Estimating the effect of semi-transparent low-height road traffic noise barriers with ultra weak variational formulation

The ultra weak variational formulation (UWVF) approach is used to study the effect of semi-transparent road traffic noise barriers of limited height. This numerical method is extended to simulate sound propagation through a porous medium, based on the Zwicker and Kosten phenomenological porous rigid-frame model. An efficient approach to calculate noise levels in multi-lane road traffic noise situations is presented. The UWVF method was validated successfully by comparison with finite-difference time-domain (FDTD) calculations, for the case of sound propagation near a porous, low-height, and complex shaped noise barrier, and for sound propagation above porous ground in a refracting atmosphere. An assessment is made of the shielding of various porous low-height noise barriers for people on the pavement along the road. Porous barriers were shown to improve noise shielding when compared to geometrically identical rigid noise barriers

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape