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

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 LinkNode 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 apriori state estimate and demand estimate. We present a detailed convergence analysis of our approach and finally demonstrate some examples of its application.