An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis

Smart card data provide valuable insights and massive samples for enhancing the understanding of transfer behavior between metro and public bicycle. However, smart cards for metro and public bicycle are often issued and managed by independent companies and this results in the same commuter having different identity tags in the metro and public bicycle smart card systems. The primary objective of this study is to develop a data fusion methodology for matching metro and public bicycle smart cards for the same commuter using historical smart card data. A novel method with association rules to match the data derived from the two systems is proposed and validation was performed. The results showed that our proposed method successfully matched 573 pairs of smart cards with an accuracy of 100%. We also validated the association rules method through visualization of individual metro and public bicycle trips. Based on the matched cards, interesting findings of metro-bicycle transfer have been derived, including the spatial pattern of the public bicycle as first/last mile solution as well as the duration of a metro trip chain.

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Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

Applied Sciences ◽

10.3390/app9173597 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3597 ◽

Cited By ~ 1

Author(s):

Zilin Huang ◽

Lunhui Xu ◽

Yongjie Lin ◽

Pan Wu ◽

Bin Feng

Keyword(s):

Data Fusion ◽

Smart Card ◽

Smart Cards ◽

P Value ◽

Gps Data ◽

Fusion Method ◽

Transfer Time ◽

Time Threshold ◽

Smart Card Data ◽

Combined Data

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

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Public Transportation OD Calculation Based on Trip Chain

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.401-403.2151 ◽

2013 ◽

Vol 401-403 ◽

pp. 2151-2154

Author(s):

Lai Ping Luo ◽

Jing Zhang

Keyword(s):

Smart Card ◽

Public Transportation ◽

Bus Stop ◽

Trip Chain ◽

Smart Card Data ◽

Card System

Public transportation smart card system in China has been widely used in many cities recently. Large amounts of information implicit in the smart card, but it is not completely applied, because the information is incomplete, such as the information of getting-off bus stop. For this problem, a method is proposed to calculate OD (Origin-Destination) of smart card data. And it is well applied in digging the information of getting-off bus stop.

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Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052711 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2711

Author(s):

Xintao Liu ◽

Ziwei Lin ◽

Jianwei Huang ◽

He Gao ◽

Wenzhong Shi

Keyword(s):

Smart Card ◽

Public Transport ◽

Urban Areas ◽

Medical Service ◽

Potential Model ◽

House Price ◽

Spillover Effect ◽

The Public ◽

Service Accessibility ◽

Smart Card Data

The measurement of medical service accessibility is typically based on driving or Euclidean distance. However, in most non-emergency cases, public transport is the travel mode used by the public to access medical services. Yet, there has been little evaluation of the public transport system-based inequality of medical service accessibility. This work uses massive real smart card data (SCD) and an improved potential model to estimate the public transport-based medical service accessibility in Beijing, China. These real SCD data are used to calculate travel costs in terms of time and distance, and medical service accessibility is estimated using an improved potential model. The spatiotemporal variations and patterns of medical service accessibility are explored, and the results show that it is unevenly spatiotemporally distributed across the study area. For example, medical service accessibility in urban areas is higher than that in suburban areas, accessibility during peak periods is higher than that during off-peak periods, and accessibility on weekends is generally higher than that on weekdays. To explore the association of medical service accessibility with socio-economic factors, the relationship between accessibility and house price is investigated via a spatial econometric analysis. The results show that, at a global level, house price is positively correlated with medical service accessibility. In particular, the medical service accessibility of a higher-priced spatial housing unit is lower than that of its neighboring spatial units, owing to the positive spatial spillover effect of house price. This work sheds new light on the inequality of medical service accessibility from the perspective of public transport, which may benefit urban policymakers and planners.

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Algorithm of Estimating Alighting Bus Stops of Smart Card Passengers Based on Trip-Chain

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1918 ◽

2012 ◽

Vol 253-255 ◽

pp. 1918-1921

Author(s):

Jun Chen ◽

Zhao Fei Wang

Keyword(s):

Smart Card ◽

Public Transportation ◽

Large Scale ◽

Estimation Algorithm ◽

Transportation Systems ◽

Trip Chain ◽

Smart Card Data ◽

Bus Stops ◽

Public Transportation Planning ◽

Public Transportation Systems

In order to apply smart card data in decision-making of public transportation planning and management, the paper researched estimating method of alighting bus stops of smart card passengers. Based on Trip-chain thought, the paper presented estimation algorithm applying the three hypotheses of “Next Trip”, “Last Trip” and “Return Trip”. Then, the algorithm was tested and analyzed using large-scale actual data of Advanced Public Transportation Systems of Nanning City in China. The results show that Trip-chain Method can estimate majority of alighting bus stops.

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Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Mathematical Problems in Engineering ◽

10.1155/2019/5070794 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Hamed Faroqi ◽

Mahmoud Mesbah ◽

Jiwon Kim

Keyword(s):

Smart Card ◽

Public Transit ◽

Similarity Measures ◽

Spatial And Temporal Patterns ◽

Transit Network ◽

The Public ◽

Temporal Space ◽

Smart Card Data ◽

Transit Services ◽

Spatiotemporal Clustering

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

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Assessing the public transport travel behavior consistency from smart card data

Transportation Research Procedia ◽

10.1016/j.trpro.2018.10.008 ◽

2018 ◽

Vol 32 ◽

pp. 44-53 ◽

Cited By ~ 4

Author(s):

Catalina Espinoza ◽

Marcela Munizaga ◽

Benjamin Bustos ◽

Martin Trépanier

Keyword(s):

Smart Card ◽

Public Transport ◽

Travel Behavior ◽

The Public ◽

Smart Card Data ◽

Behavior Consistency

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Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Journal of Advanced Transportation ◽

10.1155/2017/1318945 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 12

Author(s):

Hamed Faroqi ◽

Mahmoud Mesbah ◽

Jiwon Kim

Keyword(s):

Similarity Measure ◽

Smart Card ◽

Public Transit ◽

Pearson Correlation ◽

Similarity Measures ◽

Correlation Coefficients ◽

The Public ◽

Spatial Similarity ◽

Smart Card Data ◽

One Step

The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

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