Mobility as a Service (MaaS), as a part of the smart mobility paradigm, is recognized as one of the most effective solutions for the congestion management (CM) problem in cities. MaaS is a possible sustainable solution for transportation planning, promising the enhancement of traffic management and the lessening of congestion. MaaS can offer travelers access to several modes of transport without the need to own any vehicle, thereby presenting travelers with seamless and carefree traveling. This study aims to develop a methodological frame-work adapting MaaS as a supportive tool to alleviate traffic con-gestion. To support this mobility, the users and the drivers should be connected via a single platform based on an Artificial Intelligence algorithm (Reinforced Learning, for example). Such a strategy would optimize the mobility in the area as a whole over time by learning from actions/decisions such as: ride-sharing matching, taxi dispatching, in-route guiding, and the generation of inter-modal paths. That would help in providing solutions for real-time interaction. Decisions about departure times, paths to follow, and modes of travel would be available for all.

Railway transportation is an essential means of reducing traffic congestion, improving urban environmental conditions, and affecting people's social lives. These are some of the aims for which the Al Mashaaer train in Mecca Area was constructed for. However, high waiting times at the stations were noticed that affected pilgrims' service satisfaction levels. The proposed research addresses the problem of generating a plan for pilgrims' departure times to the Al Mashaaer train to achieve their best synchronization. It attempts to maximize the number of simultaneous arrivals of both the trains and pilgrims at the stations. The satisfaction of train users would be targeted by creating a detailed scheme for their departure times from the camps, which would minimize their waiting time at the stations. The whole problem would be formulated into the dynamic graph theory then an optimization technique would be developed for the solving stage.

Traffic flow data are a significant component of most intelligent transportation systems (ITS). Complete traffic flow data are required for most ITS applications, but providing traffic sensors in all network streets is not practical. Some flow types are difficult to observe directly, such as node pair demand (O/D flow). This study provides a mathematical analysis approach using a factorization scheme to convert conventional traffic assignment mapping into a useful format. The new mapping structure helps in identifying the amount of traffic-counting data (link flows) necessary to solve either the full observability problem for the network or a partial one. Once the required data are provided, the observability problem can be easily solved using backward substitution. In addition, the new format provides the dependencies of the different flow measures in the network. The proposed approach can track the change in the network observability state with the route choice uncertainty. Two fully reported illustrative examples in addition to a real case network are presented to demonstrate the generality of the proposed method and its potential contribution to the observability problem.

In terms of bearing lateral loads, a reinforced concrete shear wall (SW) is one of the most crucial structural elements in structures. Despite their significance, recent experimental research and post-earthquake surveys have exposed the inadequate safety margins of the SWs. Significant SW components cannot be accurately identified due to the absence of empirical and mechanistic models. This study introduces a novel paradigm for assessing the SW's crucial elements by using two consecutive modeling strategies. Initially, a finite element model is calibrated to mimic one of the laboratory-experimented SW structures. Then, deep residual neural networks (DRNNs) for non-parametric techniques are used to enhance the Abaqus software prediction skills and comprehensively understand the input parameters' influence on the selected SW structure's displacement value. The suggested DRNNs' design uses residual shortcuts (i.e., connections) that skip several levels in the deep network structure in order to address the issue of training with high accuracy. A thorough global sensitivity analysis (GSA) is done using the Latin Hypercube Simulation. Three distinct GSA techniques are used to emphasize the influence of each input variable on the amount of displacement in real-world applications while limiting the risk of result misinterpretation owing to interactions between input variables. In each GSA approach, an effort would be made to rank, filter, or map the input variables. The performance metrics of the DRNNs prediction models lend confidence to the GSA's results. The diameter of steel stirrups is reported to be the most significant component in the SW structure, while SW's displacement is more sensitive to higher and lower values of the lateral load domain.