A Novel Approach to Estimating Missing Pairs of On/Off Ramp Flows

Abstract

A freeway stretch with even one pair of unmeasured on/off-ramps is not fully observable in traffic states. Flow observability is essential for freeway traffic modeling, surveillance, and control. It is a longstanding and tricky issue to estimate flows for unmeasured ramp pairs. This problem seems to be hardly tractable in conventional approaches, and this paper intends to handle it based on machine learning. The work was partially inspired by transfer learning. Consider that no measurements are available for a target ramp pair, and the knowledge about ramp flow estimation may be drawn from other (measured) ramp pairs, provided that measured and unmeasured ramp pairs would share similarities in some key traffic flow patterns. Two simple machine learning algorithms, random forest (RF) and gradient boosting machine (GBM), were employed to this end. RF and GBM were driven by real measurement data to establish models that relate ramp flows to adjacent mainstream traffic conditions. The models were then applied for our task. The estimation performance was evaluated using real measurement data from the Shanghai Urban Expressway and the Intercity Highway in California, with satisfactory results obtained.