facilities, and the proportion of young commuters contribute significantly to the business-class ridership. (3) The emergence of business-class trip is weakly associated with bike-sharing activities but has a strong correlation with bus transfers. (4) Business establishments and medical facilities exhibit nuanced impacts on business-class travel, with excessive aggregation leading to unintended consequences. These insights offer valuable policy implications for fostering the development of business-class services in the metro systems of other Chinese cities. [ABSTRACT FROM AUTHOR]

Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy. [ABSTRACT FROM AUTHOR]