Abstract
Level of service (LOS) analysis based on LOS criteria is essential for the planning, design, and operational evaluation of public transit. However, there are no systematic transit LOS criteria at present in China. Bus rapid transit (BRT) is receiving increasing attention worldwide. Therefore, this study addresses LOS criteria for BRT in China. Transit passengers are heterogeneous in their perceptions, needs, and behavior. The traditional hard LOS criteria have an inherent weakness, because of which the accuracy of an LOS analysis is limited. Thus, in this study, we initially conducted transit market segmentation to reduce heterogeneity and subsequently developed BRT fuzzy LOS criteria for different passenger groups. Using a smartphone-based transit travel survey system, we organized BRT passenger travel surveys on three BRT systems in China to collect data. Transit market segmentation was performed based on user perceptions; passengers were segmented into a calm passenger group and an anxious passenger group using the latent class model. Passenger arrival time, passenger wait time, and running speed of the bus were selected as service metrics to reflect the BRT’s LOS. BRT fuzzy LOS criteria for the three service metrics in the case of both the calm and anxious passenger groups were developed using fuzzy C-means clustering. The LOS criteria for the two groups of passengers fit their psychological characteristics and reflected their personalized travel needs. Fuzzy LOS criteria can describe to what extent service metric values belong to the adjacent LOS categories via the use of membership. Thus, fuzzy LOS criteria can overcome the weakness of hard LOS criteria.
Highlights
Level of service (LOS) criteria using service metrics that classify them into several categories according to different thresholds can provide quantitative LOS analysis standards for transportation systems [1, 2]
Literature Review e LOS criteria provided by Transit capacity and quality of service manuals (TCQSMs) for bus transit with service metrics of frequency, service span, access, passenger load, on-time performance, headway adherence, and transitauto travel time ratio are divided into multiple levels [4, 5]
We developed a smartphone-based transit travel survey system. is system consists of an app, a server, and a web interface. e app (Figure 1) interacts with users and collects and uploads the data to the server. e server stores the data and prepares them for the web interface. e web interface is the output end of the system, from which the collected data are downloaded
Summary
Level of service (LOS) criteria using service metrics that classify them into several categories according to different thresholds can provide quantitative LOS analysis standards for transportation systems [1, 2]. Us, in this study, we will initially carry out transit market segmentation and subsequently develop BRT LOS criteria for different passenger groups. E intent of this research work is to develop BRT fuzzy LOS criteria for passengers grouped using transit market segmentation in China. Data collection, including the development of a transit travel survey system and BRT passenger travel surveys, is described After this step, the methodology involved in a latent class model and fuzzy C-means clustering are introduced. 2. Literature Review e LOS criteria provided by TCQSMs for bus transit with service metrics of frequency, service span, access, passenger load, on-time performance, headway adherence, and transitauto travel time ratio are divided into multiple levels [4, 5]. Some studies use the latent class model to conduct transit market segmentation using social demographic, travel, and behavioral characteristics as manifest variables. Because of the heterogeneity in transit passengers, we should perform transit market segmentation when studying transit LOS. e inherent weakness of the hard LOS criteria compelled us to develop fuzzy LOS criteria for transportation facilities or services. erefore, we studied BRT fuzzy LOS criteria for the problem of passenger market segmentation in China
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