Dynamic Evaluation Method for Public Transport Operation Indicators Based on Rank Transformation

<div class="section abstract"><div class="htmlview paragraph">This study presents a method to evaluate the daily operation of traditional public transportation using multi-source data and rank transformation. In contrast with previous studies, we focuses on dynamic indicators generated during vehicle operation, while ignoring static indicators. This provides a better reference value for the daily operation management of public transport vehicles. Initially, we match on-board GPS data with network and stop coordinates to extract arrival and departure timetable. This helps us calculate dynamic operational metrics such as dwell time, arrival interval, and frequency of vehicle bunching and large interval. By integrating IC card data with arrival timetable, we can also estimate the number of people boarding at each stop and derive passenger arrival time, waiting time, and average waiting time. Finally, we developed a comprehensive dynamic evaluation method of public transportation performance, covering the three dimensions: bus stops, vehicles, and routes. This method uses K-means clustering to classify and applies rank transformation techniques to score. At stop levels, we use principal component analysis(PCA) to identify key influencing factors, anf apply K-means for clustering and service-level classification. At the vehicle and route level, we perform rank transformation on indicators such as average waiting time and vehicle bunching frequency. Delphi method is used to determine the relative weights of each indicator, so as to facilitate the ranking of bus routes according to the comprehensive score. This method is applicable to the dynamic operation indicators of 20 bus routes in Shenzhen, involving 293 vehicles and 506 stops. The results show that this method can effectively evaluate the dynamic operation of public transport and make contribution to daily management.</div></div>