Version 1
: Received: 28 July 2023 / Approved: 31 July 2023 / Online: 1 August 2023 (11:18:51 CEST)
How to cite:
Ferilli, S.; Di Pierro, D.; Redavid, D.; Bernasconi, E. An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding. Preprints2023, 2023080062. https://doi.org/10.20944/preprints202308.0062.v1
Ferilli, S.; Di Pierro, D.; Redavid, D.; Bernasconi, E. An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding. Preprints 2023, 2023080062. https://doi.org/10.20944/preprints202308.0062.v1
Ferilli, S.; Di Pierro, D.; Redavid, D.; Bernasconi, E. An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding. Preprints2023, 2023080062. https://doi.org/10.20944/preprints202308.0062.v1
APA Style
Ferilli, S., Di Pierro, D., Redavid, D., & Bernasconi, E. (2023). An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding. Preprints. https://doi.org/10.20944/preprints202308.0062.v1
Chicago/Turabian Style
Ferilli, S., Domenico Redavid and Eleonora Bernasconi. 2023 "An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding" Preprints. https://doi.org/10.20944/preprints202308.0062.v1
Abstract
Studying and understanding the behavior of people and vehicles on public roads can be of utmost importance for supporting the activities of many institutional stakeholders. It may allow automated supervision of the ongoing situation in a given place, with warnings or alarms raised in case of anomalies. It may be used to plan their interventions on road and town organization. It may provide them with advanced support to decision-making. The number of involved entities and places to manage makes it infeasible to manually handle all the traffic-related tasks. Moreover, the complexity of the tasks to be carried out requires the adoption of advanced approaches. Many AI solutions are nowadays mature to support these requirements. In some cases, the motivations and objectives of traffic management require the AI outcomes to be understandable, interpretable and explainable. In this paper, we propose TrAnSIT (TRaffic ANalysis Supervision and Interpretation Tool), an AI-based framework that combines several modules, each aimed at tackling a specific traffic-related task, so as to cover a wide landscape of traffic-related issues, from overall urban or suburban traffic management to surveying specific road segments that fall under the scope of one camera. Most of these modules are based on AI techniques that support a human-level understanding of the outcomes.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
