On freeway traffic density estimation for a jump Markov linear model based on Daganzo's cell transmission model

2010 ◽  
Author(s):  
Katerina Stankova ◽  
Bart De Schutter
Keyword(s):  
Linear Model ◽  
Density Estimation ◽  
Transmission Model ◽  
Traffic Density ◽  
Cell Transmission Model ◽  
Freeway Traffic ◽  
Model Based ◽  
Cell Transmission

Analysis of an Adaptive Iterative Learning Algorithm for Freeway Ramp Flow Imputation

2011 ◽  
Author(s):  
Ajith Muralidharan ◽  
Roberto Horowitz
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Iterative Learning ◽  
Equivalent Model ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Effective Demand ◽  
Freeway Traffic ◽  
Flow Profiles ◽  
Cell Transmission

We present an adaptive iterative learning based flow imputation algorithm, to estimate missing flow profiles in on ramps and off ramps using a freeway traffic flow model. We use the Link-Node Cell transmission model to describe the traffic state evolution in freeways, with on ramp demand profiles and off ramp split ratios (which are derived from flows) as inputs. The model based imputation algorithm estimates the missing flow profiles that match observed freeway mainline detector data. It is carried out in two steps: (1) adaptive iterative learning of an “effective demand” parameter, which is a function of ramp demands and off ramp flows/ split ratios; (2) estimation of on ramp demands/ off ramp split ratios from the effective demand profile using a linear program. This paper concentrates on the design and analysis of the adaptive iterative learning algorithm. The adaptive iterative learning algorithm is based on a multi-mode (piecewise non-linear) equivalent model of the Link-Node Cell transmission model. The parameter learning update procedure is decentralized, with different update equations depending on the local a-priori state estimate and demand estimate. We present a detailed convergence analysis of our approach and finally demonstrate some examples of its application.

Freeway Ramp Metering Based on Cell Transmission Model and Single Neuron

2013 ◽  
Vol 423-426 ◽  
pp. 2877-2881
Author(s):  
Yan Di Ye ◽  
Yan Yan Liu ◽  
Xin Rong Liang ◽  
Chao Jun Dong
Keyword(s):  
Traffic Congestion ◽  
Single Neuron ◽  
Learning Algorithm ◽  
Ramp Metering ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Freeway Traffic ◽  
Tracking Process ◽  
Cell Transmission ◽  
Control Objective

In this work, we apply single neuron method to relieve freeway traffic congestion. We consider a freeway composed of cells and entry/exit ramps, and formulate the ramp metering problem as a density tracking process. The cell transmission model (CTM) is firstly formulated and ramp control objective is determined. Based on CTM and single neuron, a freeway ramp metering system is then designed, and the learning algorithm of single neuron is given in detail. Finally, the ramp metering system is simulated in MATLAB software. The results show that this method can effectively deal with this class of control problem, and can achieve a perfect density tracking performance. This ramp metering can eliminate traffic congestion and maintain traffic flow stability.

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