2,028 research outputs found
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
Emergency vehicles (EMVs) play a crucial role in responding to time-critical
calls such as medical emergencies and fire outbreaks in urban areas. Existing
methods for EMV dispatch typically optimize routes based on historical
traffic-flow data and design traffic signal pre-emption accordingly; however,
we still lack a systematic methodology to address the coupling between EMV
routing and traffic signal control. In this paper, we propose EMVLight, a
decentralized reinforcement learning (RL) framework for joint dynamic EMV
routing and traffic signal pre-emption. We adopt the multi-agent advantage
actor-critic method with policy sharing and spatial discounted factor. This
framework addresses the coupling between EMV navigation and traffic signal
control via an innovative design of multi-class RL agents and a novel
pressure-based reward function. The proposed methodology enables EMVLight to
learn network-level cooperative traffic signal phasing strategies that not only
reduce EMV travel time but also shortens the travel time of non-EMVs.
Simulation-based experiments indicate that EMVLight enables up to a
reduction in EMV travel time as well as an shorter average travel time
compared with existing approaches.Comment: 19 figures, 10 tables. Manuscript extended on previous work
arXiv:2109.05429, arXiv:2111.0027
A Micro-Simulation Study of the Generalized Proportional Allocation Traffic Signal Control
In this paper, we study the problem of determining phase activations for
signalized junctions by utilizing feedback, more specifically, by measure the
queue-lengths on the incoming lanes to each junction. The controller we are
investigating is the Generalized Proportional Allocation (GPA) controller,
which has previously been shown to have desired stability and throughput
properties in a continuous averaged dynamical model for queueing networks. In
this paper, we provide and implement two discretized versions of the GPA
controller in the SUMO micro simulator. We also compare the GPA controllers
with the MaxPressure controller, a controller that requires more information
than the GPA, in an artificial Manhattan-like grid. To show that the GPA
controller is easy to implement in a real scenario, we also implement it in a
previously published realistic traffic scenario for the city of Luxembourg and
compare its performance with the static controller provided with the scenario.
The simulations show that the GPA performs better than a static controller for
the Luxembourg scenario, and better than the MaxPressure pressure controller in
the Manhattan-grid when the demands are low
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