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A classification of public transit users with smart card data based on time series distance .
In this work, a methodology is developed to classify smart card users' behaviors . Smart card automated fare collection systems are being used more and more by .A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method
In this work, a methodology is developed to classify smart card users' behaviors based on dynamic time warping (DTW), hierarchical clustering and a sampling method. A three-dimensional space–time prism plot demonstrates the efficiency of the algorithm. Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions.A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method. A classification of the behaviour of smart card.
Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions.The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems, and offers several targeted measures to improve transfer efficiency.Smart card data from public transit systems has proven to be useful to understand the behaviors of public transit users. Relevant research has been done concerning: (1) the utilization of smart card data, (2) data mining techniques and (3) the uti-lization of data mining in smart card data.Pelletier, M. P., Trépanier, M., Morency, C. Smart Card Data Use in Public Transit: A Literature Review. Transportation Research Part C: Emerging Technologies, Vol. 19, No. 4, 2011 , pp. 557 – 568 .
User behaviour is a quite large research field, and this paper will focus mainly on weekday’s trips habits. In the literature review, we will expose some works on smart card data analysis in public transport. We will recall some references in data mining advances. Smart card automated fare collection systems have been effective for the collection of data about the travel behavior of users on public transit networks. Because some systems record only the boarding (origin) locations, a method is needed for estimating the alighting (destination) locations.A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method
In this work, a methodology is developed to classify smart card users' behaviors based on dynamic time warping (DTW), hierarchical clustering and a sampling method. A three-dimensional space–time prism plot demonstrates the efficiency of the algorithm.
Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions.
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A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method. A classification of the behaviour of smart card.
Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions.The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems, and offers several targeted measures to improve transfer efficiency.Smart card data from public transit systems has proven to be useful to understand the behaviors of public transit users. Relevant research has been done concerning: (1) the utilization of smart card data, (2) data mining techniques and (3) the uti-lization of data mining in smart card data.
Pelletier, M. P., Trépanier, M., Morency, C. Smart Card Data Use in Public Transit: A Literature Review. Transportation Research Part C: Emerging Technologies, Vol. 19, No. 4, 2011 , pp. 557 – 568 .User behaviour is a quite large research field, and this paper will focus mainly on weekday’s trips habits. In the literature review, we will expose some works on smart card data analysis in public transport. We will recall some references in data mining advances.
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