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Review of intelligent transportation systems methodology models | ||
| Journal of University of Anbar for Pure Science | ||
| Volume 19, Issue 2, November and December 2025, Pages 231-239 PDF (325.31 K) | ||
| Document Type: Review Paper | ||
| DOI: 10.37652/juaps.2025.158546.1365 | ||
| Authors | ||
| Zubaida Alazawi1; Amal A. Mohammed* 2; Khalid Z. Chaloob1; Yasmin Hamed Abd Kitab2; Omar Alani3 | ||
| 1Fallujah University, Al-Anbar, Iraq. | ||
| 2Department of Ways and Transportation, College of Engineering, Mustansiriyah University, Baghdad, Iraq | ||
| 3School of Computing, Science and Engineering, University of Salford, Manchester, UK. | ||
| Abstract | ||
| Intelligent transportation systems have been integrated and adapted to our daily lives. Transportation is one of the sustainable systems and an essential aspect of the shift toward a digital economy and intelligence. Here, we explore the theory of transportation services by exploring a mathematical model using queueing theory. Several methodologies have been employed for this theory, including micro- and Macro-simulation models. They have been used for the purpose of enhancing the analysis of intelligent transport systems. Intelligent transportation systems (ITS) are revolutionizing the management of transportation networks, leveraging advanced technologies to improve efficiency, safety, and sustainability. Operations research (OR) plays a crucial role in designing and optimizing these systems and providing mathematical models and computational techniques to address complex transportation challenges. This paper reviews the integration of OR methodologies and their role in developing and managing ITSs, including optimization, queuing theory, simulation, and network modeling. We assess technical applications for optimizing traffic flow, congestion, and instantaneous decisionmaking in transportation systems. OR contributes to ITS by using optimization models for route planning and traffic queue management. Simulation techniques also used to evaluate the system performance under different scenarios. The paper highlights the increasing trend of addressing multifaceted modern transportation networks by hybridizing models of multiple OR approaches. We aim to gather insight into current ITS methodology models, applications, and limitations by synthesizing recent advancements. Additionally, our aim is to identify emerging trends and future research directions, with a focus on big data, autonomous vehicles, and smart cities. This is a valuable resource in leveraging OR tools for ITS operations and designs. | ||
| Keywords | ||
| Autonomous vehicles; Congestion management; Hybrid models; Intelligent transportation systems; Queuing theory; Smart cities; Traffic flow | ||
| References | ||
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