  To develop the most appropriate control strategy and monitor, maintain, and
evaluate the traffic performance of the freeway weaving areas, state and local
Departments of Transportation need to have access to traffic flows at each pair
of on-ramp and off-ramp. However, ramp flows are not always readily available
to transportation agencies and little effort has been made to estimate these
missing flows in locations where no physical sensors are installed. To bridge
this research gap, a data-driven framework is proposed that can accurately
estimate the missing ramp flows by solely using data collected from loop
detectors on freeway mainlines. The proposed framework employs a transfer
learning model. The transfer learning model relaxes the assumption that the
underlying data distributions of the source and target domains must be the
same. Therefore, the proposed framework can guarantee high-accuracy estimation
of on-ramp and off-ramp flows on freeways with different traffic patterns,
distributions, and characteristics. Based on the experimental results, the flow
estimation mean absolute errors range between 23.90 veh/h to 40.85 veh/h for
on-ramps, and 31.58 veh/h to 45.31 veh/h for off-ramps; the flow estimation
root mean square errors range between 34.55 veh/h to 57.77 veh/h for on-ramps,
and 41.75 veh/h to 58.80 veh/h for off-ramps. Further, the comparison analysis
shows that the proposed framework outperforms other conventional machine
learning models. The estimated ramp flows based on the proposed method can help
transportation agencies to enhance the operations of their ramp control
strategies for locations where physical sensors are not installed.

Machine Learning