Year: 2020
Type: conference-paper
Abstract: Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks.
Authors: Hao Chen, Ke Yang, Stefano Giovanni Rizzo, Giovanna Vantini, Phillip Taylor, Xiaosong Ma, Sanjay Chawla
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3091861989