Conservation-Based Modeling and Boundary Control of Congestion with an Application to Traffic Management in Center City Philadelphia
This paper develops a conservation-based approach to model traffic dynamics and alleviate traffic congestion in a network of interconnected roads (NOIR). We generate a NOIR by using the Simulation of Urban Mobility (SUMO) software based on the real street map of Philadelphia Center City. The NOIR is then represented by a directed graph with nodes identifying distinct streets in the Center City area. By classifying the streets as inlets, outlets, and interior nodes, the model predictive control (MPC) method is applied to alleviate the network traffic congestion by optimizing the traffic inflow and outflow across the boundary of the NOIR with consideration of the inner traffic dynamics as a stochastic process. The proposed boundary control problem is defined as a quadratic programming problem with constraints imposing the feasibility of traffic coordination, and a cost function defined based on the traffic density across the NOIR.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementModel Predictive ControlSimilar Papers 제목 키워드 기반
Boundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process
In this paper, we introduce a new conservation-based approach to model traffic dynamics, and apply the model predictive control (MPC) approach to control the boundary traffic inflow and outflow, so that the traffic conge…
ManagementModel Predictive ControlA Concurrent Switching Model for Traffic Congestion Control
We introduce a new conservation-based approach for traffic coordination modeling and control in a network of interconnected roads (NOIR) with switching movement phase rotations at every NOIR junction. For modeling of tra…
modelMitigating Stop-and-Go Traffic Congestion with Operator Learning
This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic …
Computational EfficiencyOperator learningControl-orientation, conservation of mass and model-based control of compressible fluid networks
We study a gas network flow regulation control problem showing the closed-loop consequences of using interconnected component models, which have been designed to preserve a variant of mass flow conservation without the i…
Physics-informed Neural-Network Software for Molecular Dynamics Applications
We have developed a novel differential equation solver software called PND based on the physics-informed neural network for molecular dynamics simulators. Based on automatic differentiation technique provided by Pytorch,…