A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method

He, Li; Agard, Bruno; Trépanier, Martin

doi:10.1080/23249935.2018.1479722

Cited by 62 publications

(30 citation statements)

References 38 publications

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“…Antwi et al (2020) [32] compared the traffic performance indicators between public transport and private transport based on a survey in Oforikrom and suggested that transport modes and travel time dynamics should be considered together. He et al (2020) [33] estimated a PT crowding evaluation based on the smart card data of trams and buses in Europe and indicated that infrequent passengers ignore crowding when they make route choices. Tang et al (2020) [34] proposed a hybrid method combining a fuzzy rough set and a fuzzy neural network for the imputation of missing traffic data, which can improve the data quality of PT data.…”

Section: Public Transportationmentioning

confidence: 99%

“…Based on Formulas (33) and (41), the individual value of the passenger for the fare factor during the time period t 1 ∼ t m can be calculated by Formula (42):…”

Section: Passenger Utility Analysesmentioning

confidence: 99%

“…In the sample, there are two subjects related to factor F, including Pas and Dri. The fare values are set using Formulas (33) and 34:…”

mentioning

confidence: 99%

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Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Gui

2020

Sustainability

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The transportation utility values calculated by traditional utility methods are not comprehensive. Some objects and factors are ignored in traditional utility methods, and this narrow perspective is their primary drawback. In intelligent transportation systems, it is necessary to calculate transportation utility for promoting public traffic planning and management. To build a sustainable intelligent transportation system, modified utility methods are essential to analyze transportation utility in a comprehensive way with innovative technologies and efficient communication systems. To solve the disadvantages of traditional utility methods, it is necessary to establish a new method to build sustainable public transport in the future. In this study, the Multiple Utility Method and Transportation Utility Method are proposed for public transport planning and management from multiple perspectives. A sample is presented to provide a better description, and 69,174 GPS-equipped taxi data in Haikou are adopted for the application of the Transportation Utility Method. The results show that the transportation utility values calculated by the Transportation Utility Method are more comprehensive than the transportation utility calculated by traditional utility methods. This indicates that it is necessary to calculate transportation utility from multiple perspectives based on the Transportation Utility Method. Future directions could include improving the methods, considering more factors, expanding the data used, and extrapolating this research to other cities around the world with similar urban metrics and urban form.

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Section: Public Transportationmentioning

confidence: 99%

“…Based on Formulas (33) and (41), the individual value of the passenger for the fare factor during the time period t 1 ∼ t m can be calculated by Formula (42):…”

Section: Passenger Utility Analysesmentioning

confidence: 99%

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Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Gui

2020

Sustainability

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“…For example, for the two travel record vectors u and v, if v i = 0 and u i ≠ 0, the cosine distance of u and v at the ith position ∝ v i * u i = 0, which still neglect the travel frequency difference at the ith time point. The CCD has also been used to measure similarity/distance between two sequences/vectors by shifting one sequence to find a maximum correlation with another sequence [24]. However, due to the shifting mechanism, the calculation process of CCD almost gets rid of the relative position information of elements in two sequences.…”

Section: Definition Of Distance Between Smart Card Recordsmentioning

confidence: 99%

Perspectives on stability and mobility of transit passenger's travel behaviour through smart card data

Cui

Long

2019

IET Intelligent Transport Systems

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Existing studies have extensively used temporalspatial data to mining the mobility patterns of different kinds of travelers. Smart Card Data (SCD) collected by the Automated Fare Collection (AFC) systems can reflect a general view of the mobility pattern of the whole bus and metro riders in urban area. Since the mobility and stability are temporally and spatially dynamic and therefore difficult to measure, few work focuses on the transition of their travel pattern between a long time interval. In this paper, an overview of the relation between stability and regularity of public transit riders based on SCD of Beijing is presented first. To analyze the temporal travel pattern of urban residents, travelers are classified into two categories, extreme and non-extreme travelers. We have two lines for profiling all cardholders, rule-based approach for extreme and improved density-based clustering method for nonextreme. Similar clusters are aggregated according their features of regularity and occasionality. By combining transition matrix of passenger's temporal travel pattern and socioeconomic data of Beijing in the year of 2010 and 2014, several analyses about resident's temporal mobility and stability are presented to shed lights on the interdependence between stability and mobility in the time dimension. The results indicate that passengers' regularity is hard to predict, extreme travel patterns are more vulnerable and overall non-extreme travel patterns nearly stay the same.

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“…Travel pattern analysis is useful for understanding the travel demands of people in different socioeconomic groups. He, Agard, and Trépanier (2019) proposed a time-series classification algorithm for mining the travel patterns of public transit users based on smart card data. The proposed algorithm employs cross-correlation distance to evaluate pattern similarity among users and adopts the hierarchical clustering technique to classify similar users into sub-groups.…”

mentioning

confidence: 99%

Special Issue on Spatiotemporal Big Data Analytics for Transportation Applications

Chen

Kwan

2020

Transportmetrica A: Transport Science

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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

Cited by 62 publications

References 38 publications

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Perspectives on stability and mobility of transit passenger's travel behaviour through smart card data

Special Issue on Spatiotemporal Big Data Analytics for Transportation Applications

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