Use of machine learning in understanding transport dynamics of land use and public transportation in a developing city

Rapid urbanization has resulted in problems with pollution, traffic congestion, and public transportation in cities worldwide. Much urbanization research disproportionately focuses on cities in developed countries, where data is easier to obtain, to the detriment of cities in developing countries. To help address this gap, our work proposes a machine learning methodology that explores how land use patterns contribute to predicting an aspect of public transportation in growing cities (aggregate public vehicle speeds), with Cauayan City in the Philippines as the test case. We trained gradient-boosting tree models with road properties, day and time, and land use data as features to predict vehicle speeds on various roads in the city. Among various feature combinations used in training, the best model used road, time, and land use features over a range buffer radii of 50 to 1000 m, yielding an R-2 of 0.676 (0.003) and a root mean squared error of 6.61 (0.03) kph. Apart from predicting speeds, Shapley's additive explanation (SHAP) values show that proximity to dense residential and commercial areas decreases predicted vehicle speeds. Our approach uses interpretable machine learning models to obtain insights about urban transportation in developing cities.