Determining capacity flexibility of the urban transit network under different transit operation conditions

Abstract

This paper proposes the capacity flexibility models of transit networks to describe the ability of urban transit networks to accommodate the changes in passenger demands. In order to keep the variations of the average passenger travel time in a reasonable way, the concept of coefficient of variation (CoV) is introduced to measure and limit the deviation from a baseline time of the average passenger travel time. The CoV could transform the capacity flexibility values to non-dimensional form in order to compare the capacity flexibility characteristics of different scale transit networks on an equal basis. The genetic algorithm with deep search (GA-DS) for this problem is proposed to find the approximating optimal solution of the capacity flexibility models more effectively. The algorithm has been tested with benchmark problems reported in the existing literature. The optimal solution of the flexibility models shows that the flexibility values are further improved by reducing the number of stops for transit routes under different transit operation conditions. At last, the influence factors of the capacity flexibility of transit systems are discussed to address reliable transit services.

KEYWORDS:

• Urban transit network
• Capacity flexibility
• Coefficient of variation
• Genetic Algorithms (GA)
• Deep search

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural and Science Foundation of China [grant numbers 71864008, 71403064].