Evaluating Multimodal Passenger Flow Dynamics in Urban Metro Networks Using Real-Time Operational Data

Urban metro systems have become the backbone of mobility in rapidly growing cities, yet their ability to handle fluctuating and multimodal passenger flows remains insufficiently understood. Recent advancements in real-time data acquisition, including automated fare collection records, train localization feeds, and multisource passenger tracking, have created new opportunities for capturing operational dynamics with high temporal and spatial resolution. This study evaluates multimodal passenger flow dynamics across interconnected metro corridors by integrating real-time operational data with advanced analytical models. The approach considers transfers between metro lines, interface interactions with buses, walking links, and shared micromobility access points. Through the fusion of multisensor datasets, the research provides a detailed interpretation of congestion propagation, station-level pressure points, flow redistribution patterns, and the temporal stability of peak-period demand waves. The evaluation also explores how disruptions, timetable adjustments, and varying headways alter passenger accumulation and route-switching behavior. By examining these multidimensional interactions, the study offers a deeper understanding of how metro networks respond to both recurrent and irregular demand variations. The findings contribute to practical improvements in passenger assignment strategies, operational decision-making, and planning policies for multimodal integration. The results demonstrate that the use of real-time data increases the capacity to identify latent flow structures and emerging bottlenecks, ultimately strengthening the resilience and service reliability of urban metro systems. The study concludes that systematic analysis of multimodal flow dynamics can support the development of more adaptive and demand-responsive transport networks capable of meeting the complex mobility needs of contemporary urban environments.Urban metro systems have become the backbone of mobility in rapidly growing cities, yet their ability to handle fluctuating and multimodal passenger flows remains insufficiently understood. Recent advancements in real-time data acquisition, including automated fare collection records, train localization feeds, and multisource passenger tracking, have created new opportunities for capturing operational dynamics with high temporal and spatial resolution. This study evaluates multimodal passenger flow dynamics across interconnected metro corridors by integrating real-time operational data with advanced analytical models. The approach considers transfers between metro lines, interface interactions with buses, walking links, and shared micromobility access points. Through the fusion of multisensor datasets, the research provides a detailed interpretation of congestion propagation, station-level pressure points, flow redistribution patterns, and the temporal stability of peak-period demand waves. The evaluation also explores how disruptions, timetable adjustments, and varying headways alter passenger accumulation and route-switching behavior. By examining these multidimensional interactions, the study offers a deeper understanding of how metro networks respond to both recurrent and irregular demand variations. The findings contribute to practical improvements in passenger assignment strategies, operational decision-making, and planning policies for multimodal integration. The results demonstrate that the use of real-time data increases the capacity to identify latent flow structures and emerging bottlenecks, ultimately strengthening the resilience and service reliability of urban metro systems. The study concludes that systematic analysis of multimodal flow dynamics can support the development of more adaptive and demand-responsive transport networks capable of meeting the complex mobility needs of contemporary urban environments.

کلیدواژه ها:

Real-time operational data ، Multimodal passenger flows ، Urban metro networks ، Flow dynamics modelling ، Transit system performance