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.

Considering whether the dependence of high-income groups on car travel can be improved by changing the built environment and based on the survey data of residents’ travel in Xiamen in 2015 and a multinomial logit model, the differences in the influence of the built environment on the travel modes of high-income groups with and without cars, as well as the influence intensity of personal socioeconomic attributes and the built environment on the travel modes, are explored. The key factors that have significant influence on the travel modes of high-income groups and the influence mechanism are analyzed. The results show the following. (1) After controlling other variables, the increase of the mixing degree of population density and land use in traffic communities inhibited the use of cars by high-income groups, but the inhibition effect is weak. Employment density and bus stop density have no significant correlation with the travel mode of high-income groups with cars. (2) The improvement of road network density, parking space density, and greening rate in traffic communities promoted the use of cars by high-income groups with cars. (3) The increase in the density of shopping malls and leisure and entertainment places in the traffic communities promoted the walking level of high-income groups. The plot ratio of a traffic district has no significant correlation with the travel mode of high-income groups. (4) The high-income groups inside the island of Xiamen prefer walking and public transport, while the high-income groups outside the island prefer cars. (5) The influences of the built environment on the travel modes of high-income groups with and without cars are significantly different, and its effect is less than that of individual socioeconomic attributes. The above conclusions provide a reference for improving residents’ travel mode and city planning by optimizing the land-use planning of residential communities of different resident groups.

A simulation model based on Petri nets is presented to evaluate the performance of the transfer procedure for passengers in urban railway transit hubs. A systemic and physical description of urban railway transit hubs is illustrated by Petri nets model. The simulation model was verified through a real case to analyze the evolution of the number of transfer passengers on the facilities and the equipments and measure the transfer time for all the transfer passengers in the hubs. It is showed that a potential and effective method is provided to evaluate and optimize the design of the transit hubs.