  Transportation agencies need to assess ramp metering performance when
deploying or expanding a ramp metering system. The evaluation of a ramp
metering strategy is primarily centered around examining its impact on freeway
traffic mobility. One way these effects can be explored is by comparing traffic
states, such as the speed before and after the ramp metering strategy has been
altered. Predicting freeway traffic states for the after scenarios following
the implementation of a new ramp metering control strategy could offer valuable
insights into the potential effectiveness of the target strategy. However, the
use of machine learning methods in predicting the freeway traffic state for the
after scenarios and evaluating the effectiveness of transportation policies or
traffic control strategies such as ramp metering is somewhat limited in the
current literature. To bridge the research gap, this study presents a framework
for predicting freeway traffic parameters (speed, occupancy, and flow rate) for
the after situations when a new ramp metering control strategy is implemented.
By learning the association between the spatial-temporal features of traffic
states in before and after situations for known freeway segments, the proposed
framework can transfer this learning to predict the traffic parameters for new
freeway segments. The proposed framework is built upon a transfer learning
model. Experimental results show that the proposed framework is feasible for
use as an alternative for predicting freeway traffic parameters to proactively
evaluate ramp metering performance.
