Stochastic Modeling of Traffic Flow in Urban Transport Networks for Infrastructure Planning

Abstract

Traffic congestion on urban streets is still a major challenge for urban planners and transportation engineers. Since population and urbanization continue to grow and grow, faster, more efficient traffic management and infrastructure planning becomes more and more important. A particular strong trend in recent years is stochastic modelling of traffic dynamics. This sophisticated method, on the other hand, helps researchers and planners take the inherent variability and uncertainty in traffic patterns into consideration when designing and planning urban transportation systems. The authors take us on a comprehensive exploration into the fundamental concepts, methodologies, and applications that form the edge of urban mobility in this stochastic traffic modeling. We'll look at the use of these models to revolutionize our understanding of traffic flow and decisions made based on data science in infrastructure development from the bottom up (or the simple basics of queuing theory to advanced machine learning techniques). After going through this article, readers would have full understanding on how stochastic modeling is changing the urban transportation planning, which is about designing cities of tomorrow that are smarter and more resilient.