Microsimulation model for motorway merges with ramp-metering controls

Al-Obaedi, JTS and Yousif, S ORCID: https://orcid.org/0000-0003-0350-6077 2012, 'Microsimulation model for motorway merges with ramp-metering controls' , IEEE Transactions on Intelligent Transportation Systems, 13 (1) , pp. 296-306.

[img] PDF - Published Version
Restricted to Repository staff only

Download (569kB) | Request a copy


This paper presents a newly developed microsimulation model for motorway merge traffic, focusing on issues that relate to ramp-metering (RM) control and its effectiveness. The model deals with general and more specific drivers’ behavioral tasks, such as the drivers’ cooperative nature in allowing other drivers to merge in front of them either by decelerating or shifting to adjacent lanes. The main criteria of this model are governed by the application of car-following, lane-changing, and gap-acceptance rules. The model has been calibrated and validated mainly using real traffic data taken from loop detectors for two-, three-, and four-lane motorways. Compared with the S-PARAMICS software, using the same data, the model showed better results. The effectiveness of some of the widely used RM control algorithms, such as Demand-Capacity, ALINEA, and ANCONA, were also assessed after finding the optimum parameters (such as critical occupancy and position of loop detectors). Index Terms—Microsimulation, occupancy, ramp-metering (RM) algorithms, travel time.

Item Type: Article
Themes: Built and Human Environment
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: IEEE Transactions on Intelligent Transportation Systems
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
ISSN: 1524-9050
Depositing User: S Yousif
Date Deposited: 20 Mar 2012 16:41
Last Modified: 15 Feb 2022 16:20
URI: https://usir.salford.ac.uk/id/eprint/20746

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year