On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Abstract

Freeway traffic engineering is an important area in modern intelligent transportation systems (ITS), where solutions are desperately needed to address the emergent societal and environmental problems caused by freeway traffic congestions. Due to the unavailability of land resources for constructing new freeway infrastructures, to improve the efficiency of existing freeway systems is not only a challenging research topic, but also a requirement to freeway system administrators. Freeway traffic modeling and control are the main topics in freeway traffic engineering. In particular, accurate freeway traffic modeling is the basis for design and analysis of freeway traffic control system, while efficient freeway traffic control is the ultimate objective of researches in freeway traffic systems. In this work, the attention is concentrated on learning based parameter calibration for macroscopic traffic flow modeling and design of learning control strategies for local and coordinated freeway ramp metering.

A hybrid iterative parameter calibration algorithm is first proposed for estimating the parameters of macroscopic freeway traffic models. This algorithm is a hybridization of the multivariate Newton-Raphson method and the simultaneous perturbation algorithm. Convergence of parameters is theoretical guaranteed and well demonstrated through applications with real traffic data and comparison with existing method. In particular, the simultaneous perturbation based gradient estimation scheme improves the parametric convergence in face of local minima.

An optimal freeway local ramp metering algorithm is then presented, which uses Fuzzy Logic Control (FLC) and Particle Swarm Optimization (PSO). The FLC based ramp metering algorithm effectively handles the freeway system uncertainties and randomness, and the fuzzy rule parameters are optimized through a microscopic traffic simulation based PSO algorithm. A novel Weighted Total Time Spent (WTTS) based cost function is introduced to measure the efficiency of freeway local ramp metering. By minimizing the WTTS, a balance between freeway mainstream traffic and on-ramp traffic is pursued, which has rarely been discussed. A Simultaneous Perturbation Stochastic Approximation (SPSA) based parameter learning scheme is then proposed to adaptively update the parameters of the FLC based local ramp metering algorithm without disturbing the normal freeway operations.

To address the networked freeway ramp metering problem, an FLC based Local Coordinative Ramp Metering (LCRM) algorithm is proposed. By LCRM, a ramp metering controller generates the local ramp metering signals based on not only its local traffic condition but also the traffic conditions at its neighboring controllers. Such an LCRM algorithm enables cooperation among neighboring ramp metering controllers, which effectively improves the efficiency of the overall traffic control system. Finally, we propose a Macroscopic Traffic Scheduling (MTS) method for networked freeway traffic control. The MTS method divides the considered time period of traffic control into intervals, within which reference mainstream densities are assigned to and tracked by the local ramp metering controllers. Using MTS method, the optimal networked freeway ramp metering problem is treated as an optimization problem. Performances of the LCRM and MTS algorithms are improved using the SPSA based parameter learning algorithm. Algorithmic simplicity, low system costs and improved efficiencies are the main contributions of these two methods.