Multi-modal Public Transport Network Design Method

Abstract: With the rapid development of industrialization and urbanization, industrial development and population growth drive the expansion of urban space, urban transportation demand shows the characteristics of spatial decentralization and diversification, and transportation travelers' requirements for mobility, accessibility, and comfort of transportation travel services are enhanced. Mobility on demand (MoD) services such as DiDi and Uber are new modes of public transportation, bringing many new opportunities and challenges. MoD travel services, shared bicycles, and other complementary public transport modes are rapidly developing in the "Internet +" environment, serving the "one mile" before and after the residents' travel. MoD technologies play an important role as a feeder to the main public transportation lines, helping to increase public transportation patronage and improve the speed of travel for residents. In this context, the study aims to develop a multi-modal public transportation system network design methodology to provide better operational coordination between different modes of transportation and to provide faster travel services. In order to promote better coordination between different transportation modes and to provide theoretical and methodological support for the development of a multi-modal public transportation system network design system, a bi-level planning model for this problem is first constructed. The upper-level planning model is used to minimize the total travel time and cost of passengers and the economic cost of public transportation operators, and to decide which bus lines to operate, the structure of bus lines, and the frequency of operating bus lines; the lower-level operating model is used to assign passengers to make travel mode choices and to carry out traffic distribution of the public transportation network based on the minimum number of interchanges. Then, based on this bi-level planning model, an improved genetic algorithm is developed to solve the upper-level public transportation network planning problem, in which the algorithm for passenger flow allocation in the lower-level planning model is nested in the genetic algorithm.  Finally, the developed methodology is validated for the benchmark Mandl network design by comparing with the traditional public transportation network. The results show that the multi-modal public transportation network can effectively reduce passenger travel time compared with the traditional public transportation network at similar costs. Finally, we applied the network design method for the Barkarby area in the north of Stockholm, Sweden. The results show that it is appropriate to allocate mobility on demand vehicles in this area. The constructed model and the proposed algorithm are scientifically valid and can provide theoretical methodological reference and decision support for engineering practice.