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.

翻译：交通机构在部署或扩建匝道控制系统时，需要评估其性能表现。匝道控制策略的评估主要围绕其对高速公路交通通行能力的影响展开。探索此类影响的一种方式是比较交通状态，例如匝道控制策略变更前后的车速。预测实施新匝道控制策略后场景下的高速公路交通状态，可为评估目标策略的潜在有效性提供重要参考。然而，当前文献中采用机器学习方法预测新场景下的高速公路交通状态、评估交通政策或匝道控制等交通控制策略有效性的研究较为有限。为弥补这一研究空白，本研究提出了一种框架，用于预测实施新匝道控制策略后的高速公路交通参数（车速、占有率、流量）。该框架通过学习已知高速公路路段在变更前后交通状态时空特征之间的关联，可将此学习成果迁移至预测新路段交通参数。该框架基于迁移学习模型构建。实验结果表明，该框架可作为替代方法用于预测高速公路交通参数，从而主动评估匝道控制性能。