Transportation Systems

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.

The transportation networks analysis aims to investigate the system's structural characteristics and dynamical evolution to evaluate the transit services. In this regard, the topological characteristics of the Iran railway network have been studied and compared to two well-studied railway networks, China and Spain. Also, the network vulnerability to station failures has been studied based on different attacks. Accordingly, in the first step of this work, the city stations have been extracted from Iran railway information to construct the network. Then, some structural properties, including the degree distribution, betweenness centrality, clustering coefficient, and distance distribution, have been analyzed for three networks. Finally, the network reliability has been evaluated using a random as well as adversarial attack. The structural analysis reveals that the Iran railway network would require some structural optimization to improve the economic benefits. Based on the vulnerability investigation, the network efficiency of the network will be dropped more quickly utilizing the maximum betweenness attack. In addition, as it were, little parts of the network seem to keep their usefulness as the estimate of the giant component is diminished exceptionally strongly when less than 20% of nodes are expelled from the network haphazardly or intentioned.