.png){kind=icon}
Return
Passenger Segmentation Using Smart Card Data
.png){kind=icon}
OA
AI
DOI：10.1109/TITS.2014.2368998.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.
Keywords:
Automated fare collection (AFC) system
market segmentation
public transport
smart cards (SCs)
transit passenger
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.4
Papers:
9.7K
Citations:
6.3W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSavePsychological characteristics and biofeedback mitigation in preadolescents with eating disorders
PREAI
ShareSaveShareSave
View more