Seminar on The Urban Rail Capacity and Delay Analysis Using a Train Following Model (Hosted by Dr. Amir Samimi)

Dr. Saeid Saidi, PhD
Assistant Professor
Department of Civil Engineering
University of Calgary

Bio:
Dr. Saeid Saidi is an Assistant Professor in the Department of Civil Engineering at the University of Calgary. Prior to join the University of Calgary, he was a Postdoctoral Associate at the Institute for Data, Systems and Society (IDSS) at Massachusetts Institute of Technology and a member of the MIT Transit Lab. He earned his PhD and MSc in civil engineering with transportation engineering and planning specialization from the University of Calgary.  He received his bachelor degree in industrial and system engineering from Sharif University of Technology. His main research activities are on transportation network modeling, public transportation planning and operation, and big data analytics using mobility sensing data. He is a member of Rail Transit Systems Committee (AP065) at the Transportation Research Board of the National Academies.

Abstract:

In this research seminar, a train following model is introduced which can efficiently capture the effects of train interactions in an urban rail line. The train following model is based on the estimation of induced delay for a train closely following a lead train, and is derived from empirical analysis based on historical track circuit data. Based on an analysis of sequential train delays, a train state prediction model is developed which can be used to predict the behavior of the system with respect to changes in initial conditions (e.g. scheduled headway, headway variation or dwell time) and disruptions. The performance analysis using this train following model is richer and more accurate than that from analytical macroscopic models while not being as time- and resource- intensive as a detailed micro-simulation model. The development and potential application of this model is demonstrated for the Massachusetts Bay Transportation Authority (MBTA) Red Line. The model can be used for both real-time control and offline operational strategy analyses.

Date:
Sunday, 22 December 2019
12:00 – 13:00

Venue:
304 Civil Engineering Department
Sharif University of Technology