Research Information System
Transportation Research Part B: Methodological, vol.106, pp.102-129, 2017 (SCI-Expanded)
Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation trans- portation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for schedul- ing longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell’s simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level re- action time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle’s speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions.