Optimization of Multimodal Discrete Network Design Problems Based on Super Networks

In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network.

Discrete Network Design Problem

The network design problem aims to minimize the travelers’ total cost under budget restrictions. This research provides a framework to incorporate variable demand assignment in the discrete network design problem. The findings emphasized the impact of considering variable demand in discrete network design problem.

Incorporating Variable Demand Assignment Into Discrete Network Design Problem

The network design problem (NDP) is a bi-level problem with integer and decimal variables that aims to minimize the users' total cost under the budget constraints. Although utilizing variable demand models will theoretically change the NDP’s result, the demand was assumed to be fixed and known in the literature. In this paper, a mathematical analysis will be presented to justify the importance of using variable demand in the discrete network design problem (DNDP). The DNDP for Sioux-falls network will be solved in both variable and fixed demand conditions by using total enumeration (in the upper-level) and Frank-Wolf (in the lower-level) method. The result shows that DNDP findings for variable and fix demand conditions have significant differences, especially in the mid-budget level.

Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network

Urban road maintenance is an important part of urban traffic management. However, in modern cities, road maintenance work needs to occupy some traffic resources; therefore, unreasonable road maintenance schemes often lead traffic networks to unexpected large-scale congestion. In this paper, a dynamic programming model is proposed in order to minimize the delay caused by road maintenance scheme. This model can obtain a globally optimal maintenance scheme which contains the decisions and sequence for every stage of maintenance. Each stage of this model can be boiled down to a discrete network design problem. This model helps make suggestions for the traffic managers with the request of minimizing the delay caused by the maintenance scheme. This paper uses two examples to illustrate this method, one is a small-scale Nguyen-Dupuis network, and the other one is a larger scale Sioux-Falls network.