Multi-state Modeling of Delay Evolution in Suburban Rail Transports
By: Stefania Colombo , Alfredo Gimenez Zapiola , Francesca Ieva and more
Train delays are a persistent issue in railway systems, particularly in suburban networks where operational complexity is heightened by frequent services and high passenger volumes. Traditional delay models often overlook the temporal and structural dynamics of real delay propagation. This work applies continuous-time multi-state models to analyze the temporal evolution of delay on the S5 suburban line in Lombardy, Italy. Using detailed operational, meteorological, and contextual data, the study models delay transitions while accounting for observable heterogeneity. The findings reveal how delay dynamics vary by travel direction, time slot, and route segment. Covariates such as station saturation and passenger load are shown to significantly affect the risk of delay escalation or recovery. The study offers both methodological advancements and practical results for improving the reliability of rail services.
Similar Papers
Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Machine Learning (CS)
Predicts train delays to keep trains on time.
Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Models
Machine Learning (CS)
Predicts train delays and riders better than weather.
Probabilistic modeling of delays for train journeys with transfers
Applications
Predicts train delays for better travel plans.