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0 · What Is Data Mining? Meaning, Techniques, Examples
1 · &Smart Card Data Mining of Public Transport Destination: A
2 · Mining smart card data to estimate transfer passenger flow in a
3 · Mining metro commuting mobility patterns using massive smart
4 · Data Mining Examples: Most Common Applications of Data
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Smart card data is increasingly used to investigate passenger behavior and the demand . An accurate estimation of transfer passenger flow can help improve the . We develop a method to mine metro commuting mobility patterns using .Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data.
An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data-driven methodology for estimating the transfer passenger flow volume of each transfer station . 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.This paper uses a probabilistic topic model for smart card data destination estimation and travel pattern mining. We establish a three-dimensional LDA model than captures the time, origin, and destination attributes in smart card trips. The smart card data from Beijing subway in China is used to validate the effectiveness of the proposed approaches. Results show that 88.7% of passengers’ home locations and four types of trip purposes (six subtypes) can be detected effectively by mining the card transaction data in one week.
What Is Data Mining? Meaning, Techniques, Examples
In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. This study develops a series of data mining methods to identify the spatiotemporal commuting patterns of Beijing public transit riders. Using one-month transit smart card data, we measure spatiotemporal regularity of individual commuters, .
Such data barriers hinder the development of a large-scale transit performance monitoring system. This study attempts to fill these research gaps by developing a series of data mining algorithms for transit rider's origin and destination information extraction . An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data-driven methodology for estimating the transfer passenger flow volume of each transfer station .Research On Smart Card Data Mining for Multi-Modal Public Transit | Guide books. Author: Hao Siyu, Advisor: + 1. Publisher: National University of Singapore (Singapore) ISBN: 979-8-3526-8570-9. Order Number: AAI29352773. Purchase on ProQuest. Save to Binder Export Citation. Bibliometrics. Downloads (cumulative) 0. Citation count. 0.Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data.
An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data-driven methodology for estimating the transfer passenger flow volume of each transfer station . 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.This paper uses a probabilistic topic model for smart card data destination estimation and travel pattern mining. We establish a three-dimensional LDA model than captures the time, origin, and destination attributes in smart card trips. The smart card data from Beijing subway in China is used to validate the effectiveness of the proposed approaches. Results show that 88.7% of passengers’ home locations and four types of trip purposes (six subtypes) can be detected effectively by mining the card transaction data in one week.
In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. This study develops a series of data mining methods to identify the spatiotemporal commuting patterns of Beijing public transit riders. Using one-month transit smart card data, we measure spatiotemporal regularity of individual commuters, .Such data barriers hinder the development of a large-scale transit performance monitoring system. This study attempts to fill these research gaps by developing a series of data mining algorithms for transit rider's origin and destination information extraction .
An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data-driven methodology for estimating the transfer passenger flow volume of each transfer station .
&Smart Card Data Mining of Public Transport Destination: A
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Mining smart card data to estimate transfer passenger flow in a
Mining metro commuting mobility patterns using massive smart
Data Mining Examples: Most Common Applications of Data
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smart card data mining|Mining smart card data to estimate transfer passenger flow in a