Trip-pair based clustering model for urban mobility of bus passengers in Macao

ABSTRACT

Public transit is a major mode of urban mobility in Macao, a compact and populous city in China. Public transit ridership is related to round-the-clock shift-based work arrangements in the gaming industry. Smartcards that are used in transit fare collection are a vital data source for understanding the travel patterns and characteristics of transit passengers. However, transactional information from single data sources is fragmented, hindering the extraction of mobility patterns. We proposed a trip-pair-based clustering model that extracts the within-day mobility patterns of bus riders. DBSCAN is employed to filter scattered trips and hierarchical agglomerative clustering to identify clusters. Using a six-month smartcard dataset from Macao, categories of bus passengers were identified. Travel patterns exist with irregular, one-way, and round-trip passengers, and shift workers. The unique shift-based work-life and mixed travel modes in Macao can serve as an example of dense and compact city travel for smart cities.

KEYWORDS:

• Smartcard data
• clustering
• urban mobility
• regularity
• transit bus

Acknowledgement

The authors appreciate the anonymous reviewers for their constructive comments. The authors express their sincere appreciation to the Transport Bureau of Macau SAR for providing the valuable data.

Disclosure statement

No potential conflict of interest was reported by the author(s).