Presented By: Industrial & Operations Engineering
SEMINAR: "Using Analytics to Plan Reliable Itineraries Across Transportation Networks" — Michael Redmond
The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
Title:
Using Analytics to Plan Reliable Itineraries Across Transportation Networks
Abstract:
As access to information on transportation options becomes more readily available, the need arises to find modes of travel and itineraries that will be reliable and easy-to-use for travelers. While there is widespread information on cost and scheduled travel time for everything from airlines to ride-hailing services, the work on showing travelers the variation in travel time of these different options is lagging behind.
This work takes into account the uncertainty in travel time across modes of transportation, including flights, driving and public transit. It plans for this uncertainty by recommending reliable itineraries, which are itineraries that get travelers to their destination on time without missing any connections along the way. This research focuses on modeling transportation networks to discover itinerary reliability in situations where the answer may not be readily apparent, such as travel with layovers or missed transfers.
The experimental results show the value that finding these reliable itineraries can have over shortest travel time itineraries that travelers are accustomed to seeing. Also, this reliability metric gives an additional tool for travelers to use during the decision-making process of their trip planning. By making reliable transportation itineraries more transparent to travelers and network planners, it can help convince travelers to choose these modes of transportation in the future and take some of the uncertainty and stress out of travel planning.
Bio:
Michael Redmond recently graduated in Summer 2020 from the University of Iowa with a PhD in Business Analytics with a focus on transportation analytics and stochastic programming. He is an active member in the Transportation Science and Logistics Society of INFORMS and served as the INFORMS student president at Iowa. Prior to his PhD, Michael worked with companies and nonprofits, including the Chicago Bears, UI Office of Sustainability and Integrated DNA Technologies, on consulting projects during his time in the Supply Chain & Analytics MBA program. He has been involved in education for the past decade and thoroughly enjoys teaching – before diving into academia, he was a K-8 Math and Spanish teacher in Omaha. Michael is beginning post-doctoral research with Dr. Mark Daskin and Ford Motor Company on supply chain and demand uncertainty and is looking forward to meeting everyone in the U-M IOE community.
Title:
Using Analytics to Plan Reliable Itineraries Across Transportation Networks
Abstract:
As access to information on transportation options becomes more readily available, the need arises to find modes of travel and itineraries that will be reliable and easy-to-use for travelers. While there is widespread information on cost and scheduled travel time for everything from airlines to ride-hailing services, the work on showing travelers the variation in travel time of these different options is lagging behind.
This work takes into account the uncertainty in travel time across modes of transportation, including flights, driving and public transit. It plans for this uncertainty by recommending reliable itineraries, which are itineraries that get travelers to their destination on time without missing any connections along the way. This research focuses on modeling transportation networks to discover itinerary reliability in situations where the answer may not be readily apparent, such as travel with layovers or missed transfers.
The experimental results show the value that finding these reliable itineraries can have over shortest travel time itineraries that travelers are accustomed to seeing. Also, this reliability metric gives an additional tool for travelers to use during the decision-making process of their trip planning. By making reliable transportation itineraries more transparent to travelers and network planners, it can help convince travelers to choose these modes of transportation in the future and take some of the uncertainty and stress out of travel planning.
Bio:
Michael Redmond recently graduated in Summer 2020 from the University of Iowa with a PhD in Business Analytics with a focus on transportation analytics and stochastic programming. He is an active member in the Transportation Science and Logistics Society of INFORMS and served as the INFORMS student president at Iowa. Prior to his PhD, Michael worked with companies and nonprofits, including the Chicago Bears, UI Office of Sustainability and Integrated DNA Technologies, on consulting projects during his time in the Supply Chain & Analytics MBA program. He has been involved in education for the past decade and thoroughly enjoys teaching – before diving into academia, he was a K-8 Math and Spanish teacher in Omaha. Michael is beginning post-doctoral research with Dr. Mark Daskin and Ford Motor Company on supply chain and demand uncertainty and is looking forward to meeting everyone in the U-M IOE community.
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