25 research outputs found
Application of support vector machine in a traffic lights control
This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value
QarSUMO: A Parallel, Congestion-optimized Traffic Simulator
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.
In this paper, we develop QarSUMO, a parallel, congestion-optimized version
of the popular SUMO open-source traffic simulator. QarSUMO performs high-level
parallelization on top of SUMO, to utilize powerful multi-core servers and
enables future extension to multi-node parallel simulation if necessary. The
proposed design, while partly sacrificing speedup, makes QarSUMO compatible
with future SUMO improvements. We further contribute such an improvement by
modifying the SUMO simulation engine for congestion scenarios where the update
computation of consecutive and slow-moving vehicles can be simplified.
We evaluate QarSUMO with both real-world and synthetic road network and
traffic data, and examine its execution time as well as simulation accuracy
relative to the original, sequential SUMO
Urban Traffic Control Assisted by AI Planning and Relational Learning
Abstract Urban Traffic Control is a key problem for most big cities. An inefficient traffic control system can lead to increased traffic congestions that degrade city quality metrics such as average travel time or city pollution. Most common approaches focus on controlling traffic by appropriately setting traffic lights. Current systems in operation range from static control of traffic light phases to adaptive systems based on numeric models. In this paper, we propose an autonomic approach based on declarative automated planning to generate control plans only when the default behavior should be overridden. Planning is complemented with plan execution control and monitoring, replanning, as well as self-adaptive behavior using Relational Learning. Learning is used to anticipate the appearance of congestions and correctly solve them. Our system outperforms static approaches as well as a planning-based system that recently won a competition on autonomic behavior in Urban Traffic Control