A bottom-up clustering approach to identify bus driving patterns and to develop bus driving cycles for Hong Kong

Hing Yan Tong*, Ka Wai Ng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Bus transport has been an important mode taking up a significant share of urban travel demand and thus the corresponding impacts on the environment are of great concerns. Use of driving cycles to evaluate the environmental impacts of buses has attracted much attention in recent years worldwide. The franchised bus service is currently playing important roles in the public transport system in Hong Kong; however, there is no driving cycle developed specifically for them. A set of bus driving cycle was therefore developed using a bottom-up approach where driving data on the bus network with mixed characteristics were collected. Using the Ward’s method for clustering, the collected data were then categorized into three clusters representing distinct franchised bus route patterns in Hong Kong. Driving cycles were then developed for each route pattern including (i) congested urban routes with closely spaced bus stops and traffic junctions; (ii) inter-district routes containing a number of stop-and-go activities and a significant portion of smoother high speed driving; and (iii) early morning express routes and mid-night routes connecting remote residential areas and urban areas. These cycles highlighted the unique low-speed and aggressive driving characteristics of bus transport in Hong Kong with frequent stop-and-go activities. The findings from this study would definitely be helpful in assessing the exhaust emissions, fuel consumptions as well as energy consumptions of bus transport. The bottom-up clustering approach adopted in this study would also be useful in identifying specific driving patterns based on vehicle speed trip data with mixed driving characteristics. It is believed that this approach is especially suitable for assessing fixed route public transport modes with mixed driving characteristics.

Original languageEnglish
Pages (from-to)14343-14357
JournalEnvironmental Science and Pollution Research
Issue number12
Early online date18 Nov 2020
Publication statusPublished - Mar 2021

Bibliographical note

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11356-020-11554-w


  • Bus driving patterns
  • Cluster analysis
  • Driving cycles
  • GPS data collection
  • Vehicle emissions and energy consumption
  • Vehicle specific power (VSP)


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