Improved mobility not only contributes to more intensive human activities but also facilitates the spread of communicable disease, thus constituting a major threat to billions of urban commuters. In ...this study, we present a multi-city investigation of communicable diseases percolating among metro travelers. We use smart card data from three megacities in China to construct individual-level contact networks, based on which the spread of disease is modeled and studied. We observe that, though differing in urban forms, network layouts, and mobility patterns, the metro systems of the three cities share similar contact network structures. This motivates us to develop a universal generation model that captures the distributions of the number of contacts as well as the contact duration among individual travelers. This model explains how the structural properties of the metro contact network are associated with the risk level of communicable diseases. Our results highlight the vulnerability of urban mass transit systems during disease outbreaks and suggest important planning and operation strategies for mitigating the risk of communicable diseases.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
•A time-varying weighted encounter network to model epidemic spreading in PT.•A scalable and lightweight theoretical framework to solve the problem.•Various public health and transportation-related ...control policies are evaluated.•Partial closure of bus routes cannot fully contain the spreading of epidemics.•Isolating influential passengers” at an early stage can reduce the spreading.
Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people’s preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying “influential passengers” using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
With the purpose of using numerous different network services with single registration, various multi-server authentication schemes have been proposed. Furthermore, in order to protect the users from ...being tracked when they login to the remote server, researchers have proposed some dynamic ID based remote user authentication schemes for multi-server environments. Recently, Lee et al. have pointed out the security weaknesses of Hsiang and Shih’s dynamic ID based multi-server authentication scheme, and proposed an improved dynamic ID based authentication scheme for multi-server environments. They claimed that their scheme provided user anonymity, mutual authentication, session key agreement and can resist several kinds of attacks. In this paper, however, we find that Lee et al.’s scheme is still vulnerable to forgery attack and server spoofing attack. Besides, their scheme cannot provide proper authentication if the mutual authentication message is partly modified by the attacker. In order to remove these security weaknesses, we propose a novel smart card and dynamic ID based authentication scheme for multi-server environments. In order to protect the user from being tracked, the proposed scheme enables the user’s identity to change dynamically when the user logs into the server. The proposed scheme is suitable for use in multi-server environments such as financial security authentication since it can ensure security while maintaining efficiency.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A comprehensive review of recent development on transit OD estimation is presented.•Transit OD matrix estimation process is divided into four components.•The current algorithms and errors associated ...with them are discussed in detail.•Sketch of solutions of current issues are also included.
In public transport, smartcards are primarily used for automatic fare collection purpose, which in turn generate massive data. During the last two decades, a tremendous amount of research has been done to employ this big data for various transport applications from transit planning to real-time operation and control. One of the smart card data applications is the estimation of the public transit origin–destination matrix (tOD). The primary focus of this article is to critically analyse the current literature on essential steps involved in the tOD estimation process. The steps include processes of data cleansing, estimation of unknowns, transfer detection, validation of developed algorithms, and ultimately estimation of zone level transit OD (ztOD). Estimation of unknowns includes boarding and alighting information estimation of passengers. Transfer detection algorithms distinguish between a transfer or an activity between two consecutive boarding and alighting. The findings reveal many unanswered critical research questions which need to be addressed for ztOD estimation using smartcard data. The research questions are primarily related to the conversion of stop level OD (stOD) to ztOD, transfer detection, and a few miscellaneous problems.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Significant advances in wireless communication technologies have led to the emergence and proliferation of a wide range of mobile devices and mobile services. However, the use of various cloud ...servers has made the traditional single-server architecture, where we have one server and many users, inefficient in terms of its performance. To address this drawback, multi-server architectures have been proposed. Password or smart card-based authentication schemes suffer from poor security in the multi-server environment and as a result biometrics have become a preferred choice for secure and robust authentication because of its close link with the physical characteristics of an individual. Recently Kumari and Li et al. proposed a biometrics-based authentication scheme for multi-server environment. However, we found that their scheme fails to meet user anonymity requirement and is vulnerable to several attacks. Therefore, first of our work, we describe the various possible attacks on the previous scheme. Then, to enhance user anonymity, we propose a new biometrics-based authentication scheme with key distribution for the mobile multi-server environment. Our proposed scheme is based on smart card and elliptic curve cryptosystem. Informal and formal security analyses demonstrate that our scheme can satisfy the security and functional requirements in the mobile multi-server environment. Moreover, performance results (such as computation and communication cost) obtained with our proposed scheme demonstrate significant improvements in the level of security.
•It is a biometric-based anonymous authentication scheme for multi-server environment.•It is proven to be provably secure under random oracle model.•It incurs low overhead making it suitable for deployment at mobile devices.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•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.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
► We investigate statistical side channel analysis attacks on the SEED block cipher implemented in commercial smart cards used in a real-world electronic payment system. ► Our results show that an ...unprotected implementation of SEED allows one to recover the secret key with low number of power or electromagnetic traces. ► This paper shows that, although hiding countermeasures such as random current and random noise may increase the number of power traces needed for a successful attack, it is difficult to provide sufficient resistance to side channel attacks for itself.
We investigate statistical side channel analysis attacks on the SEED block cipher implemented in two commercial smart cards used in a real-world electronic payment system. The first one is a contact-only card and the second one is a combination card. Both cards have no masking scheme at algorithm level and the combination card supports only hiding techniques in hardware level. Our results show that an unprotected implementation of SEED allows one to recover the secret key with low number of power or electromagnetic traces. Moreover, this paper clearly confirms that, although hiding countermeasures such as random current and random noise may increase the number of power traces needed for a successful attack, it is difficult to provide sufficient resistance to side channel attacks for itself. We believe that our results in this research will also be beneficial to the analysis and protection of other algorithms and commercial smart cards.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Propose a data fusion model for combining self-reported RP and smart card data.•Propose a combined Expectation Maximization and Nested Logit estimation algorithm.•Formulate stochastic travel time ...budget by incorporating individual differences.•Propose a path choice model with stochastic travel time budget.•Propose a latent variable model to formulate individual risk-averse attitude.
With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Clustering of passenger cards using continuous temporal activities.•The replicability of the approach proposed by Briand et al. (2016) is demonstrated.•Offering a simple interpretation of cluster ...patterns.•A longitudinal analysis is performed to study the evolution of passenger behaviour.•Spatial characterization is performed on the clusters using Shannon entropy.
In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•The present study proposed a rigorous methodology to impute the sequence of activities elicited form smart-card data using a continuous hidden Markov model (CHMM).•The proposed model requires ...neither labeled data for training nor subsequent measurements such as prompted-recall surveys.•The present study showed the power of unsupervised machine-learning models.•Self-clustered activities and transition probabilities between them were fully validated by observed data.
Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
PDF
