On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Framework

TL;DR

This paper introduces a data-driven transfer learning framework that accurately estimates missing on-ramp and off-ramp traffic flows using only freeway mainline loop detector data, aiding traffic management without physical sensors.

Contribution

The paper presents a novel transfer learning approach that relaxes distribution assumptions, enabling high-accuracy ramp flow estimation across diverse traffic patterns and locations.

Findings

Flow estimation MAE ranges between 23.90-40.85 veh/h for on-ramps.

Flow estimation MAE ranges between 31.58-45.31 veh/h for off-ramps.

The framework outperforms conventional machine learning models.

Abstract

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.