Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus

Customized commuter bus (CCB) is a kind of innovative public transit service launched starting in 2013 in many cities all over the world. It is designed to meet the direct travel demands of commuters who have similar origin and destination locations during their long-distance commuting trips at peak...

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Vydáno v:IEEE transactions on intelligent transportation systems Ročník 20; číslo 9; s. 3351 - 3360
Hlavní autoři: Qiu, Guo, Song, Rui, He, Shiwei, Xu, Wangtu, Jiang, Min
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Clustering algorithms
Commuting
Customized commuter bus
density-based spatial clustering algorithm
Estimation
Intelligent transportation systems
origin-destination distribution
Passengers
Planning
potential passengers
Prediction algorithms
Public transportation
smart card data
Smart cards
Urban areas
ISSN:1524-9050, 1558-0016
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Shrnutí:Customized commuter bus (CCB) is a kind of innovative public transit service launched starting in 2013 in many cities all over the world. It is designed to meet the direct travel demands of commuters who have similar origin and destination locations during their long-distance commuting trips at peak hours. To identify the origin and destination distribution of potential CCB passengers, this paper proposes a pair wise density-based spatial clustering algorithm. With the proposed algorithm, the method to extract the potential CCB passengers from regular bus passengers based on the bus smart card data is introduced. Meanwhile, the discovered hot locations of potential CCB passengers can be regarded as the candidate locations of CCB stops and can be used to set candidate CCB lines. Finally, the demand survey data collected from the actual registered CCB passengers are applied to verify the accuracy and feasibility of the clustering results obtained by the proposed algorithm. The related findings could guide bus operators to design CCB lines and allocate vehicle capacities on different lines.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2875466