A Two-Level Data-Driven Event Graph Approach for Performance Evaluation of a Driver Advisory System for Freight Trains

Abstract

As rail networks face increasing pressure to improve punctuality and capacity under mixed-traffic conditions, digital support tools such as Connected Driver Advisory Systems (C-DAS) are increasingly deployed. Yet, empirical evidence of their operational effects in daily practice remains limited. This paper presents a data-driven two-level approach based on macroscopic and microscopic event graph representations, constructed from the same underlying corridor and observed train movements, to evaluate the performance of C-DAS. It fuses timetable event records, track section occupation and release data, and C-DAS usage logs to enable comparison between trains operated with and without C-DAS at timetable point and block section levels. Derived from the event graphs, differences in timetable deviation, running time deviation, and the occurrence of unplanned yellow signal aspects are assessed using non-parametric statistical tests. The approach is demonstrated through its application to RouteLint, a C-DAS deployed for freight operations in the Netherlands, using one year of data from a mixed-traffic mainline corridor comprising 23,573 freight train runs. The results show modest but consistent differences in operational performance between trains operated with and without RouteLint. Statistically significant differences in timetable deviation are identified at multiple key locations, with RouteLint-equipped trains showing approximately 30–70 s lower deviations at major stations and junctions. At the microscopic level, running time deviations exhibit substantial local variability, while lower deviations are observed on selected block sections for RouteLint-equipped trains. In addition, RouteLint-equipped trains show approximately 6% lower overall odds that an observed yellow signal aspect is unplanned, while the association varies across operating contexts.