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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 107601, 9 pages
Research Article

Capacity Estimation for On-Ramp Merging Section of Urban Expressway Based on Time Headway Loss

1School of Transportation Central South University, Changsha 410075, China
2Hunan Province University Key Laboratory of Bridge Engineering (Changsha University of Science & Technology), Changsha 410000, China
3School of Transportation and Logistics Central South University of Forestry and Technology, Changsha 410004, China

Received 25 September 2013; Revised 31 December 2013; Accepted 1 January 2014; Published 20 February 2014

Academic Editor: Huimin Niu

Copyright © 2014 Xing-jian Xue et al. 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.


This paper proposes a model for estimating capacity of on-ramp merging section of urban expressway based on dynamics and gap acceptance theory, considering lane-changing processes and time headway loss. Survey data were collected from on-ramp merging sections of shanghai urban expressway system and showed that capacity drop of on-ramp merging section is caused by drivers’ lane-changing which may lead to unsteady speed of vehicles and so prolonged time headway compared to the minimum time headway corresponding to the maximum capacity. Three parameters (optimal time headway, time headway loss, and interference quantity of lane-changing) are given and a methodology by accumulating time headway loss due to lane-changing is developed to estimate the capacity drop. Results’ comparisons between real data and microsimulation of on-ramp merging sections and sensitivity analysis show that the proposed model can produce reliable and accurate results. This study also reveals that ramp flow and the difference between the optimal speed and the lane-changing speed of fleet have a great impact on capacity drop. This study is beneficial to evaluate congestion levels, to understand complex traffic phenomena, and so to find efficient solutions.