Classifying urban commuters by trip chaining behavior in a developing country city

• Four groups of commuters with distinct trip chaining characteristics. • The largest commuter group uses non-motorized modes in complex trip chains. • The smallest group of commuters makes simple trip chains with motorized transport. • Use of transit is typically associated with simple trip chains. • Complex trip chains are influenced by gender, marital status, and land-use mix. The complex urban life has prompted commuters to combine non-work activities with their work trips to save time and money, resulting in commute-based trip chains closely tied to mode choice. Dhaka, one of the world’s most densely populated megacities and the capital of Bangladesh, exhibits the potential for unique trip chaining patterns driven by its complex land use and extensive range of transport options. This study examines the trip chaining behavior of commuters in Dhaka, utilizing Latent Class Analysis (LCA) to categorize them based on their trip chaining and mode use characteristics. The analysis reveals four distinct commuter classes. Class 1 represents simple trip chain makers, traveling without additional stops between home and work, using motorized transport. They typically belong to middle- to high-income households with access to private vehicles. Class 2 is characterized by complex trip chain makers – individuals who combine work and non-work activities – primarily during the journey to home, utilizing motorized vehicles. Females and those from middle- to high-income households tend to fall into this group. Class 3 consists of simple trip chain makers who rely on public transit or non-motorized vehicles. These commuters are mainly low-income males without access to private vehicles. Finally, Class 4 represents complex trip chain makers using non-motorized transport and typically belong to middle-income households with no private vehicle access. The findings offer valuable insights into the complex travel behavior of commuters, aiding the development of effective travel demand management policies, particularly in addressing peak-hour traffic contributed by commuters.