Travel Mode Choice Prediction to Pursue Sustainable Transportation and Enhance Health Parameters Using R

Travel mode choice (TMC) prediction, improving health parameters, and promoting sustainable transportation systems are crucial for urban planners and policymakers. Past studies show the influence of health on activities, while several studies use multitasking activities and physical activity intensity to study the association between time use and activity travel participation (TU and ATP) and health outcomes. Limited studies have been conducted on the use of transport modes as intermediate variables to study the influence of TU and ATP on health parameters. Therefore, the current study aims to evaluate urban dependency on different transport modes used for daily activities and its influence on health parameters to promote a greener and healthier society and a sustainable transportation system. Pearson’s Chi-squared test was used for transport mode classification, and multinominal logit models were used for regression using R programming. A total of five models were developed for motorized, non-motorized, public transport, physical, and social health to study the correlation between transport modes and health parameters. The statistical analysis results show that socio-demographic and economic variables have a strong association with TMC in which younger, male, workers and high-income households are more dependent on motorized transport. It was found that a unit rise in high-income causes a 4.5% positive increase in motorized transport, whereas it negatively influences non-motorized and public transport by 4.2% and 2.2%, respectively. These insights might be useful for formulating realistic plans to encourage individuals to use active transport that will promote sustainable transportation systems and a healthier society.