Introduction:

Long-term mobility decisions in housing, job and car ownership has been a subject for long in transportation and urban planning. Since the decision is relatively long time spanned, the decision making is relatively complicated comparing with the short-term daily routines. Often such a decision involves the joint decision of household members. In this talk, I would like to introduce an interpretable machine learning method, Dynamic Bayesian Network approach, to modeling the dynamics of life events and change of social demographics that result in the change of housing, job and transportation, as well as key life events such as marry, child birth. Specific focuses will be given to the dynamics happened in life course. The approach is promising not only to understand the moving decisions but also is helpful to predict individuals' joint decisions among the changes of housing, job and transportation. The model will be introduced with an application based on a retrospective survey data.

Lecturer:

Dr.Tao FENG,Professor of Hiroshima University, Japan

Host: Assoc Prof. Wen JIANG

Date:

4th November, 2022 10:30

Event Location:

Tencent Webinar   339 552 403  

Tencent Livestream Channel  X-scapeLab