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This research paper proposes a hybrid quantum key distribution (QKD)-based framework for secure enterprise communication using post-quantum cryptography (PQC) and smart cards. The paper discusses the limitations of current PQC implementations and the need for a hybrid approach that combines classical non-PQC algorithms with quantum-resistant PQC algorithms to establish secure communication channels. The proposed framework utilizes point-to-point QKD links between quantum devices to establish a shared secret key, which is then used to encrypt and decrypt data using PQC algorithms. Smart cards are used for authentication and key synchronization to ensure the security of end-user devices. The paper also reviews relevant literature on QKD and PQC and discusses future work. The proposed framework offers a cost-effective and secure way for enterprises to communicate with each other. [ABSTRACT FROM AUTHOR]

Many travel surveys used in transportation planning still collect information over a single weekday while mobility and activity patterns fluctuate from day to day. However, the emergence of new continuous data sources has led to the generation of large amounts of longitudinal data. Therefore, this paper aims to value these two data types to cross-analyze the temporal variability of aggregated travel behaviors. A weighting method based on large-scale single-day survey samples was first developed to extend the representativeness of the daily trip diaries available in the three most recent Origin-Destination household travel surveys of Montreal, Canada, to the entire data collection period (four months in the fall). This method was then validated using passive data streams (smart card and count data) for three modes: car, subway, and bicycle. The variations in the daily use of these modes measured independently by the surveys and passive data were compared over several months of the fall period and throughout an average week. Indicators were also proposed to evaluate whether the two data sources capture the same trend and seasonality, especially the weekly rhythms. The results show that it is possible to accurately infer day-to-day variability in travel behavior from a large-scale cross-sectional single-day survey. Furthermore, this paper demonstrates the potential for complementarity between traditional surveys and emerging data (at least over the survey period), as well as the possibility to combine them since there are large similarities in the aggregated travel patterns they allow monitoring. [ABSTRACT FROM AUTHOR]

• Metacognitive potential of children with ADHD syndrome tends to be underestimated in education. • Four conceptions of mind were elicited in phenomenographic interviews analysis. • A metaphor of a mind-machine enabled expression of intuitive, idiosyncratic knowledge. • Children with ADHD are aware of their limitations: inability to inhibit impulsive reactions. • Characteristic overload reaction can lead to a symbolic "turning off" that is often described. The paper explores the concepts of mind in young learners with ADHD syndrome: How do they experience and conceptualize their own minds? To what extend are they able to explain the mental operations activated during learning? Which of these processes do they consider to be the most important in their learning? What are the educational consequences of such perception? The article consists of four parts. The first provides the reader with a short though comprehensive theoretical backgrounds, including a critical review of latest research on the subject. Second part describes the methodology of phenomenographic research conducted with 8-years old students suffering from ADHD syndrom (N = 25). The results of interviews (both on iconic and verbal expressions' levels) constitute the main body of the text. The subjects were asked to imagine their mind being an unusual machine, and then draw and explain how such mind-machine would work while learning. Four conceptions of mind were elicited and described: a/ mind as an untamed mystery; b/ as a memory card; c/ as a processing center, and d/ as a control panel. The results were discuss in terms of their limitations, the possibilities of extending the research on metacognitive potential of children suffering from ADHD syndrom, and consequences to pedagogical practice. The paper is addressed to educators, both scientific researchers and elementary teachers, to help them understand the way students with ADHD perceive and interpret their learning environment and one's own mind's potential. [ABSTRACT FROM AUTHOR]

Drawing on transit smart card data allied with local weather station records over a 12-month period, this paper takes Brisbane, Australia as the study context and examines the way in which weather impose influences on public transit passenger's travel behaviour. In terms of transit passenger's spatiotemporal origin–destination (OD) information, a new travel behaviour indicator called stickiness is developed to reflect passenger's travel similarity in using the transit service over a period. By applying a suite of regression models, it shows that weather's effects on transit passenger's travel behaviour vary by temporal period and passenger type. In general, weather is shown to exert a stronger effect on passenger's stickiness during midday off-peak hours in comparison with either AM peak or PM peak. Across all types of passengers, child passengers are found to be most tolerant to weather changes and even be stickier to their regular travel pattern during AM peak. The modelling results also reveal that when it relates to journey-to-work or journey-to-school, weather's effects on alighting location are much smaller than other three OD-related travel behaviour features (i.e., boarding time, boarding location, and alighting time). In contrast with the postponement during AM peak, transit passengers are prone to bring forward their trip back home during PM peak in response to poor weather conditions. Moreover, this paper verifies that weather parameters are not singly perceived by transit passengers but as an interrelated unity in shaping transit passenger's travel behaviour. By enriching the weather–travel behaviour scholarship, the empirical findings of this study are helpful to render a holistic understanding of the weather–travel behaviour relationship and have important implications for transit operators in building a more weather-resilient transit system. [ABSTRACT FROM AUTHOR]

Spanish civil engineers will no longer have to worry about the cumbersome task of signing approval of paper-based blueprints thanks to a smart-card-based system which allows them to sign off the document electronically.Visit www.compseconline.com for the latest computer security industry news [Copyright &y& Elsevier]

This paper presents a secure face recognition system with advanced template protection schemes for Cyber–Physical–Social Services (CPSS). The implementation of the proposed system consists of five components. The initial step performs image preprocessing, where it detects the facial region from the captured image using the Tree-Structured Part Model (TSPM). The second phase involves feature extraction, where it utilizes the Scale Invariant Feature Transform (SIFT) descriptor to extract features from small patches of the preprocessed images, forming a collection of feature descriptors. The collection of feature descriptors is then clustered using the K-means clustering algorithm, returning the centers of K-clusters that serve as the vocabulary of a dictionary. Finally, a histogram is generated using the vocabularies and frequencies, referred to as the "Bag of Visual Words (BoVW)". Using this dictionary and a feature learning technique called Sparse Representation Coding (SRC), followed by Spatial Pyramid Mapping (SPM), the system generates feature vectors from training/testing image samples. In the third component, the modified FaceHashing technique is applied to the original feature vectors, generating cancelable feature vectors. The fourth component employs a Bio-Cryptographic technique to preserve the cancelable feature vectors in a database. Lastly, the fifth component utilizes a multi-class linear SVM classifier on the decrypted and query-cancellable feature vector to classify users. The system evaluates its performance using FERET and CASIA-FaceV5 benchmark databases, providing 100% identification accuracy for 200-dimensional cancelable feature vectors. The performance and security comparisons demonstrate the superiority of the proposed system over existing methods. • Encrypted domain-based recognition system. • Modification of the FaceHashing technique up to three levels of security. • Two additional prime factors in the RSA cryptosystem. • Access control via OTP-based smart card. • Original feature vectors kept offline. [ABSTRACT FROM AUTHOR]

• This paper contributes to the understanding of the taste heterogeneity of travelers to pre-peak discount pricing strategy. • Naturalistic data from smart card data users in Beijing before and after a policy intervention is used in this regard. • Four groups of travelers with heterogeneous characteristics are classified by constructing a latent class model. • Different targeted policy recommendations for each group of travelers are proposed in order to improve the policy effect. Studies of travelers' response behavior to transportation demand management is receiving substantial attention among researchers and transport operators in recent years. While previous studies in this area have generally assumed that the sensitivity of travelers to different factors is homogeneous and relies on survey responses, which may be prone to self-reporting errors and/or subject to behavioral incongruence. Relying on naturalistic data, this paper aims to investigate the behavioral response to pre-peak discount pricing strategy in the context of the Beijing subway with a special focus on the heterogeneity among the travelers. Anonymous smart card data from 5946 travelers before and after the introduction of a peak avoidance policy in Beijing are used to construct a latent class choice model to capture the sensitivity to different factors and the associated taste heterogeneity of travelers. Given the passive nature of the data, the model can offer more realistic outputs. The results indicate that there is substantial heterogeneity in travelers' responses to the peak avoidance policy, and that they can be probabilistically allocated to four latent classes. For all classes of travelers, the decision to shift their departure to off-peak is affected by the monetary saving, the required change in departure time and the frequency of travel, but in different magnitudes. In particular, only two classes of travelers (who exhibit lower standard-deviation in pre-intervention departure time) show significant sensitivity to price changes indicating that the discount policies are more likely to be effective for these groups. The rest of travelers are largely price insensitive – warranting the need for non-monetary incentives as opposed to fare discounts. To the best of our knowledge, this study is the first to innovatively apply the LCC framework to analyze travelers' heterogeneous behavior using large-scale smart card data without socio-demographic information. The findings can provide guidance to the subway authority in devising differential peak avoidance policies targeted for different groups of users, which are likely to be more effective than the current 'one size fits all' approach. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of the open-system Bus Rapid Transit (BRT) network on property values. An open-system BRT is one where bus feeder lines can enter and leave the BRT system, depending on their origin or destination so the BRT system infrastructure is shared by multiple routes. Brisbane, Australia is the empirical setting where smartcard (GoCard) data shows that 43% of bus passengers accessed the BRT system from feeder line stops. The paper investigates whether feeder line stops are important for increasing network accessibility in Brisbane's open-system BRT. The hypothesis underpinning the study is that the improved accessibility resulting from an open-system BRT network results in higher property values within feeder line corridors in addition to simply around the BRT system. A Geographically Weighted Generalized Linear Model (GWGLM) is used to investigate property value premiums and their spatial distribution. GWGLM makes an improvement over Geographically Weighted Regression Model (GWR) by providing flexible local and global variable settings that decrease the risk of multi-collinearity in local models. The results identify property value uplift of up to 1.64% for every 100 m closer to feeder bus stops with frequent services in western and eastern Brisbane suburbs. Future studies should pay attention to the type of BRT operation (whether open- or closed-) in investigating the value of accessibility from BRT implementation. The results are policy relevant for the debate between whether BRT systems should be open or closed. [ABSTRACT FROM AUTHOR]

The rail transit has difficulties in meeting daily travel needs of passengers owing to a large population and accelerating urbanization. Analyzing urban travel behaviors with big data helps the design in infrastructures and the optimized personnel allocation. Furthermore, travel behaviors are characterized by dynamic at different time and locations, displaying the rule of urban traffic operation. This paper utilizes smart card data in two cities with different geographical features to analyze the temporal–spatial characteristics of urban travel behaviors. More specifically, by creating travel networks based on the pick-up and drop-off stations and the passenger population among these stations, an interesting observation is that the community structure of travel networks owns a metabolic trend and a stable feature simultaneously. The finding shows that the traffic system can be managed in several parts. Moreover, similar mobility patterns exist in some stations, which can be organized and controlled in the same way. Finally, travel behaviors are related to the urban layout and structure, so the distribution of urban areas can be understood better. Experiments provide enlightening insights for policy makers to comprehend the urban travel behaviors, thus improving the rail transit service plans and scheduling strategies. [ABSTRACT FROM AUTHOR]

• Route choice behaviour of metro-bikeshare users is studied using smartcard data. • The congestion status of metro-bikeshare integration network is measured. • Passengers' socio-economic attributes and perceived congestion are considered. • Research results is help to improve personalized transit-oriented travel services. Understanding the determinants of the route choice behaviour on a multi-modal transit network of metro and shared bike is important to improve personalized multimodal travel services. This paper attempts to analyse the route choice behaviour of metro-bikeshare users considering passengers' socio-economic attributes and perceived congestion which is approximated by load status. An abstract integrated metro-bikeshare network (IMBN) is built with virtual nodes by aggregating shared bike stations within the walkable distance and abstract routes by aggregating optional paths for each OD pair. Using the metro- and shared bike smart- card data from Nanjing, China, the route sets of metro-bikeshare users were extracted from the IMBN. A multinomial Logit model (MNL) was then applied to investigate the determinants of route choice behaviour for two types of users, namely "return-enter" and "exit-lease", respectively. The results show that the models with the load status attributes have a better performance than the models without these attributes. We found the sensitivity of "exit-lease" users to the train crowding is significantly greater than that of the "return-enter" users. "Return-enter" users have a higher perception of out-of-vehicle travel time (OVTT) than that of in-vehicle travel time (IVT), while the "exit-lease" users have the opposite perception. Besides, the change rate of shared bike inventory, departure time and whether he or she is a regular user also have a significant impact on route choice behaviour. The findings can help policymakers and system operators to improve the services and the efficiency of the multimodal transportation system. [ABSTRACT FROM AUTHOR]

This paper presents a data fusion method to infer the origin and destination zones of transit trips from smart card data. The fusion framework has disaggregate mixed multinomial logit models at its core that predict the most probable origin and destination zones of individual transit trips using smart card transaction records, land use data, and transit system characteristics. The logit models are estimated using revealed trip origin and destination responses from a travel survey that are augmented by land use and transit system data to provide contexts about the zones' trip generation and attraction potentials. For empirical analysis, the methodology is applied to the smart card system of the Greater Toronto and Hamilton Area. Specifically, it is tested under different system configurations (tap-on-only and tap-on-and-off) and for networks with substantial shares of automobile and walk access/egress. When applied to transit trips constructed from the smart card transactions, the estimated models successfully capture the spatial distribution of trip origin and destination at the traffic analysis zone level. The empirical analysis also demonstrates that the proposed fusion method can be appropriately used to reconcile information provided by transit smart card and travel surveys to generate up-to-date transit demand data necessary for public transport planning and operations. [ABSTRACT FROM AUTHOR]

Many human activities, including daily travel, show a mix of stable, intermittent and changing patterns in demand by individuals over time. However, the lack of continuous, long-term, passenger-linked data for public transport (PT) journeys means that we do not know how passenger ridership evolves in real-world networks. This paper proposes the CIAM model for classifying long-term passenger engagement with PT. CIAM is a data-driven model combining year-on-year churn (C), monthly intensity (I), annual (A) and multi-year (M) engagement. Parameter search algorithms are used to ensure that the learned features are distinctive and robust. We evaluated CIAM using a 5-year dataset from a PT network with over 300 million journeys. CIAM identified distinct patterns of long-term ridership at multiple time scales. Although the total number of annual journeys was relatively stable over the five years, we found long-term differences between passenger subgroups. Churn of passengers was a major factor in ridership with only 55% of passengers retained from year to year. Patterns of annual engagement are often intermittent, so short-term snapshots of a few weeks are typically not good indicators for longer term engagement. Only 27% of high-frequency, full-fare riders still have the same level of engagement four years later, compared with 55% who continue high-frequency engagement after only one year. • CIAM learns long-term patterns of PT engagement at multiple temporal scales. • Churn is a major factor of ridership with year on year passenger retention of only 55%. • Annual engagement is a mixture of contrasting short-term intensities. • Only 27% of high-frequency, full-fare passengers maintain that engagement for 5 years. • Data-driven CIAM identifies underlying context transitions from movement data alone. [ABSTRACT FROM AUTHOR]

The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud detection, in collaboration with our industrial partner, Worldline. Transactional companies are more and more dependent on machine learning models such as deep learning anomaly detection models, as part of real-world fraud detection systems (FDS). We focus on continual learning to find the best model with respect to two objectives: to maximize the accuracy and to minimize the catastrophic forgetting phenomenon. For the latter, we proposed an evaluation procedure to quantify the forgetting in data streams with delayed feedback: the plasticity/stability visualization matrix. We also investigated six strategies and 13 methods on a real-size case study including five months of e-commerce credit card transactions. Finally, we discuss how the trade-off between plasticity and stability is set, in practice, in the case of FDS. • Fraud detection models must be updated continually to handle new fraud strategies. • They must balance plasticity (learn new patterns) and stability (remember old ones). • We show how to quantify both and discuss the trade-off for fraud detection. • We provide an extensive comparison of six strategies and 13 different models. • We present a real case study based on more than 50 million e-commerce transactions. [ABSTRACT FROM AUTHOR]

An anonymous transit pass system allows passengers to access transport services within fixed time periods, with their privileges automatically deactivating upon time expiration. Although existing transit pass systems are deployable on powerful devices like PCs, their adaptation to more user-friendly devices, such as mobile phones with smart cards, is inefficient due to their reliance on heavy-weight operations like bilinear maps. In this paper, we introduce an innovative anonymous transit pass system, dubbed A n o P a s , optimized for deployment on mobile phones with smart cards, where the smart card is responsible for crucial lightweight operations and the mobile phone handles key-independent and time-consuming tasks. Group signatures with time-bound keys (GS-TBK) serve as our core component, representing a new variant of standard group signatures for the secure use of time-based digital services, preserving users' privacy while providing flexible authentication services. We first constructed a practical GS-TBK scheme using the tag-based signatures and then applied it to the design of AnoPas. We achieve the most efficient passing protocol compared to the state-of-the-art AnoPas/GS-TBK schemes. We also present an implementation showing that our passing protocol takes around 38.6 ms on a smart card and around 33.6 ms on a mobile phone. [Display omitted] • Anonymous transit pass on mobile phones with smart cards. • Practical group signatures with time-bound keys. • Support for automatic revocation and prematurely revocation. • Flexible authentication for time-based digital services. • Privacy protection for users. [ABSTRACT FROM AUTHOR]

Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns. Where cardholders are clustered into different groups based on their transaction amount. Then using sliding window strategy [1], to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers [3],[5],[6],[8] are trained over the groups separately. And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift [1]. In this paper, we worked with European credit card fraud dataset. [ABSTRACT FROM AUTHOR]

Few studies based on large sample data have examined mobility patterns from a travel distance perspective and investigated the potential influence of urban form and land use on people's daily travel distances. This paper provides additional empirical insights into spatiotemporal urban mobility patterns and their relationship with urban form and land use using station-based average travel distances (ATDs). Drawing on smart card data of the Nanjing metro system, land use data and open source points-of-interest (POIs) data, we apply exploratory spatial data and quantile regression analysis to examine distance patterns and explore the potential effects of urban form and land use calculated at different spatial scales (i.e. 800 m, 2 km and 5 km) on the ATDs. By comparing mobility patterns between weekdays and weekends and for different times of day, our findings highlight that ATDs are not uniformly nor randomly distributed in space. Positive spatial autocorrelation is found for different time segments. The results of OLS and quantile regression models show a positive and robust relationship between ATDs and distances to the city center (DCs). The models also prove that land use mix (especially measured at the 2 km and 5 km scale) significantly affects ATDs , supporting the importance of land use mix in decreasing daily travel distances. No significant relationship is found between ATDs and distances to the nearest subsidiary center (DSCs), while the employment/entertainment-residence balance has a marginal effect on ATDs at relatively large spatial scales (i.e. 2 km, 5 km). Consequently, with respect to reducing the ATDs , we recommend enhancing land use mix and reducing the imbalance between employment/entertainment and residence at larger spatial scales. Potential applications and future research directions are discussed. The findings in the present paper are helpful for guiding urban planning and policy making. • We explored urban mobility rhythm using the station-based average travel distances (ATDs) of out-flows, in-flows and all-flows for a whole day and a specific time span within a day. • ATDs are not uniformly or randomly distributed in space and show the pattern of concentric rings from the central to peripheral urban area. • Quantile regression models were used in the study to investigate the relationships between distances to the city center, distances to the nearest subsidiary center, land use mix, POIs-based employment/entertainment-residence balance and ATDs. • Land use mix has a significantly positive impact on ATDs. • We recommend enhancing land use mix and reducing the imbalance between employment/entertainment and residence at larger spatial scales. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative method of replicating the multi-modal public transport system in Singapore with high precision using smart card database. It replicates the operation of public transport system with known exogenous passenger demand and provides many operational details, including passenger inter-modal trip chains, operational timetable, and detailed transfer behaviour. The paper elaborates on the methodology of the replication including data cleaning, filtering, processing and converting the collected data to meaningful information such as bus journey trajectories and metro system timetable. Thereafter, actualised passenger trip chains are directly assigned to the replicated public transport supply. The resulting replication covers almost 96% of trips made in public transport in Singapore. It provides solid quantitative information on several aspects to support decision making, including precise temporal and spatial travel demand analysis, transfer pattern analysis, traffic condition investigation and bus utilisation analysis. [ABSTRACT FROM AUTHOR]

This paper presents a combined two-dimensional word-based serial-in/serial-out systolic processor for field multiplication and squaring over GF( 2 m ) to improve hardware utilization and power consumption. The proposed processor is extracted by applying non-linear scheduling and projection functions to the algorithm dependency graph. The extracted processor features scalability that gives the designer more flexibility to control the processor size and the execution time. ASIC Implementation results of the proposed combined two-dimensional word-serial design and the best existing designs show that the proposed structure realizes a considerable saving in the area and consumed energy up to 93.7% and 98.2%, respectively. This makes it more suitable for restricted implementations of cryptographic primitives in resource-constrained consumer electronics devices such as hand-held devices, wearable and implantable medical devices, smart cards, wireless sensor nodes, restricted nodes in the Internet of Things (IoT), and radio frequency identification (RFID) devices. [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]

In densely populated areas, joint activity participation such as shopping and eating outside represents a substantial portion of individuals' daily activity-travel patterns. Individuals have several considerations in making joint activity choice behaviour such as space-time coordination, activity start time, activity location and joint activity duration. In a congested transit network, the travel time uncertainty significantly affects individuals' activity and travel choice behaviour. To conduct some important joint activities, individuals normally have high expectations of on-time arrival. Thus, the measurement of space-time accessibility in transportation networks should be extended to consider joint activity choices and travel time uncertainty. In this paper, a new method for measuring space-time accessibility is proposed for individuals' joint activities in transit network with consideration of travel time uncertainty. A reliable alighting location model is proposed to identify all feasible alighting locations in transit network to perform joint activities by explicitly considering constraints of on-time arrival probabilities. Individuals' joint space-time accessibility (JSTA) is measured based on reliable alighting locations, travel time budget, and points of interests. Massive smart card data from metro network in Nanjing, China are used to show the merits of the proposed JSTA measure. The results show that there is a significant difference between the space-time accessibility to independent activity and that to joint activity under various on-time arrival probabilities. The effects of spatial (de)concentration of anchor locations and minimum joint activity duration on JSTA, and the role of additional participants are also presented in various scenarios. The proposed JSTA measure can be used to help policy makers to evaluate transit network development and land use planning in urban areas. [ABSTRACT FROM AUTHOR]

Health related information of an individual is very sensitive and demands a high level of security and privacy. Healthcare providers have the responsibility to ensure that patient information is secure and accessible only to authorized users. Healthcare systems are using biometrics since long for authentication and/or access control purposes. Biometrics can also be used for healthcare data security and privacy. This paper proposes an iris based cancelable biometric cryptosystem to securely store the healthcare data of patients on the smart card. It employs symmetric key cryptography to encrypt the healthcare data and store it on the smart card in encrypted form. We use the fuzzy commitment scheme to bind the secret encryption key with the cancelable iris template of the patient. Our proposed scheme provides user authentication as well as the decryption of healthcare data when needed by using the iris template of the owner of the healthcare smart card. The implementation results show that our proposed scheme provides better performance as compared to other schemes. It can generate an encryption key of a maximum of 252 bits from the input iris template with a false acceptance rate (FAR) of 0 and a false rejection rate (FRR) of 0.07. The generated key can be used for encrypting the health care data of patients using a symmetric encryption algorithm, e.g. Advance Encryption Standard (AES), International Data Encryption Algorithm (IDEA), Blowfish, etc. As compared to a conventional encryption system where the security of the system depends on keeping the key secret, our proposed scheme binds the encryption key with the iris - template of the patient impeccably without the need to store it securely. The security analysis demonstrates that it is not possible for an attacker to retrieve the secret key or healthcare data of the patient from the stolen healthcare card. [ABSTRACT FROM AUTHOR]

The wide application of pervasive computing technology has allowed for the emergence of big data on spatial behavior and therefore provides an opportunity to explore dynamic urban space. In this paper, an eigendecomposition method is proposed to capture the common patterns of passengers’ variation over time among all metro stations as well as to explore the spatial heterogeneity of the dynamic space around the metro stations based on the common patterns with low dimensional structures. Using Shenzhen as a case study, four datasets for check-in/check-out and weekday/weekend are decomposed to obtain the principal components (PCs) and eigenvectors. The first several PCs are the most common patterns of passengers’ variation over time among all metro stations, while the corresponding elements in the eigenvectors, referred to as EigenStation in this paper, can describe the characteristics of the metro station. The decomposition result is evaluated at both the aggregation and individual station levels, and the result demonstrates that the first two elements of the EigenStation can approximate the original dataset. The EigenStation vector angle, i.e., ω, is used to represent the structure of the EigenStation, and its value is highly related to the land use structure around the metro stations. The proposed method can provide deep insight into static and dynamic urban spaces, which can help improve urban planning around metro stations. [ABSTRACT FROM AUTHOR]

Mobile phones are devices that are nowadays widely used by people. The development of technology has resulted in them being used not only for talking and messaging. They have functionalities that make them similar to personal computers. These devices are used to send and receive e-mails, browse the web, organize time, take pictures, play games, listen to music. There is also a possibility of installing various applications on them. The capabilities of these devices are closely related to their parameters, such as memory card size, internal memory, RAM, camera resolution. Price, which increases with the improvement of the parameters, also is a significant criterion of choice. Therefore, the choice of the most appropriate mobile phone model to match the consumer's expectations requires the consideration of many criteria. Choosing the best option when making a decision is challenging because many factors influence the final decision. Additionally, they can be very diverse and difficult to take into account as a whole. An excellent solution to this problem is multi-criteria decision analysis (MCDA) methods, which support choosing from a wide variety of criteria. In this paper, four methods from the MCDA group - COMET, PROMETHEE II, VIKOR and TOPSIS - were used to determine the best mobile phone model among a group of alternatives. It is a complicated issue given the number of models available on the market and the number of parameters to consider when comparing. These methods have been successfully applied in the past to solve problems of similar complexity. All alternatives were analyzed using each method. Then the results were compared to each other to evaluate their similarity. Thus, the best mobile phone was selected for every applied method, and an attempt was made to find out the effect of MCDA method selection on the final ranking. The results obtained show that the tool based on MCDA methods is useful, appropriate, and effective in supporting consumer decisions when choosing mobile phones. [ABSTRACT FROM AUTHOR]

This paper develops a method for analysing the elasticity of travel demand to public transport fares. The methodology utilizes public transport smartcard data for collecting disaggregate full population data about passengers' travel behaviour. The study extends previous work by deriving specific fare elasticities for distinct socioeconomic (e.g., car ownership and income) groups and public transport modes (metro, trains and buses), and by considering the directionality of the fare change. The case study involves a public transport fare policy introduced by the regional administration of Stockholm County in January 2017, where the zonal fare system for single-trip tickets was replaced by a flat-fare policy. The overall fare elasticity of travel funds is found to be −0.46. User sensitivity grows along with the journey distance. Metro users demonstrate the lowest sensitivity, followed by bus and commuter train riders. Low socioeconomic groups, in particular with respect to car ownership, tend to be less sensitive than the high-factor groups. In addition to the direct effect of changed fares, simplification and unification of the fare scheme appears to have substantially contributed to its attractiveness. The flat fare may allow the geographic disparity of public transport travel to be reduced and new users to be attracted from remote areas who are more prone to own cars. • Method for calculating fare elasticities from smart card validation records. • Impacts of zone-based to flat fare scheme change for different user groups. • Fare elasticities vary across socioeconomic groups and public transport modes. • Regular travel, low income, trip distance, train use increase fare sensitivity. • Fare scheme simplification contribute to attracting travel demand. [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]

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]

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements. [ABSTRACT FROM AUTHOR]

The data capturing and access process is an important stage in the big data applications. Most of these applications are exploited the wireless sensor networks (WSNs) to accomplish this process through the sensor nodes that are deployed in unsecure and unattended environments. Therefore, these applications are suffered from numerous security weaknesses, an adversary may exploit such weaknesses to break user's privacy, confidentiality of sensor nodes, and control the communication channel between the network components. Consequently, the security issues for this process have attracted much interest of the researchers with the increasing spread of use such applications. The majority of the proposed authentication schemes fail to solve all existing security weaknesses simultaneously. Thus, the authentication scheme is a critical issue in the WSNs. This paper proposes anonymous access authentication scheme for wireless sensor networks in big data environments (AAA-WSN) to achieve appealing security services. Comparing with the recent WSNs authentication schemes, the AAA-WSN scheme cannot only achieve strong security services such as user anonymity and mutual authentication, but also performs the perfect forward secrecy feature with high level of efficiency. The security analysis shows that the AAA-WSN scheme is resistant to the current known attacks. Moreover, the performance analysis in terms of the storage, computations and communications costs demonstrates that the AAA-WSN scheme achieves high level of security with desirable level of efficiency comparing the recent WSNs authentication schemes. [ABSTRACT FROM AUTHOR]

• Estimated the causal economic impact of an off-peak fare discount in Hong Kong. • Applied the difference-in-difference method to control for confounding bias. • Found the aggregate effect to be small but statistically significant and heterogeneous. • Responsiveness was governed by travel costs and crowding. • Produced novel causal estimates of trip rescheduling elasticities. This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy. [ABSTRACT FROM AUTHOR]

In recent years smartcards have been implemented in many transit systems around the world as a means by which passengers pay for travel. In addition to allowing speedier boardings there are many secondary benefits of smartcard systems including better understanding of travel patterns and behaviour of travellers. Such research is dependent on the smartcard correctly recording the boarding stop, and where available the alighting stop. It is also dependent on the smartcard system correctly aggregating individual rides into trips. This paper identifies causes for why smartcard systems may not correctly record such information. The first contribution of the paper is to propose a set of rules to aggregate individual rides into a single trip. This is critical in the research of activity based modelling as well as for correctly charging the passenger. The second contribution of the paper is to provide an approach to identify erroneous tap-out data, either caused by system problems or by the user. An approach to detecting this phenomenon is provided. The output from this analysis is then used to identify faulty vehicles or data supply using the “comparison against peers approach”. This third contribution of the paper identifies where transit agencies and operators should target resources to improve performance of their Automatic Vehicle Location systems. This method could also be used to identify users who appear to be tapping out too early. The approaches are tested using smartcard data from the Singapore public transport network from one week in April 2011. The results suggest that approximately 7.7% of all smartcard rides recorded the passenger as alighting one stop before the bus stop that they most probably alighted at. A further 0.7% of smartcard rides recorded the passenger as alighting more than one stop before the bus stop that they most probably alighted at. There was no evidence that smartcards overestimated the distance travelled by the passenger. [ABSTRACT FROM AUTHOR]

• An heterogeneous corpus of data sources is now available to estimate travel demand. • We compare three independent data sources to estimate public transit OD matrices. • Automatic fare collection data is not error-free. • Household travel survey may significantly underestimate the volume of trips. • Better understanding of the available data sources and the potential trade-offs. Origin–destination (OD) matrices are one of the key elements in travel behaviour analysis. For decades, transportation researchers have mostly used data obtained by active solicitation such as surveys to construct these matrices but new data sources like automatic fare collection (AFC) are now available and can be used to measure OD flows. As a result, a more heterogeneous corpus of data sources is now available to estimate travel demand. However, little research examines how comparable the estimated demands may be. In this paper, three data sources namely a household travel survey, a large scale origin–destination survey and entry only automated fare collection are processed to derive typical weekday public transit OD trip matrices. Various elements of the resulting matrices are then compared. While all the matrices share some common characteristics, there are also substantial differences that must be addressed. AFC data is not error-free and needs to be supplemented with data from other sources to construct a representative OD trip matrix. This is because not all destinations can be inferred, the smart card penetration rate is far less than 100% and fare evasion cannot be ignored. Our empirical results suggest that scaling an AFC matrix with automated passenger counts may be a viable solution. The results also indicate that the household travel survey significantly underestimates the volume of public transit trips compared to the other sources. The findings of this research contribute to a better understanding of the available data sources for public transit demand estimation. They can help practitioners to improve the quality and accuracy of OD matrices. [ABSTRACT FROM AUTHOR]

• A new concept to understand the mobility and location features of subway stations. • Transplant Language Processing to understand subway stations as compound words. • Obtain mobility and location semantics from neural net and affinity propagation. • Interpretsemantic results in an urban planning aspect. Rapid urbanization and modern civilization require sound integration with public transportation systems. In the same time, the volume and complexity of public transportation network are increasing, making it harder to understand the public transportation dynamics. As a first step, understanding the similarity among subway stations is imperative. In this paper, we proposed a semantic framework inspired from natural language processing (NLP) to interpret subway stations as compound words. Specifically, we transplanted context and literal meaning of compound words into mobility and location attributes of stations. Using smart card data, we trained stacked autoencoders (SAE) with designed flow matrices as an embedding method to learn the mobility attributes. Subsequently, to discover the location attributes, we have applied affinity propagation clustering to classify 9 point of interest (POI) categories. Combined with urban planning knowledge, we manage to comprehend the land use meanings of 9 POI clusters. The location semantics is chosen from those categories reflecting its urban land use pattern. By choose meaningful combination of mobility and location semantics for stations' similarity case studies, we summarized potential applications of this semantic framework. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are one of the most commonly used network technologies and the security of sensing data in WSNs has attracted much attentions. However, many security schemes, such as encryption and authentication, used in WSNs are limited due to the energy constraint of sensor nodes. Therefore, how to deal with the trade-off between security and energy consumption is a challenging issue in current study. In this paper, we propose an asymmetric key encryption scheme based on elliptic curve to provide a solution for WSNs. Firstly, the scheme optimizes the key scheme which generates and manages keys periodically. Secondly, it uses privacy homomorphism to support end-to-end encryption. Thirdly, we design a rotation MAC generationalgorithm to achieve the hop-by-hop verification. The experimental results show that the proposed mechanism has lower energy consumption and higher security level. [ABSTRACT FROM AUTHOR]

IoT's population is converging at a rate exceeding the population rate of many countries. IoT environment consist of many heterogeneous entities which are interconnected, controlled and monitored through internet. One of the most challenging tasks in IoT is providing authentication in the heterogeneous environment. Authentication ensures the security of the devices and information that are interconnected in the IoT network. In this paper, the authors proposed a light weight authentication protocol which uses implicit certificates to provide mutual authentication and key agreement between the IoT devices. The proposed protocol (LAPIC) is resistant to various attacks such as replay, masquerade, node compromise and man in the middle attack. The efficiency and reliability of the proposed technique is verified through performance, security and comparative analysis. The results revealed that proposed authentication protocol consumes nominal time and energy for its operation and is found to be resistant against many attacks which make it a suitable approach for resource constrained IoT applications. [ABSTRACT FROM AUTHOR]

• Proposes a general approach for the design of promotion-based transit TDM strategies. • Formulates the optimal promotion design as a 0-1 integer optimization problem. • Recognizes explicitly the role of effective and ineffective users in the formulation. • Evaluates the performance profile of various structures in space, time and discount. Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies. [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]

• This paper proves that the user access to drone scheme of Wazid et al. is vulnerable to several attacks including stolen verifier, traceability attacks. • In Wazid et al.s scheme, the attacker with access to the verifier, can impersonate any user, drone or server of the system. Moreover, the access to verifier enhances the attacker's capability to disclose the session key computed between a user and a drone. • An enhanced scheme is then proposed to cope with these weaknesses. • The security claims of proposed scheme are proved by formal and informal security analysis. • The performance and security comparisons show that proposed scheme has slight overhead in performance, while maintaining the security. The Internet of drones (IoD) is a very useful application of the Internet of things (IoT) and it can help the daily life comfort through various functions including the smart city surveillance. The IoD can enhance the comfort to reach inaccessible and hard to access sites and can save lot of effort, time and cost. However, in addition to traditional threats, the IoD may suffer from new threats and requires customized methods to combat the security weaknesses. Very recently, Wazid et al. proposed a security solution for securing IoD application scenario and claimed its security. However, in this paper we show that their scheme cannot resist stolen verifier and traceability attacks. Moreover, an attacker with access to the verifier, can impersonate any user, drone or server of the system. An enhanced scheme is then proposed to cope with these weaknesses. The security claims of proposed scheme are endorsed by formal and informal security analysis. Moreover, the performance and security comparisons show that proposed scheme completes a cycle of authentication with a slight increase in computation time, but it offers all the required security features as compared with the scheme of Wazid et al. [ABSTRACT FROM AUTHOR]

Over-the-air (OTA) application installation and updates have become a common experience for many end-users of mobile phones. In contrast, OTA updates for applications on the secure elements (such as smart cards) are still hindered by the challenging hardware and certification requirements. The paper describes a security framework for Java Card-based secure element applications. Each application can declare a set of services it provides, a set of services it wishes to call, and its own security policy. An on-card checker verifies compliance and enforces the policy; thus an off-card validation of the application is no longer required. The framework has been optimized in order to be integrated with the run-time environment embedded into a concrete card. This integration has been tried and tested by a smart card manufacturer. In this paper we present the architecture of the framework and provide the implementation footprint which demonstrates that our solution fits on a real secure element. We also report the intricacies of integrating a research prototype with a real Java Card platform. [ABSTRACT FROM AUTHOR]

Abstract: Micropayment is common in retail outlets and public transport systems. This paper considers the situation where smart-card-based e-micropayment scheme starts off as single-purpose scheme and is progressing to be a multi-purpose scheme. User deciding whether to use such a scheme is a multiple criteria decision-making (MCDM) problem and depends on factors such as transaction cost, availability of discount, ease of use, convenience, reliability, and participation by transport-based companies, retail outlets, large organizations, as well as whether others are using it. Through a case study of successful smart-card-based single-purpose e-micropayment schemes in Taiwan and from the user’s perspective, the dynamic picture of the progression is obtained to analyze how different factors interact and collectively contribute to the successful transformation and diffusion of the scheme. Since this is a MCDM problem, Decision Making Trial and Evaluation Laboratory, a MCDM method, is used. Hence, this paper contributes to extending practical applications of DEMATEL to the field of electronic payment system. It is found that the dynamic picture of the progression of single-purpose smart-card-based e-micropayment scheme to multi-purpose scheme is indeed complex. The factors are definitely not independent of one another and they interact with one another. Ease of use, discount and participation by retail outlets are net causer factors. Top-up locations, reliability of the scheme, participation by transport companies and large organizers are net receiver factors. Factors related to participants of the scheme (such as user base, retail-outlet and transport companies) also exhibit the presence of network effect. The scheme operator faces a chicken and egg situation but the operator has to start somewhere to kick-start the process, end the waiting game, and hopefully to achieve the bandwagon effect. A possible starting point is to focus a lot of the energy on getting more retail outlets onboard. To further accelerate the progression to multi-purpose scheme, a multi-pronged approach to manage the transformation is also advised. To attract more users and to push them to use the scheme to make payment at the participating retail outlets, discount can be offered. [Copyright &y& Elsevier]

Shared bicycles provide a convenient mobility option to commuters especially for short-distance trips. Nevertheless, it also presents a challenge to bicycle-sharing operators as they have to deal with reallocation issues, i.e. removing bicycles from parking facilities which are at or near capacity and refilling parking facilities that are in need of bicycles. Few studies in the literature have actually tried understanding why certain docking stations are prone to excessive demand or suffer from a lack of parking supply. This paper attempts to identify the demographic, built-environment and transport-infrastructure indicators that can potentially aid policy-makers and operators in identifying parking facilities prone to bicycle reallocation. In particular, we have adopted the bicycle sharing operations in Nanjing, China as a case study to understand how such indicators can be identified for appropriate parking infrastructural enhancements. To achieve this goal, this study has established zero-inflated negative binomial models using multi-source data including point-of-interest (POI), daily weather, transit stop location, demographic data and bike-share smart card data. The model results obtained from this study suggest that built environment correlates significantly to shared bicycle reallocation count. In general, bicycle docking stations with large reallocation counts are more likely to be found near residences, bus stops, metro stations, employment areas, restaurants, amenities, parks, sports facilities, and clinics/hospitals; while stations near entertainment facilities, places of attraction, hotels, shopping malls, and educational institution tend to have balanced demand and supply. Analysis on the elasticity values revealed that mean temperature and station capacity are the most influential factors in bicycle reallocation. Among all POIs, presence of restaurants and areas with high employment tend to exhibit strongly a need for morning bicycle removal and evening bicycle refilling at docked stations. Policy makers can provide actual guidelines in the planning of shared bicycle parking facilities using the findings and methodologies presented in this study. [ABSTRACT FROM AUTHOR]

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of local weather conditions on adult transit ridership across three transit modes. Drawing on smart card data and half hourly weather station records for a 12 month period, analysis reveals that weather imposes an effect on adult transit ridership and that its influence varies by mode. Ferry ridership is found to be more sensitive to changes in weather compared to either bus or train ridership. Findings also reveal that weather's influence on ridership varies across the course of a day. During morning and evening peak hours, weather is shown to exert a weaker effect than other periods throughout the day. We argue that our findings are important in their capacity to contribute to a new evidence base with the potential to inform the (re)design of more weather-resilient transit systems by shedding new light on the weather–transit ridership relationship. [ABSTRACT FROM AUTHOR]

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers' flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient. • A data-driven methodology is proposed to obtain metro passenger flow distributions. • A similarity measure model is built to identify possible routes by clustering result. • The travel times are clustered to obtain the possible travel time groups. • Real data from the Beijing metro smart card system is applied to verify our approach. [ABSTRACT FROM AUTHOR]

Recent years have witnessed amount of studies on characteristics of public transport network and passengers' flows, however, most of them are discussed independently. An important criterion for evaluating public transport network is whether it could satisfy travel demand well. Therefore, in this paper, we study the interaction between public transport network and passengers' flow. Firstly, a layered network model is constructed for depicting public transport network, passengers' Origin–Destination (OD) flows and transfer flows. Based on that, we propose layer operations to analyze interaction between them and to evaluate suitability between traffic supply (physical infrastructure) and travel demand (passengers' OD trips and transfers). The proposed model is validated against empirical data from Chengdu and Qingdao, two metropolis cities in China. Results reveal the two cities have significant difference in suitability between travel demand and traffic supply. Although in contrast with Chengdu, Qingdao with advantages on transporting passengers by more straight lines and non-walking transfers, the average transfer flows and OD flows on stations in Qingdao are much heavier. The study demonstrates the importance of layer operations on exploring the interaction study of public transport network and traffic dynamics. • Public transport network, people's OD and transfer are studied by layered network. • Layer operations are proposed to explore the interaction between layers. • GPS and IC card data from Chengdu and Qingdao are used to validate our methods. • Layer operation is useful to study suitability between traffic supply and demand. [ABSTRACT FROM AUTHOR]

In the present world, we are enabled with distinct technology devices and/or systems mostly to reduce human effort in completing various tasks. To fulfill our activities within a reasonable period, we expect reliable communication system(s) to exchange significant data securely. The vehicular cloud computing (VCC) is a system to control vehicle-related data for various computations and this data helps to different vehicle operators straightaway or indirectly. However, a receiver should confirm the correctness of obtained information else it influences in erroneous ways. Recently, Zhong et al. suggested a privacy-preserving authentication model but in this paper, we identify that this scheme cannot withstand against some attacks, i.e., impersonation, modification, plain-text, and man-in-the-middle. Thus, we propose an improved message confirmation system for the VCC to protect various security attacks, e.g., replay, plain-text, impersonation, man-in-the-middle, and modification. Further, we do performance and security analysis of the suggested method. Next, we compare the proposed system with different message verification protocols and the results show that the suggested method is more secure and effective compared to other related communication protocols. [ABSTRACT FROM AUTHOR]

With the increasing demographic shift towards a larger population of elderly, it is essential for policy makers and planners to have an understanding of travel characteristics of elderly and their difference with younger counterparts. Existing studies emphasize elderly's travel mode, travel frequency, travel distance, travel purpose and affecting factors, however, very few aim at comparing the spatio-temporal characteristics difference between weekday and weekend. In this paper, based on GPS data and Smart Card data of buses in Qingdao, the two cohorts' basic spatio-temporal travel patterns in aspects of travel distance, travel frequency and travel start time in weekday and weekend are compared. In addition, directed weighted networks of elderly's trips and younger people's trips in weekday and weekend are constructed for analyzing spatial characteristics. Results show that although the number of elderly experiences a reduction on weekend their travel frequency and travel distance show growth on weekend. In contrast with younger people, larger geospatial expansion of elderly's travel on weekend is observed. Elderly are found to prefer traveling in areas with high elderly people's residential density. Our study provides a deeper and nuanced understanding of elderly's spatio-temporal travel characteristics difference between weekday and weekend, so as to support better traffic policy making and the promotion on age-friendly public transport service. [ABSTRACT FROM AUTHOR]

Abstract Time is a fundamental characteristic for understanding human activities. When analysing temporal pattern of a group of activities, most researchers tend to utilise one temporal attribute when representing time use of activities. Thus, temporal pattern of activities is usually visualised and understood as a profile of various observations listed sequentially over time. This paper aims to investigate the temporal pattern of activities in urban areas from a new perspective. Temporal pattern is visualised and analysed as the distribution of activity points in a two-dimensional temporal plane defined by the start and end time of activities as x and y axes. Kernel density estimation is used as a typical method to observe the temporal pattern of activities in Shanghai based on a one-week smart card dataset generated in the Shanghai's metro system. The results show that the proposed perspective can reveal considerably more information regarding the temporal pattern than a conventional one can. Highlights • We analyse the temporal pattern of activities from a new perspective. • Kernel density estimation is applied to observe the temporal pattern of activities using a big data approach. • Temporal patterns of activities are explored at different spatial scales. • Our new method is able to reveal more information about activities compared with the traditional one. [ABSTRACT FROM AUTHOR]