Listen to the People! Comparing Perceived and Documented Disruptions in Public Transportation, through Quantitative Quality of Experience, the Case Study of NYC

Abstract

Public transportation systems are core infrastructures to smart cities and are expected to perform on a daily basis and in a reliable manner. However, such systems can be affected by external and internal events that manifest as delays. There are two sources customers can consult, which will impact their decision rewarding commuting times; either the official (provided by the transportation agency) or the reported by other passengers. It is debated whether the first approach is more accurate than the latter. This work compares both alternatives, using Natural Language Processing techniques and visual analytics to determine the delays on trains and evaluate their resilience process. For this purpose the impact of a massive snowstorm in November, 2018 to New York's Metro North Railroad system was used as a case study.

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