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 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 U.S. and in Europe. This paper examines how it could be extended to the multimodal network level, discusses the supporting evidence, and provides recommendations for implementation. A case study on the disruption management following the Asiana Crash at San Francisco International Airport is presented. The crash led to a large number of flight diversions to many airports, such as Oakland, Los Angeles, but also Seattle for instance, disrupting the journeys of thousands of passengers. 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. Based on this case study, recommendations for the adoption of multimodal CDM are elaborated. This paper paves the way for further data-driven research for increased resilience of passenger door-to-door journeys.
|Original language||English (US)|
|Number of pages||19|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Oct 1 2016|