Transportation Forecasting Competition (TRANSFOR 24) – Call for Participation – 2024 TRB Annual Meeting Workshop

Call for Participation – 2024 TRB Annual Meeting Workshop

Transportation Forecasting Competition (TRANSFOR 24)

Organized by: AED50 Artificial Intelligence and Advanced Computing Applications.

Supported by: IEEE ITSS Technical Activities Sub-Committee “Smart Mobility and Transportation 5.0”.

Sponsored by: The Center for Urban Informatics and Progress (CUIP) at The University of Tennessee at Chattanooga (UTC), NSF, City of Chattanooga, ITS America and Amazon Web Services (AWS).

Description:

Welcome to the TRB 2024 Data Challenge, an exciting opportunity to showcase your data science and machine learning skills while addressing critical issues in intelligent transportation! In this challenge, participants will be tasked with predicting pedestrian and vulnerable road user (VRU) intentions at intersections, as well as conducting a safety evaluation of the current state of the intersection.

Why is this Challenge Important?

Intelligent transportation systems are rapidly evolving to enhance road safety, traffic efficiency, and sustainability. A crucial aspect of this evolution is improving the interaction between vehicles and VRUs, such as pedestrians and cyclists, especially at complex intersections. Understanding and predicting the intentions of these users is vital for preventing accidents, reducing congestion, and enhancing overall transportation safety and efficiency. The prediction of VRU intentions, when combined with connected vehicle technology, holds significant value in enhancing road safety, traffic efficiency, and the overall effectiveness of intelligent transportation systems. The TRB 2024 Data Challenge addresses this critical issue by focusing on two key components:

Data Provided:

Participants will have access to a rich dataset that includes:

Participants will receive access to an Amazon Web Services (AWS) data science environment called MLSpace. MLSpace is a secure web based solution for data science teams providing frictionless access to machine learning resources. The solution leverages the power of AWS and Amazon SageMaker, a fully managed machine learning service. Within MLSpace Participants will also have access to selected resources like Jupyter Notebook or JupyterLab. This environment will provide Participants with the ability to collaboratively work within Projects to build, train, and deploy machine learning models for the Competition.

Benefits of Participation:

Participating in the TRB 2024 Data Challenge offers several advantages:

Problem Formulation

Contestants are asked to explore advanced approaches for detecting high-risk pedestrians attempting to cross the street at signalized intersections, and predict intentions for every subclass of VRUs to cross the street. Often, these populations are severely disadvantaged at crosswalks and intersections because there is usually difficulty in predicting their intentions and accurately identify their plans to cross intersections in a timely manner which may pose risks to them while crossing. The proposed research could aid cities and municipalities in developing data-driven solutions that bring us closer to Vision Zero goals and create a safer traffic environment for all road users.

Panel Members:

Laura Chace, Zach Buss, David Reinke, Heng Wei, Guoyuan Wu, Yiyi Wang, Meixin Zhu, Mingyang Li, Xudong Fan, Farinoush Sharifi, Masoud Hamedi, Ankur Tyagi, Yingyan Lou, Eleni Vlahogianni, Mina Sartipi and Osama A. Osman.

Competition Rules

Teams should build solutions from scratch using the provided dataset. Third party OPEN-SOURCE tools and frameworks are allowed as well as your normal tooling. You may also incorporate pre-existing material that is freely available to the public into your project, such as public domain images, Creative Commons music, open-source libraries, existing APIs and platforms, and the like. However, the core development of the code MUST be the original development of the team.

Questions?

If you have questions, please email transforcompetition@gmail.com

Important Dates:

Intellectual Property

An Entrant submission provided the Submission components are solely the Entrant’s work product and the result of the Entrant’s ideas and creativity, and the submission must be open-sourced. An Entrant may submit a Submission that includes the use of open source software or hardware, provided the Entrant complies with applicable open source licenses and, as part of the Submission, creates software that enhances and builds upon the features and functionality included in the underlying open source product. By entering the Competition, you represent, warrant, and agree that your Submission meets these requirements.

Plagiarism

Plagiarism is the use of information or concepts from another article, website, or report without clearly attributing the source. Plagiarism is not acceptable. Phrases, sentences, or sections taken from another document, even if written by the same author(s), must appear within quotation marks and the source must be credited.

Join us at TRB 2024 to push the boundaries of transportation technology, improve road safety, and make our streets more accessible and secure for everyone. Get ready to analyze data, build predictive models, and help shape the future of intelligent transportation. We look forward to your participation!