The overall purpose of this study is to enhance existing transit systems by planning a new underground metro network. The design of a new metro network in the existing cities is a complex problem. Therefore, in this research, the study idea arises from the prerequisites to get out of conventional metro network design to develop a future scheme for forecasting an optimal metro network for these existing cities. Two models are proposed to design metro transit networks based on an optimal cost–benefit ratio. Model 1 presents a grid metro network, and Model 2 presents the ring-radial metro network. The proposed methodology introduces a non-demand criterion for transit system design. The new network design aims to increase the overall transit system connectivity by minimizing passenger transfers through the transit network between origin and destination. An existing square city is presented as a case study for both models. It includes twenty-five traffic analysis zones, and thirty-six new metro stations are selected at the existing street intersection. TransCAD software is used as a base for stations and the metro network lines to coordinate all these data. A passenger transfer counting algorithm is then proposed to determine the number of needed transfers between stations from each origin to each destination. Thus, a passenger Origin/Destination transfer matrix is created via the NetBeans program to help in determining the number of transfers required to complete the trips on both proposed networks. Results show that Model 2 achieves the maximum cost–benefit ratio (CBR) of the transit network that increases 41% more than CBR of Model 1. Therefore, it is found that the ring radial network is a more optimal network to existing square cities than the grid network according to overall network connectivity. 

Evaluating the hot mix asphalt (HMA) expected performance is one of the significant aspects of highways research. Dynamic modulus (E*) presents itself as a fundamental mechanistic property that is one of the primary inputs for mechanistic-empirical models for pavements design. Unfortunately, E* testing is an expensive and complicated task that requires advanced testing equipment. Moreover, a significant source of difficulty in E* modeling is that many of the factors of variation in the HMA mixture components and testing conditions significantly influence the predicted values. For each laboratory practice, a vast number of mixes are required to estimate the E* accurately. This study aims to extend the knowledge/practice of other laboratories to a target one in order to reduce the laboratory effort required for E* determination while attaining accurate E* prediction. Therefore, the transfer learning solution using deep learning (DL) technology is adopted for the problem. By transfer learning, instead of starting the learning process from scratch, previous learnings that have been gained when solving a similar problem is used. A deep convolution neural networks (DCNNs) technique, which incorporates a stack of six convolution blocks, is newly adapted for that purpose. Pre-trained DCNNs are constructed using a large data set that comes from different sources to constitute cumulative learning. The constructed pre-trained DCNNs aim to dramatically reduce the effort elsewhere (target lab) when it comes to the E* prediction problem. Then, a laboratory effort reduction justification is investigated through fine toning the constructed pre-trained DCNNs using a limited amount of the target lab data. The performance of the proposed DCNNs is evaluated using representative statistical performance indicators and compared with well-known predictive models (e.g., gbased Witczak 1-37A, G,d-based Witczak 1-40D and G-based Hirsch models). The proposed methodology proves itself as an excellent tool for the E* prediction compared with the other models. Moreover, it could preserve its accurate performance with less data input using the transferred learning from the previous phase of the solution.

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Traffic flow data are needed for traffic management and control applications as well as for transportation planning issues. Such data are usually collected from traffic sensors; however, it is not practical or even feasible to deploy traffic sensors on all of a network’s links. Instead, it is necessary to extend the information acquired from a subset of link flows to estimate the entire network’s traffic flow. To this end, this study proposes a robust deep learning architecture based on a stacked sparse autoencoders (SAEs) model for a precise estimation of the whole network’s traffic flow with an already-deployed sensor set. The proposed deep learning architecture has two consequent components: a deep learning model based on the SAEs and a fully connected layer. First, the SAEs model is used to extract traffic flow features and reach a meaningful pattern of the relation between the traffic flow data and network structure. Subsequently, the fully connected layer is used for the traffic flow estimation. Then, the whole architecture is fine-tuned to update its parameters in order to enhance the traffic flow estimation. For training the proposed deep learning architecture, synthetic link flow data are randomly generated from the network’s prior demand information. The performance of the proposed model is evaluated then validated using two real networks. A third medium real-size network is used to measure the robustness of applying the proposed methodology to this specific problem of traffic flow estimation.