Public transport trajectory planning with probabilistic guarantees

Journal article, 2020

The paper proposes an eco-cruise control strategy for urban public transport
buses. The aim of the velocity control is ensuring timetable adherence, while
considering upstream queue lengths at traffic lights in a probabilistic way. The
contribution of the paper is twofold. First, the shockwave profile model (SPM)
is extended to capture the stochastic nature of traffic queue lengths. The model
is adequate to describe frequent traffic state interruptions at signalized intersections.
Based on the distribution function of stochastic traffic volume demand,
the randomness in queue length, wave fronts, and vehicle numbers are derived.
Then, an outlook is provided on its applicability as a full-scale urban traffic network
model. Second, a shrinking horizon model predictive controller (MPC) is
proposed for ensuring timetable reliability. The intention is to calculate optimal
velocity commands based on the current position and desired arrival time of the
bus while considering upcoming delays due to red signals and eventual queues.
The above proposed stochastic traffic model is incorporated in a rolling horizon
optimization via chance-constraining. In the optimization, probabilistic guarantees
are formulated to minimize delay due to standstill in queues at signalized intersections.
Optimization results are analyzed from two particular aspects, (i)
feasibility and (ii) closed-loop performance point of views. The novel stochastic
profile model is tested in a high fidelity traffic simulator context. Comparative
simulation results show the viability and importance of stochastic bounds in urban
trajectory design. The proposed algorithm yields smoother bus trajectories
at an urban corridor, suggesting energy savings compared to benchmark control
strategies.