The nested logit model

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Lecture from the MOOC "Discrete choice models: selected topics"
  1. Michel Bierlaire
  2. discrete choice
  3. nested logit
  4. logit
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Комментарии
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Dr. Bierlaire you are the best. I have been involved with SP studies for the past 34 years from Park and Ride, LRT, BRT, early or late start time ( peak spreading), risk averse propensity at signalised junctions. having conducted over 30K SP surveys myself on the past 34 years I always have had questions and never found transparent answers concerning theory and estimation but you are a super start who explains leaving nothing un-answered. A Big Thanks

AkablaaTribe
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Dr. Bierlaire, thank you so much for creating these videos - they are super helpful!

irfan
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thank you for explaining the concept in simple terms. Really good video

tarabalam
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Great teacher. I am very happy that I found your great channel.

husseinfg
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Michel, you are an amazing teacher. Super well explained.

joaoguilhermearaujo
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Thank you, sir, can you make videos on sensitivity analysis in different modes using time or cost attribute. I have not found any coding there. thanks

skillsandresearch
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This video is super helpful, but there's one thing I'm still confused about. We have U_bus = V_bus + epsilon_C, so that the person is choosing max(V_car + epsilon_car, V_bus + epsilon_C + epsilon_bus), but at 12:00, you are computing P(car) as if the decision was max(V_car + epsilon_car, V_bus + epsilon_bus). What happened to epsilon_C? Do we cleverly construct epsilon_bus in such a way that epsilon_C + epsilon_bus ~ EV(0, mu)?

jeffreysun