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Seyedehsan Seyedabrishami on Muck Rack

Seyedehsan Seyedabrishami

As seen in: Nature, MDPI, Journal of Advanced Transportation
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Articles

Dimensions management of traffic big data for short-term traffic prediction on suburban roadways - Scientific Reports

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By Seyedehsan Seyedabrishami
| Nature Verified
Abstract Since intelligent systems were developed to collect traffic data, this data can be collected at high volume, velocity, and variety, resulting in big traffic data. In previous studies, dealing with the large volume of big traffic data has always been discussed. In this study, big traffic data were used to predict traffic state on a section of suburban road from Karaj to Chalous located in the north of Iran.
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A Two-Stage Sequential Framework for Traffic Accident Post-Impact Prediction Utilizing Real-Time Traffic, Weather, and Accident Data

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By Seyedehsan Seyedabrishami, Steve O’Hern, Amirhossein Abdi
| Journal of Advanced Transportation
Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables.
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Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow

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By Arash Rasaizadi, Seyedehsan Seyedabrishami
| Journal of Advanced Transportation
Journal of Advanced Transportation PDF For authorsFor reviewersFor editorsTable of Contents Submit Journal of Advanced Transportation / 2022 / Article Research Article | Open Access Arash Rasaizadi, Seyedehsan Seyedabrishami, "Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow", Journal of Advanced Transportation, vol. 2022, Article ID 3198636, 12 pages, 2022.
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