Anatomy of a Crash

TL;DR

This paper analyzes the ripple effects of the 2013 Asiana Crash on the US transportation network, highlighting the need for data-driven resilience strategies for interdependent infrastructure systems.

Contribution

It provides a detailed case study of a real-world transportation disruption and emphasizes the importance of data-driven research for improving network resilience.

Findings

Disruptions caused delays and cancellations across multiple transportation modes.

Ripple effects included congestion and unusual traffic peaks in the Bay Area.

Highlights the need for data-driven models to enhance transportation resilience.

Abstract

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road or rail, are coupled and interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. The present paper provides a case report of the Asiana Crash in San Francisco International Airport on July 6th 2013 and its repercussions on the multimodal transportation network. It studies the resulting propagation of disturbances on the transportation infrastructure in the United States. The perturbation takes different forms and varies in scale and time frame : cancellations and delays snowball in the airspace, highway traffic near the airport is impacted by congestion in previously never congested locations, and transit passenger demand exhibit unusual traffic peaks in between airports in the Bay Area. This paper, through a case study, aims at stressing the importance of further data-driven research on interdependent infrastructure networks for increased resilience. The end goal is to form the basis for optimization models behind providing more reliable passenger door-to-door journeys.