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Abstract
The problem of Rapid Transit Network Design (RTND) is studied in this paper. Due to the noticeable contribution of rapid transit lines in public transportation network of large urban areas, this problem is interesting to the transportation specialists. On the other hand, the success stories of Bus Rapid Transit (BRT) systems in different countries have motivated us to study BRT network planning. BRT systems can be developed with less investment costs and construction time in comparison with rail-based systems. Therefore, planning Bus Rapid Transit lines, either to develop a new rapid transit network or extend a current one can be an interesting research topic. This problem, like other network design problems is difficult to solve for large scale networks. In this study, a mixed-integer mathematical model that addresses the Transit Network Design Problem (TNDP) is presented. The objective function of the model is maximization of trip coverage. To solve the model, an algorithm is proposed and implemented in C# environment. The main modules of the algorithm are the following: (1) routes generation, (2) search tree, (3) solution evaluation, and (4) inference. In Route Generation module, the candidate transit route set is determined. Afterwards, the Search Tree module provides a strategy which guarantees that all feasible combinations can be considered in the search process. To evaluate the performance of each transit route combination, a transit assignment algorithm is used in the Solution Evaluation part. Finally, the intelligence core of the search process, that is called Inference, helps the algorithm to find parts of the search space which cannot contain the optimal solution. The algorithm is tested on a real size network, i.e., the extension of the Greater Isfahan rapid transit network with BRT routes. The output of the algorithm is the set of BRT routes that maximizes the daily trip coverage index while satisfying the budget constraint. By solving the case study problem, it is shown that our proposed model and algorithm are capable of tackling real size rapid transit network design problems.