TY  - JOUR
PY  - 2017//
TI  - Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models
JO  - Transportation research part B: methodological
A1  - Wei, Yuguang
A1  - Avcı, Cafer
A1  - Liu, Jiangtao
A1  - Belezamo, Baloka
A1  - Aydın, Nizamettin
A1  - Li, Pengfei(Taylor)
A1  - Zhou, Xuesong
SP  - 102
EP  - 129
VL  - 106
IS  - 
N2  - Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling 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 reaction 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0191-2615
UR  - http://dx.doi.org/10.1016/j.trb.2017.10.012
ID  - ref1
ER  -