Designing large-scale bus network with seasonal variations of demand
mode - bus, place - asia, planning - network design, planning - service level, ridership - demand
Bus-network design, Multiple-demand scenarios, Bus routes, Seasonal-demand variation
Creating a bus network that covers passenger demand conveniently is an important ingredient of the transit operations planning process. Certainly determination of optimal bus network is highly sensitive to any change of demand, thus it is desirable not to consider average or estimated figures, but to take into account prudently the variations of the demand. Many cities worldwide experience seasonal demand variations which naturally have impact on the convenience and optimality of the transit service. That is, the bus network should provide convenient service across all seasons. This issue, addressed in this work, has not been thoroughly dealt with neither in practice nor in the literature. Analyzing seasonal transit demand variations increases further the computational complexity of the bus-network design problem which is known as a NP-hard problem. A solution procedure using genetic algorithm efficiently, with a defined objective-function to attain the optimization, is proposed to solve this cumbersome problem. The method developed is applied to two benchmarked networks and to a case study, to the city of Mashhad in Iran with over 3.2 million residents and 20 million visitors annually. The case study, characterized by a significant seasonal demand variation, demonstrates how to find the best single network of bus routes to suit the fluctuations of the annual passenger demand. The results of comparing the proposed algorithm to previously developed algorithms show that the new development outperforms the other methods between 1% and 9% in terms of the objective function values.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Amiripour, S.M.M., Ceder, A. & Mohaymany, A.S. (2014). Designing large-scale bus network with seasonal variations of demand. Transportation Research Part C: Emerging Technologies, Vol. 48, pp. 322–338.