Connectivity is a significant problem in large-scale transit networks because the number of transfers required to conduct a trip is considered a discomfort by transit users. This paper presents a practical solution for an underground metro line planning problem by integrating existing bus and metro networks into a single connected transit network. The proposed method aims to obviate the usual combinatorial complexity when solving a transit route design problem. It aims to increase the overall transit system connectivity by selecting a consistent and non-demand-oriented criterion for the design. The metro lines are designed by minimizing passenger transfers through the transit network according to predefined demand node pairs. The design scheme offers a set of ring route alternatives for a sizeable case study in Greater Cairo. The case study selected sixteen traffic analysis zones, an existing metro network consisting of three main lines (113.6 km long), and twelve main bus lines (487.7 km long) for analysis. TransCAD software was used as the basis for coordinating the stations and lines of both the bus and metro systems. Subsequently, a passenger transfer counting algorithm was implemented to determine the number of transfers required between stations from each origin to each destination. A passenger origin–destination transfer matrix was created using the NetBeans integrated development environment to help determine the number of transfers required to complete trips on the transit network before and after proposing the new line. Based on the evaluation, the ring lines were highly efficient at significantly decreasing passenger transfers between stations with the minimum construction cost. This study will be of value during the strategic stages of the transit line design and will assist in rapidly generating initial solutions when certain demand information is unavailable.

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.