Multimodal collaborative passenger-centric decision making to mitigate the impact of airside perturbations
Marzuoli, Aude Claire
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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. Collaborative Decision Making has shown significant benefits at the airport level, both in the US and in Europe. This dissertation examines how it could be extended to the multimodal network level, discusses the supporting qualitative and quantitative evidence, and provides recommendations for implementation. A case study on the crisis management following the Asiana Crash at San Francisco International Airport in July 2013 is presented. The resulting propagation of disturbances on the transportation infrastructure in the United States is examined. The perturbation takes different forms and varies in scale and time frames : 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. The crash led to a large number of domestic and international flight diversions to many airports, such as Oakland, San Jose, Los Angeles, but also Denver, Salt Lake City and Seattle for instance. Thousands of passengers found themselves struggling to reach their original destination. Passenger reaccommodation varied greatly from airline to airline and airport to airport.First a passenger-centric reaccommodation scheme is developed to balance costs and delays, for each diversion airport. Second, assuming better information sharing and collaborative decision making, we show that there was enough capacity at the neighboring airports, Oakland and San Jose, to accommodate most of the diverted flights and reoptimize the allocation of flight diversions to the Bay Area airports. The present research paves the way 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 and improved transportation performance.