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Evaluation of On-ramp Control Algorithms

Author

Listed:
  • Jin, Wenlong
  • Zhang, Michael

Abstract

A freeway corridor consists of the freeway and its entrance/exit ramps, the cross streets, and adjacent parallel arterial streets. It is designed to provide a generally high level of service (LOS) to their users and to the communities which they serve. However, many corridors in the country are congested, with the worst congestion problems usually arising during the two peak periods(morning and evening) (Schrank and Lomax, 1999). =There are two types of traffic congestion observed: recurrent and non-recurrent. Recurrent congestion are due to excessive peak demands and non-recurrent congestion are due to capacity reduction caused by events such as accidents. The control of a traffic corridor, which consists of two major components freeway system control and arterial street system control, is aimed at improving flows on both freeway and arterial streets, and has been demonstrated as an effective mean to increase the level of service of a corridor system during peak periods. Ramp metering, or on-ramp control, which is designed to determine a metering rate for each controlled on-ramp based on traffic conditions of part or whole of the corridor, has been considered a very important component of corridor traffic control.

Suggested Citation

  • Jin, Wenlong & Zhang, Michael, 2001. "Evaluation of On-ramp Control Algorithms," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1gz7w0wm, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt1gz7w0wm
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    Citations

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    Cited by:

    1. Nima Dadashzadeh & Murat Ergun, 2019. "An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume," Sustainability, MDPI, vol. 11(22), pages 1-26, November.
    2. Toan, Trinh Dinh & Wong, Yiik Diew & Lam, Soi Hoi & Meng, Meng, 2022. "Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Jacob, Celine & Abdulhai, Baher, 2010. "Machine learning for multi-jurisdictional optimal traffic corridor control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(2), pages 53-64, February.
    4. Kerner, Boris S., 2005. "Control of spatiotemporal congested traffic patterns at highway bottlenecks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 565-601.
    5. Zhang, Michael & Ma, Jingtao, 2008. "Developing Calibration Tools for Microscopic Traffic Simulation Final Report Part 1: Overview Methods and Guidelines on Project Scoping and Data Collection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3hh8f7nz, Institute of Transportation Studies, UC Berkeley.

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