# A Transfer Learning Framework for Proactive Ramp Metering Performance Assessment

arXiv: https://arxiv.org/abs/2308.03542 · HTML: https://mlsanity.com/paper/2308.03542/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction](https://mlsanity.com/paper/1701.04245/) (2017)
- [ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models](https://mlsanity.com/paper/2110.11960/) (2021)
- [Contrasting the landscape of contrastive and non-contrastive learning](https://mlsanity.com/paper/2203.15702/) (2022)
- [MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization](https://mlsanity.com/paper/2302.10418/) (2023)
- [Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze](https://mlsanity.com/paper/2008.05678/) (2020)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Self-Supervised Image Denoising for Real-World Images with Context-aware Transformer](https://mlsanity.com/paper/2304.01627/) (2023)
- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [DeepMTL Pro: Deep Learning Based MultipleTransmitter Localization and Power Estimation](https://mlsanity.com/paper/2112.13181/) (2021)
- [Particle-based Online Bayesian Sampling](https://mlsanity.com/paper/2302.14796/) (2023)
- [Experimental Resilience Assessment of An Open-Source Driving Agent](https://mlsanity.com/paper/1807.06172/) (2018)
- [Towards Reliable Item Sampling for Recommendation Evaluation](https://mlsanity.com/paper/2211.15743/) (2022)
- [TKIL: Tangent Kernel Approach for Class Balanced Incremental Learning](https://mlsanity.com/paper/2206.08492/) (2022)
- [Sampling Through the Lens of Sequential Decision Making](https://mlsanity.com/paper/2208.08056/) (2022)
- [High Dynamic Range Imaging with Context-aware Transformer](https://mlsanity.com/paper/2304.04416/) (2023)
- [Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance](https://mlsanity.com/paper/2303.02841/) (2023)

## Built on by (most cited first, 3 of 3)

- [LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2402.11417/) (2024)
- [Domain Adaptation Framework for Turning Movement Count Estimation with Limited Data](https://mlsanity.com/paper/2503.20113/) (2025)
- [Network-Level Vehicle Delay Estimation at Heterogeneous Signalized Intersections](https://mlsanity.com/paper/2510.01292/) (2025)

## Abstract (arXiv)

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.