Modelling and Control of Multi-Vehicle Traffic Networks using an Integrated VISSIM-MATLAB Platform

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Shindgikar, S. (2020). Modelling and Control of Multi-Vehicle Traffic Networks using an Integrated VISSIM-MATLAB Platform. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

This thesis aims to optimize the road traffic network by using different control algorithms and techniques to monitor road driving conditions and improve road safety. Intelligent transportation systems (ITS) have paved the way for opportunities in traffic network monitoring, adaptive signalized intersections, optimized traffic networks and predictive traffic analysis. This thesis aims to develop a platform for integrating PTV VISSIM and MATLAB SIMULINK to design and analyze the flow of traffic in a highway traffic network. As a case study, a 2-lane road network in Charlotte, North Carolina was designed and simulated in PTV VISSIM which is a microscopic multi modal traffic flow software. We model a non-signalized traffic network in VISSIM. The math model and network dynamics were formulated and implemented in MATLAB due to its mathematical prowess. From VISSIM, we take the inflow and outflow rates data and send them to the controller in MATLAB through a VISSIM Component Object Model (COM) interface. A COM-server was created in MATLAB to merge and integrate the software. By employing an extremum seeking approach, an optimal velocity is determined and sent back to VISSIM through the COM interface. Extremum seeking controller is a non model based controller and hence is used in the research due to limited knowledge of the system. The extremum seeking controller was designed in MATLAB SIMULINK. Integration of VISSIM-MATLAB-SIMULINK is done by the COM server. Numerical simulation demonstrates the effectiveness of the platform for testing different traffic control approaches and optimizing flow.

Details
Author

Shindgikar, Shubhankar

Title

Modelling and Control of Multi-Vehicle Traffic Networks using an Integrated VISSIM-MATLAB Platform

Physical Description

1 online resource (61 pages) : PDF

Date

2020

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

This thesis aims to optimize the road traffic network by using different control algorithms and techniques to monitor road driving conditions and improve road safety. Intelligent transportation systems (ITS) have paved the way for opportunities in traffic network monitoring, adaptive signalized intersections, optimized traffic networks and predictive traffic analysis. This thesis aims to develop a platform for integrating PTV VISSIM and MATLAB SIMULINK to design and analyze the flow of traffic in a highway traffic network. As a case study, a 2-lane road network in Charlotte, North Carolina was designed and simulated in PTV VISSIM which is a microscopic multi modal traffic flow software. We model a non-signalized traffic network in VISSIM. The math model and network dynamics were formulated and implemented in MATLAB due to its mathematical prowess. From VISSIM, we take the inflow and outflow rates data and send them to the controller in MATLAB through a VISSIM Component Object Model (COM) interface. A COM-server was created in MATLAB to merge and integrate the software. By employing an extremum seeking approach, an optimal velocity is determined and sent back to VISSIM through the COM interface. Extremum seeking controller is a non model based controller and hence is used in the research due to limited knowledge of the system. The extremum seeking controller was designed in MATLAB SIMULINK. Integration of VISSIM-MATLAB-SIMULINK is done by the COM server. Numerical simulation demonstrates the effectiveness of the platform for testing different traffic control approaches and optimizing flow.

Genre

masters theses

Subjects--Topics

Mechanical engineering
Transportation

Degree

M.S.

Keywords

Component Object Model
Extremum Seeking Control
Matlab-Vissim Integrated
Traffic Networks

Subject Area

Mechanical Engineering

Advisor(s)

Ghasemi, Amir

Committee Members

Pulugurtha, Srinivas
Xu, Jun

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2020.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Shindgikar_uncc_0694N_12438

Permalink

http://hdl.handle.net/20.500.13093/etd:2105