An Exact Solution Approach for the Bus Line Planning Problem with Integrated Passenger Routing

The bus line planning problem or transit network design problem with integrated passenger routing is a challenging combinatorial problem. Although well-known benchmark instances for this problem have been available for decades, the state of the art lacks optimal solutions for these instances. The branch and bound algorithm, presented in this paper, introduces three novel concepts to determine these optimal solutions: (1) a new line pool generation method based on dominance, (2) the introduction of essential links, i.e., links which can be determined beforehand and must be present in the optimal solution, and (3) a new network representation based on adding only extra edges. Next to presenting the newly obtained optimal solutions, each of the abovementioned concepts is examined in isolation in the experiments, and it is shown that they contribute significantly to the success of the algorithm.

Data-Driven Transit Network Design at Scale

Mass transit remains the most efficient way to service a densely packed commuter population. However, reliability issues and increasing competition in the transportation space have led to declining ridership across the United States, and transit agencies must also operate under tight budget constraints. Recent attempts at using bus network redesign to improve ridership have attracted attention from various transit authorities. However, the analysis seems to rely on ad hoc methods, for example, considering each line in isolation and using manual incremental adjustments with backtracking. We provide a holistic approach to designing a transit network using column generation. Our approach scales to hundreds of stops, and we demonstrate its usefulness on a case study with real data from Boston.

Impact of Automated Vehicles on Optimal Demand-Responsive Feeder Transit Network Design

This article aims to examine the economic benefits of automating flexible demand responsive feeder transit systems using a developed feeder bus routing optimization algorithm. The objective function of the algorithm is to minimize total passengers' and operating costs of the system. The results showed that when unit operating costs decline, total operating costs, and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in the total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce not only total operating costs and total costs, but also total passenger travel costs.