Enhancing public transit adoption through personalized incentives: a large-scale analysis leveraging adaptive stacking extreme gradient boosting in China

Abstract

Motivating individuals to utilize public transportation through financial strategies, including both rewards and penalties, has been acknowledged as an effective approach to manage traffic demand and mitigate congestion-related issues. Personalized travel rewards, in contrast to economic sanctions like road tolls, tend to be more socially accepted. Nonetheless, insights into the effectiveness of personalized incentives remain limited, often constrained by studies that rely on small, non-representative samples of travelers. This study seeks to identify the variables that prompt individuals to switch to public transportation, drawing on extensive quasi-experimental data from a widespread public transit incentive program featured in one of China’s the largest navigation apps. This data encompasses the sociodemographic details of users, as well as their local and long-distance travel patterns. Both a binary Logit model and an adaptive stacking extreme gradient boosting (AS-XGB) model are applied to interpret and predict the changes in users’ public transit usage. Besides gender, job type and preferred travel mode, incentive reward category is found to be one of the significant determinants. In particular, rewards such as breakfast bread or travel vouchers have proven more effective than other types of incentives, like supermarket coupons or tissue gift bags. Female participants, individuals without children, and those who used public transportation in the week prior to receiving the incentives showed a higher propensity to embrace these rewards. However, the influence of education level, car ownership status, or preferred travel mode largely varies as the city’s development level. For intercity travel, regardless of whether the user owns a car or not, her/his income level and education level both have significant impacts on the incentive effectiveness.