Semi-autonomous bus platooning service optimization with surrogate modeling

Abstract

Autonomous driving holds great promise for easing the driver shortage issue that has occurred in many countries. In this paper, we consider a semi-autonomous transit system where (1) individual buses could form platoons to increase bus capacity during peak hours and operate separately during off-peak hours; and (2) buses may drive autonomously in certain geofenced areas yet need to be guided by human drivers elsewhere (e.g., SAE level 4, semi-autonomous). We model buses driving autonomously within the geofenced area as short-turn bus service, and jointly optimize bus dispatch headway, platoon size and service type through an integer programming model to address the driver shortage issue. The proposed model considers real-world and micro-level bus operation processes for mixed traffic of short-turn and full route buses, with bus fleet and driver workforce constraints being modeled endogenously. Since the model is non-linear, we further develop a radial basis function-based surrogate framework to solve the model efficiently. Experimental results show that, compared to the conventional bus service that uses fixed bus capacity only, semi-autonomous bus platooning service reduces operation cost significantly while reducing passenger wait time. System performance variations of the proposed model under different driver availabilities are also examined.