This study tackles inefficient urban mobility in Indian cities by showing how bus trip rate (trip generation) modelling can strengthen public transit planning and mitigate congestion and pollution. Predictive models are developed across multiple Indian cities using multiple linear regression (MLR) and artificial neural networks (ANN), finding that ANN consistently achieves higher predictive accuracy – especially in high-density contexts – by capturing non-linear relationships in mobility patterns. Sensitivity analysis highlights trip purpose and age as dominant predictors: bus reliance varies by trip purpose, while ridership declines with age, particularly among older populations. Population density positively influences bus use, underscoring stronger dependence on public transport in dense areas. Gender also proves significant, reinforcing the need for safer, more accessible, gender-inclusive systems to enhance women’s mobility. These results motivate targeted interventions – route optimisation tailored to diverse travel needs, improved access for vulnerable users and gender-sensitive policies – alongside strengthening networks in dense corridors, integrating inclusive infrastructure and aligning with national sustainability agendas (e.g., Smart Cities Mission, National Action Plan on Climate Change). To address prior limitations, a generalised model is proposed that integrates common socio-demographic drivers in developing-economy contexts and is applicable across cities of varying sizes.
Chauhan et al. (Fri,) studied this question.