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Keywords

Ramp metering, Local responsive control, ALINEA, Intelligent transportation systems, Feedback control, Developing countries

Document Type

Article

Abstract

Ramp metering is one of the most widely used traffic management practices to alleviate congestion on freeways. Although the use of such algorithms has proven effective in developed transportation systems, this adaptation and installation of suitable ramp metering algorithms can still be a problem in developing countries, especially when traffic characteristics, data availability and institutional capacity differ greatly from the developed ones. This paper is a systematic review of local responsive ramp metering strategies covering more than ninety publications from 1991-2026. The review identifies four categories of strategies: threshold-based, feedback-based (such as ALINEA and its variations), optimization-based and AI-based. The evaluation of each family is done on a number of dimensions of performance such as congestion reduction effectiveness, implementation complexity, calibration requirements, computational effort, robustness under non-standard conditions and maturity of field validation. The paper uses comparative analysis to determine that the best option for implementation in developing nations like Iraq is feedback-based control, specifically the ALINEA algorithm and its proportional-integral control variant (PI-ALINEA), which is also practical. Specific recommendations for the expressways of Baghdad are given and a roadmap for periodical implementation is provided. This paper is a systematic review paper that critically synthesizes the published literature on local responsive ramp metering; it does not present new primary data or field experiments. Given the breadth of the four strategy families covered (threshold-, feedback-, optimization- and AI-based), the work is intentionally integrative and could subsequently be subdivided into three focused studies (i) threshold and feedback control for developing countries, (ii) optimization-based and MPC methods, and (iii) AI-based and hybrid ramp metering.

DOI

10.30684/2412-0758.2417

First Page

101

Last Page

127

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