•We proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders using the transit smart card data.•Transit riders’ trip chains are identified based on ...the temporal and spatial characteristics of smart card transaction data.•A Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit rider’s historical travel patterns.•The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities.
To mitigate the congestion caused by the ever increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. A better understanding of travel patterns and regularity at the “magnitude” level will enable transit authorities to evaluate the services they offer, adjust marketing strategies, retain loyal customers and improve overall transit performance. However, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. This paper proposes an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of their smart card transaction data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm then analyzes the identified trip chains to detect transit riders’ historical travel patterns and the K-Means++ clustering algorithm and the rough-set theory are jointly applied to cluster and classify travel pattern regularities. The performance of the rough-set-based algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed rough-set-based algorithm outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•We present three models to design demand-sensitive timetables for metro services.•We use smart card data to better understand spatial–temporal passenger demand.•The demand sensitive timetables are ...advantageous in reducing passenger cost.
Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Recently, He et al. proposed an anonymous two-factor authentication scheme following the concept of temporal-credential for wireless sensor networks (WSNs), which is claimed to be secure and capable ...of withstanding various attacks. However, we reveal that the authentication phase of their scheme has several pitfalls. Firstly, their scheme is susceptible to malicious user impersonation attack, in which a legal but malicious user can impersonate as other registered users. In addition, their scheme is also vulnerable to stolen smart card attack. Furthermore, the scheme cannot provide untraceability and is prone to tracking attack. Then we put forward an untraceable two-factor authentication scheme based on elliptic curve cryptography (ECC) for WSNs. Our new scheme makes up for the missing security features necessary for real-life applications while maintaining the desired features of the original scheme. We prove that the scheme fulfills mutual authentication in the Burrows-Abadi-Needham (BAN) logic. Moreover, by way of informal security analysis, we show that the proposed scheme can resist a variety of attacks and provide more security features than He et al.’s scheme.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Urban spatial structure in large cities is becoming ever more complex as populations grow in size, engage in more travel, and have increasing amounts of disposable income that enable them to live ...more diverse lifestyles. These trends have prominent and visible effects on urban activity, and cities are becoming more polycentric in their structure as new clusters and hotspots emerge and coalesce in a wider sea of urban development. Here, we apply recent methods in network science and their generalization to spatial analysis to identify the spatial structure of city hubs, centers, and borders, which are essential elements in understanding urban interactions. We use a 'big' data set for Singapore from the automatic smart card fare collection system, which is available for sample periods in 2010, 2011, and 2012 to show how the changing roles and influences of local areas in the overall spatial structure of urban movement can be efficiently monitored from daily transportation.
In essence, we first construct a weighted directed graph from these travel records. Each node in the graph denotes an urban area, edges denote the possibility of travel between any two areas, and the weight of edges denotes the volume of travel, which is the number of trips made. We then make use of (a) the graph properties to obtain an overall view of travel demand, (b) graph centralities for detecting urban centers and hubs, and (c) graph community structures for uncovering socioeconomic clusters defined as neighborhoods and their borders. Finally, results of this network analysis are projected back onto geographical space to reveal the spatial structure of urban movements. The revealed community structure shows a clear subdivision into different areas that separate the population's activity space into smaller neighborhoods. The generated borders are different from existing administrative ones. By comparing the results from 3 years of data, we find that Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city's master plan.
To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.
Full text
Available for:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
As the hub of urban railway transit, metro stations portray the skeleton structure of the public transit network. This study proposes a method of station classification from the dual perspectives of ...network structure and passenger flow. Each perspective considers the two aspects, one is the characteristics of the node itself, such as degree and the entrance and exit ridership; another considers the characteristics of the influence of other nodes, such as betweenness centrality and passing flow. Among them, the importance index of passing flow is calculated by the PageRank algorithm. According to these characteristics, metro stations are classified by k-means clustering algorithm after dimensionality reduction. The case study is conducted through nearly five million records from 278 stations in Beijing. From the classification results, stations are divided into six categories. Qualitative and quantitative regulations are proposed to reduce the risk of high ridership stations and improve the operation efficiency for few ridership stations.
•The PageRank algorithm is used to obtain the key nodes from the perspective of passenger flow.•When selecting characters, the influence of the neighbor nodes on the current node is considered.•After classification, different control methods which can guide the passenger flow towards a balanced direction are proposed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Investigating physical encounters among individuals is important for various applications such as infectious disease modeling and friend recommendation. As enclosed spaces, public transit systems ...(e.g., buses and metros) in densely populated areas are locations where physical encounters occur numerously. Currently, encounter networks in bus systems have been investigated with the help of smart card data (SCD); however, no attempt has been made toward the metro systems, which is more challenging as the travel behaviors of metro passengers are complex but not recorded in the SCD in detail. This study proposed a novel framework for investigating physical encounters of individuals in urban metro systems with SCD. First, we developed a method to match passengers to specific trains, which can allow the segmentation of individual trips inside a metro system. Second, we proposed an approach to measuring the encounter frequencies and durations of each passenger pair by synthesizing their encounter behaviors in not only the train space, but also the entering/exiting space and the transfer space. Finally, using the SCD of Shenzhen, China, we analyzed the physical encounter patterns at a population scale, and demonstrated the potential of applying the encounter network to trace the spread of infectious diseases. Overall, this study provided a framework for evaluating physical encounters in metro systems with SCD, and revealed the underlying physical encounter patterns in the metro system of a metropolitan city, which is of considerable application value.
•We made the first attempt to investigate physical encounters in urban metro systems.•We developed a method to segment passenger trips inside a metro system.•We revealed the statistical properties of physical encounters in a metro system.•We applied the derived encounter network in tracing the spread of infectious disease.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Though the collection of metro smart card data could help improve the operations of the metro system, the release of such data might lead to privacy issues. Few studies have quantified the ...probability to re-identify a user from the smart card data using very limited trajectory points. Thus, this study investigates this topic by analyzing eight-day metro smart card data of Chengdu, China. Results reveal that, on the macro level, three random trajectory points with a temporal resolution of one minute and one hour are enough to identify over 90% and 67% of the users. Even when the resolution is reduced to one day, 20% of the users could be still be identified by three points. On the individual level, three carefully selected points with a temporal resolution of one minute, one hour, and one day could lead to a re-identification risk no less than 0.5 for 99%, 89%, and 52% of the users. The effects of number of points, number of users, and other temporal resolutions are also thoroughly evaluated. These findings emphasize the great privacy issues involved in the release of metro smart card data and remind metro operators to take proactive measures to enhance privacy protection.
•Uniqueness and re-identification risk of metro users in trip data are quantified.•Three random trajectory points could identify over 90% of the users.•Three points could raise the re-identification risk of 99% of the users up to 0.5.•Effects of number of points, number of users, and temporal resolutions are evaluated.•Results reveal the privacy issues involved in the release of metro smart card data.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With the development of society and economy, the metro has become one of the essential components of the urban transportation system. Commuting passengers prefer the metro due to its punctual, high ...speeds and uncongested characteristics compared to private cars, taxis, bus, etc., especially in morning and evening rush hour. So, identifying metro commuters and mining its commuting mobility patterns play an essential role in improving service quality, promoting public transit use, and optimizing operational scheduling. We develop a method to mine metro commuting mobility patterns using massive smart card data. Firstly, we extracted individual daily regular OD (origin and destination) based on spatio-temporal similarity measurement from massive smart card data. The information entropy gain algorithm is used to further identify commuters from individual regular OD. Secondly, the station-oriented commute space model is built from space views. Metro stations are divided into employment, residential, and balanced type according to job-housing function pattern. They are divided into high efficiency, general, and low efficiency type according to commute efficiency pattern. Function pattern refers to the proportional relationship between the residence and employment land use around the rail station. Efficiency pattern is a comprehensive index to measure the commute time and distance. Finally, stations are clustered by the K-means method to determine what type they are. The experiment found that metro commuters accounted for 41% of the morning peak traffic using smart card data in Chongqing, China. Three typical job-housing function patterns and three commute efficiency patterns are discovered, respectively, and the characteristics of each are mined.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Estimating the large-scale Origin–Destination (OD) matrices for multi-modal public transport (PT) in different cities can vary largely based on the network itself, what modes exist, and what traffic ...data is available. In this study, to overcome the issue of traffic data unavailability and effectively estimate the demand matrix, we employ several data sets like the total boarding and alighting, smart card as well as the General Transit Feed Specification (GTFS) in order to capture the PT dynamic patronage patterns.
First, we propose a new method to model the dynamic large-scale stop-by-stop OD matrix for PT networks by developing a new enhancement of the Gravity Model via graph theory and Shannon’s entropy. Second, we introduce a method entitled “Entropy-weighted Ensemble Cost Features” that incorporates diverse sources of costs extracted from traffic states and the topological information in the network, scaled appropriately. Last, we compare the efficiency of a single travel cost versus various combinations of travel costs when using traditional methods like the Traverse Searching and the Hyman’s method, alongside our proposed “Entropy-weighted” method; we demonstrate the advantages of using topological features as travel costs and prove that our method, coupled with multi-modal PT OD matrix modelling, is superior to traditional methods in improving estimation accuracy, as evidenced by lower MAE, MAPE and RMSE, and reducing computing time.
•A new framework for dynamic and multi-mode stop-by-stop PT OD matrix estimation.•An extended Gravity Model boosted by a novel calibration method using Entropy-weighting.•Consider traffic characteristics and graph topological features for PT OD estimation.•An application of historical and big smart card data in PT OD matrix estimation.•An assessment of various feature combinations’ performance for PT OD estimation.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Introduction: Fever increases body temperature above 37.50 C, making the child uncomfortable; the child's body and face are hot, red, and shivers. Fever conditions also affect parents, especially ...mothers who will also feel anxious. This study aimed to determine the effect of the Mother's Smart Card on the knowledge and awareness of mothers in the management of children with fever in the pre-hospital phase.
Methods: This study used a Quasi-Experimental design with a pre-test and post-test approach with a purposive sampling technique on 40 mothers. A mother's knowledge is measured by knowledge about fever. In contrast, vigilance is measured by how the mother behaves and her accuracy in making decisions when her child has a fever. All measurements used a questionnaire.
Results: The results of data analysis using paired t-test showed a significant effect of the Mother's Smart Card on mother's knowledge and awareness in managing children with fever at home with a p-value of 0.000.
Conclusion: This means that the Mother's Smart Card can increase the knowledge and awareness of mothers in the management of children with fever in the pre-hospital phase. This result is hoped that the Mother’s Smart Card can be a reference in increasing mothers' knowledge and awareness and minimizing treatment in inappropriate health facilities that can be at risk of transmitting other diseases.
