Flexible transit services, which bring together the characteristics of fixed-route transit and demand-responsive transit, have been proven to be cost-efficient in low-density residential areas. In this paper, a methodology is proposed to assist planners in making better decisions when choosing between route deviation policy and point deviation policy, which are two promising types of flexible transit services. A user cost function is developed to measure the service quality of the transit systems, and analytical models are constructed to compare the system performance under both expected and unexpected demand levels. Based on the experiments for various scenarios over a real-life transit example, the critical demands, which represent the switching point between the two competing service policies, have been derived. Our findings show that point deviation policy is more efficient at low-demand levels, while route deviation policy is a better choice at low-to-moderate demand levels. At unexpectedly high demand levels, route deviation policy is better able to accommodate rejected passengers than point deviation policy.

Given the potential benefits of flex-route transit for the environment and traffic congestion, many cities have goals for diffusing flex-route transit. However, previous studies have focused on practical experience, strategic planning, and operational planning of flex-route transit. Few studies have identified the determinants of flex-route transit adoption. This study aims to address the effect of demographic characteristics and psychological factors on flex-route transit adoption by employing a stage of change framework. First, the transtheoretical model is applied to divide respondents into five ordered stages (i.e., Precontemplation, Contemplation, Preparation, Action, and Maintenance). Second, a survey was conducted in Beijing, China, using an online platform to obtain the demographic characteristics and measurement items of the respondents in the five ordered stages. Then, a structural equation model is applied to quantify psychological factors. In addition, the partial proportional odds (PPO) model is employed to estimate the best model and identify the determinants of flex-route transit adoption. Finally, an elasticity analysis is conducted to uncover the effect of determinants on flex-route transit adoption in the five ordered stages. The results suggest that the PPO model has the best fit and validity. The model reveals the significant implications of psychological factors and demographic characteristics for transport policy in various stages. These findings show that, regardless of the initial stage, interventions concentrating on psychological factors and demographic characteristics appear to have the potential to transfer the stage of individuals from one to another. To make policies as effective as possible for promoting flex-route transit adoption, the targeted interventions should be complemented in the specific stage.

To improve the reliability, responsiveness, and productivity of the flex-route transit service, this paper investigates the vehicle scheduling and routing problem under a dynamic operating environment. First, we discuss the new operating polices after the introduction of intelligent transportation systems (ITSs), including automatic vehicle location (AVL) system, mobile data terminal (MDT), and computer-aided dispatch (CAD) system. Second, a mixed integer programming (MIP) formulation is employed to solve the offline routing problem. Third, an online scheduling scheme is presented to tackle different dynamic events, such as dynamic requests, travel time fluctuations, cancellations of requests, and customer no-shows. Finally, simulation experiments based on a real-life flex-route transit service are conducted to assess the influence of different dynamic events. The results demonstrate that the proposed scheduling scheme is reliable for coping with various dynamic events, and our findings can be used to guide the policy making of flex-route transit services.

As an innovative combination of conventional fixed-route transit and demand-responsive services, flex-route transit is a promising transit option that can address the travel needs of residents in growing low-density suburban and rural areas. This paper assesses potential demand and passengers’ service design preferences prior to the deployment of a flex-route transit service in China. Using the city of Nanjing as an example, a survey was designed and implemented that involves a series of stated-preference experiments in order to (1) examine travelers’ willingness to use flex-route transit services, (2) identify the most promising users, and (3) guide the service design and policymaking before actual operation. Three discrete choice models, namely multinomial logit model, nested logit model, and panel mixed logit model, are applied to describe the mode-choice process using the data collected from the survey. An orthogonal design is used to generate the stated-choice experiments among traditional fixed-route transit, private cars, and hypothetical flex-route transit. Walking time, waiting time, in-vehicle time, and cost are selected as alternative attributes, which vary across each choice scenario. The survey results show that nearly 78% of respondents are willing to try the flex-route transit service. Women, bike-share members, and people who are disabled, retired, or need to transfer to the metro are the target groups of the flex-route transit service. The choice model results also indicate that slack time between checkpoints and vehicle delays should be kept within a reasonable range when designing and operating flex-route transit services.

How to meet the daily demand for resident transport while limiting the transmission of infectious diseases is a problem of social responsibility of urban transport systems during major public health emergencies. Considering the novel coronavirus pneumonia epidemic (COVID-19), a bus timetable system based on the “if early, wait, and if late, leave soon” strategy is proposed. Based on public transport vehicle constraints in this system, the concept of reliability is introduced to evaluate public transport timetable systems, and a model based on an event tree is built to calculate the failure rate of urban bus timetables. Then, the public transport situation in Yixing city is used as an example to perform confirmatory analysis, and the fluctuations in the reliability of the bus timetable during the novel coronavirus pneumonia epidemic are discussed. The research results show that the method proposed in this paper can obtain the overall failure rate of urban bus timetable by traversing the calculation of each round-trip interval and achieve an accurate evaluation of the reliability of bus timetables. During the early, middle, and more recent stages of the COVID-19 outbreak, the failure rate of bus timetables in Yixing city initially decreased and then increased. In the early stage of the outbreak, the failure rate of the Yixing bus timetable was 7.8142. However, the failure rate decreased to 4.3306 and 5.0160 in the middle and late stages of the epidemic, respectively. In other words, the failure rate of the public transport network in the middle and late stages decreased by 44.58% and 35.81%, respectively, compared with that in the early stage. Thus, during major health emergencies, such as the novel coronavirus pneumonia outbreak, the reliability of the urban bus timetable system can be improved by at least 35%, and cross-infection at bus stations can be prevented. The research results verify the feasibility and reliability of the implementation of bus timetabling strategies during major health emergencies.