Mapping Bangkok's Informal Transit: Geospatial Insights for Smart Mobility

Abstract

This study examines the spatial distribution of motorcycle taxis in Bangkok, Thailand, contributing to the understanding of informal transportation systems within smart cities. Despite their significant role in urban mobility, particularly in developing countries, informal transportation modes are often overlooked in smart city planning. Using data from 3,101 motorcycle taxi locations across 27 districts, this research employs advanced geospatial analysis techniques, including Kernel Density Estimation (KDE) and Local Indicators of Spatial Association (LISA), to analyze spatial patterns. The study utilizes an innovative data collection methodology combining Google Street View surveys with open city data. Results reveal a fivefold higher density of motorcycle taxis in commercial zones compared to residential areas, with a strong positive correlation between motorcycle taxi distribution and population density (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r=0.700$</tex>, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}&lt;.001$</tex>). The proximity to public transit stops also shows a positive relationship with taxi density (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{r}=0.522, \mathrm{p}&lt;.01$</tex>). LISA analysis identifies statistically significant High-High clusters in southwestern commercial areas and Low-Low clusters in southeastern residential zones. These findings provide valuable insights for urban planners and policymakers, highlighting the need to integrate informal transportation into smart city strategies. The methodologies presented offer a replicable framework for analyzing informal transportation patterns in urban contexts, potentially informing more inclusive and efficient smart city development globally.