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Title: Longitudinal train dynamics model for a rail transit simulation system

Abstract

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of the model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.

Authors:
ORCiD logo [1];  [1]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Transportation Inst. and Center for Sustainable Mobility
Publication Date:
Research Org.:
PARC, Palo Alto, CA (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); Transportation for Livability by Integrating Vehicles and the Environment (TranLIVE); Georgia Inst. of Technology, Atlanta, GA (United States)
OSTI Identifier:
1427871
Grant/Contract Number:  
AR0000612
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 86; Journal Issue: C; Journal ID: ISSN 0968-090X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; Train; Longitudinal dynamics; Microscopic simulation; Rail transit

Citation Formats

Wang, Jinghui, and Rakha, Hesham A. Longitudinal train dynamics model for a rail transit simulation system. United States: N. p., 2018. Web. doi:10.1016/j.trc.2017.10.011.
Wang, Jinghui, & Rakha, Hesham A. Longitudinal train dynamics model for a rail transit simulation system. United States. doi:10.1016/j.trc.2017.10.011.
Wang, Jinghui, and Rakha, Hesham A. Mon . "Longitudinal train dynamics model for a rail transit simulation system". United States. doi:10.1016/j.trc.2017.10.011.
@article{osti_1427871,
title = {Longitudinal train dynamics model for a rail transit simulation system},
author = {Wang, Jinghui and Rakha, Hesham A.},
abstractNote = {The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of the model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.},
doi = {10.1016/j.trc.2017.10.011},
journal = {Transportation Research Part C: Emerging Technologies},
number = C,
volume = 86,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
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