With the rapid urbanization in developing countries, urban agglomeration area (UAA) forms. Also, transportation demand in UAA grows rapidly and presents hierarchical feature. Therefore, it is imperative to develop models for transit hubs to guide the development of UAA and better meet the time-varying and hierarchical transportation demand. In this paper, the multiperiod hierarchical location problem of transit hub in urban agglomeration area (THUAA) is studied. A hierarchical service network of THUAA with a multiflow, nested, and noncoherent structure is described. Then a multiperiod hierarchical mathematical programming model is proposed, aiming at minimizing the total demand weighted travel time. Moreover, an improved adaptive clonal selection algorithm is presented to solve the model. Both the model and algorithm are verified by the application to a reallife problem of Beijing-Tianjin-Hebei Region in China. The results of different scenarios in the case show that urban population migration has a great impact on the THUAA location scheme. Sustained and appropriate urban population migration helps to reduce travel time for urban residents.
Custom bus routes need to be optimized to meet the needs of a customized bus for personalized trips of different passengers. This paper introduced a customized bus routing problem in which trips for each depot are given, and each bus stop has a fixed time window within which trips should be completed. Treating a trip as a virtual stop was the first consideration in solving the school bus routing problem (SBRP). Then, the mixed load custom bus routing model was established with a time window that satisfies its requirement and the result were solved by Cplex software. Finally, a simple network diagram with three depots, four pickup stops, and five delivery stops was structured to verify the correctness of the model, and based on the actual example, the result is that all the buses ran 124.42 kilometers, the sum of kilometers was 10.35 kilometers less than before. The paths and departure times of the different busses that were provided by the model were evaluated to meet the needs of the given conditions, thus providing valuable information for actual work.
The community shuttle system plays an important role in serving communities with a heavy travel demand for the metro service. Stop location and route design are the two main decisions of planning a community shuttle service. Those two decisions are interrelated and interact, and are strongly related to the user cost and operating cost. The optimal stop location and route can help to reduce the walking distance of passengers and the route length. To make a trade-off between the walking distance of passengers and route length, we propose a discrete optimization problem. A single integrated formulation is established to optimize stop location and route design. Planners can decide the stop location and route design of the community shuttle system simultaneously based on this formulation. Then, we present a non-dominated sorting genetic (NSGA-II) based algorithm to obtain the non-dominated solutions of the discrete optimization formulation. The numerical experiments and a case study based on real-world data are used to demonstrate that the proposed solution method can yield a set of plans of stop location and route in a reasonable time. We also find that when the maximum tolerable walking distance is set to 418 m, the trade-off between the total walking distance of passengers and route length can be obtained.
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