A mixed-integer linear programming (MILP) model for dynamic coordinated signal optimization along a traffic corridor

In this thesis, we address urban traffic congestion, which imposes substantial economic and environmental costs while traditional infrastructure solutions remain increasingly infeasible due to financial constraints and induced demand. We develop a dynamic Mixed-Integer Linear Programming (MILP) framework for coordinated arterial signal optimization. Our contributions include a unified optimization model that jointly determines cycle length, phase sequencing, green-time allocation, and offset synchronization. We also introduce demand-responsive bounds adapting to real-time traffic conditions and a rolling-horizon execution strategy enabling continuous adaptation. The methodological pipeline integrates three modules: detector-based demand aggregation from VISSIM microsimulation, MILP optimization using Gurobi with operational constraints, and closed-loop signal control through COM interface. We validated the framework on a hypothetical three-intersection corridor and the Martin Luther King Boulevard in Chattanooga, Tennessee, demonstrating measurable improvements in speed, Vehicle Hours Traveled, and vehicle stops. Future work includes scaling to larger networks, incorporating pedestrian phases, and integrating connected vehicle data.

Grantor dc:publisher

University of Tennessee at Chattanooga

Year dc:date.available

2027

Author dc:creator

• Elhag, Firas

Contributors dc:contributor

• Sartipi, Mina
• Liang, Yu; Sun, Pengyuan
• College of Engineering and Computer Science

• Intelligent transportation systems
• Linear programming
• Traffic signs and signals--Control systems--Automation

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/1041

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-2224