Many past researchers have ignored the multi-objective nature of the transit route network design problem (TrNDP), recognizing user or operator cost as their sole objective. The main purpose of this study is to identify the inherent conflict among TrNDP objectives in the design process. The conventional scheme for transit route design is addressed. A route constructive genetic algorithm is proposed to produce a vast pool of candidate routes that reflect the objectives of design, and then, a set covering problem (SCP) is formulated for the selection stage. A heuristic algorithm based on a randomized priority search is implemented for the SCP to produce a set of nondominated solutions that achieve different tradeoffs among the identified objectives. The solution methodology has been tested using Mandl's benchmark network problem. The test results showed that the methodology developed in this research not only outperforms solutions previously identified in the literature in terms of strategic and tactical terms of design, but it is also able to produce Pareto (or near Pareto) optimal solutions. A real-scale network of Rivera was also tested to prove the proposed methodology's reliability for larger-scale transit networks. Although many efficient meta-heuristics have been presented so far for the TrNDP, the presented one may take the lead because it does not require any weight coefficient calibration to address the multi-objective nature of the problem.

Traffic control and management applications require the full realization of traffic flow data. Frequently, such data are acquired by traffic sensors with two issues: it is not practicable or even possible to place traffic sensors on every link in a network; sensors do not provide direct information about origin–destination (O–D) demand flows. Therefore, it is imperative to locate the best places to deploy traffic sensors and then augment the knowledge obtained from this link flow sample to predict the entire traffic flow of the network. This article provides a resilient deep learning (DL) architecture combined with a global sensitivity analysis tool to solve O–D estimation and sensor location problems simultaneously. The proposed DL architecture is based on the stacked sparse autoencoder (SAE) model for accurately estimating the entire O–D flows of the network using link flows, thus reversing the conventional traffic assignment problem. The SAE model extracts traffic flow characteristics and derives a meaningful relationship between traffic flow data and network topology. To train the proposed DL architecture, synthetic link flow data were created randomly from the historical demand data of the network. Finally, a global sensitivity analysis was implemented to prioritize the importance of each link in the O–D estimation step to solve the sensor location problem. Two networks of different sizes were used to validate the performance of the model. The efficiency of the proposed method for solving the combination of traffic flow estimation and sensor location problems was confirmed from a low root-mean-square error with a reduction in the number of link flows required.