Mitigating the Impact of Light Rail on Urban Traffic Networks using Mixed Integer Linear Programming
Guilliard, I., Trevizan, F. and Sanner, S.
To appear in IET Intelligent Transport Systems.
We are working on the camera-ready of this paper and it will be available soon.
Bellow is the abstract of this paper.
As urban traffic congestion is on the increase worldwide, many cities are
increasingly looking to inexpensive public transit options such as light
rail that operate at street-level and require coordination with conventional
traffic networks and signal control. A major concern in light rail
installation is whether enough commuters will switch to it to offset the
additional constraints it places on traffic signal control and the resulting
decrease in conventional vehicle traffic capacity. In this paper, we study
this problem and ways to mitigate it through a novel model of optimized
traffic signal control subject to light rail schedule constraints solved in
a Mixed Integer Linear Programming (MILP) framework. Our key results show
that while this MILP approach provides a novel way to optimize fixed-time
control schedules subject to light rail constraints, it also enables a novel
optimized adaptive signal control method that virtually nullifies the impact
of the light rail presence, reducing average delay times in microsimulations
by up to 58.7% vs. optimal fixed-time control.