# From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

arXiv: https://arxiv.org/abs/2002.02210 · HTML: https://mlsanity.com/paper/2002.02210/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 9 of 9)

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- [Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis](https://mlsanity.com/paper/2210.10010/) (2022)
- [Adaptive Modeling of Uncertainties for Traffic Forecasting](https://mlsanity.com/paper/2303.09273/) (2023)
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- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis](https://mlsanity.com/paper/2510.11907/) (2025)
- [State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance](https://mlsanity.com/paper/2406.13117/) (2024)
- [A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles](https://mlsanity.com/paper/2201.04968/) (2022)
- [Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability](https://mlsanity.com/paper/2210.16049/) (2022)

## Abstract (arXiv)

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a ``story'' intensively producing and consuming large amounts of data. A~diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in~turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These~information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in~other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we~define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We~deliberately generalize model learning to be adaptive, since, in~the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we~provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.