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 intrinsically coupled through passenger transfers and are interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. This paper provides a case report of the Asiana crash in San Francisco International Airport (SFO) on July 6, 2013, and its repercussions on the multimodal transportation network. It studies the resulting propagation of disturbances on the transportation infrastructure in the USA, particularly on the U.S. air transport network and the ground transportation in the Bay Area. The perturbation takes different forms and varies in scale and time frame: cancelations and delays snowball in the airspace, with up to 86% of cancelations in the U.S. due to the SFO crash; highway traffic near the airport is impacted by congestion in previously not congested locations, with low speed and high delays on US 101; and transit passenger demand exhibits unusual traffic peaks in between airports in the Bay Area, with up to 180 passengers more per hour between SFO and Oakland International Airport Bay Area Rapid Transit stations. This paper also investigated the effect of the crash on the social media Twitter. This paper, through a case study, aims at stressing the importance of further data-driven research on interdependent infrastructure networks. The end goal is to form the basis for optimization models behind providing more reliable passenger door-to-door journeys and improved transport network resilience.
|Original language||English (US)|
|Number of pages||18|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Feb 1 2016|