Version 1
: Received: 17 September 2022 / Approved: 19 September 2022 / Online: 19 September 2022 (10:22:00 CEST)
How to cite:
Alazemi, F.; Fadhloun, K.; Rakha, H.; Mittal, A. Towards Building a Naturalistic Cycling Dataset Capturing Bicycle/Car Interactions. Preprints2022, 2022090275. https://doi.org/10.20944/preprints202209.0275.v1
Alazemi, F.; Fadhloun, K.; Rakha, H.; Mittal, A. Towards Building a Naturalistic Cycling Dataset Capturing Bicycle/Car Interactions. Preprints 2022, 2022090275. https://doi.org/10.20944/preprints202209.0275.v1
Alazemi, F.; Fadhloun, K.; Rakha, H.; Mittal, A. Towards Building a Naturalistic Cycling Dataset Capturing Bicycle/Car Interactions. Preprints2022, 2022090275. https://doi.org/10.20944/preprints202209.0275.v1
APA Style
Alazemi, F., Fadhloun, K., Rakha, H., & Mittal, A. (2022). Towards Building a Naturalistic Cycling Dataset Capturing Bicycle/Car Interactions. Preprints. https://doi.org/10.20944/preprints202209.0275.v1
Chicago/Turabian Style
Alazemi, F., Hesham Rakha and Archak Mittal. 2022 "Towards Building a Naturalistic Cycling Dataset Capturing Bicycle/Car Interactions" Preprints. https://doi.org/10.20944/preprints202209.0275.v1
Abstract
As machine learning and computer vision techniques and methods continue to advance, the collection of naturalistic traffic data from video feeds is becoming more and more feasible. That is especially true for the case of bicycles, for which the collection of naturalistic data is not achievable in the traditional vehicle approach. This study describes a research effort that aims to extract naturalistic cycling data from a video dataset for use in safety and mobility applications. The used videos come from a dataset collected in a previous Virginia Tech Transportation Institute study in collaboration with SPIN in which continuous video data at a non-signalized intersection on the Virginia Tech campus was recorded. The research team applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles at a high level of precision. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined.
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.
