Date
2024年10月11日
Venue
東京大学工学部11号館 1階 HASEKO-KUMAホール＋ラウンジ
Yafeng Yin教授 東京大学工学系研究科フェロー就任記念講演
東京大学 社会基盤学科 交通・都市・国土学研究室の大山です．
この度，ミシガン大学教授のYafeng Yin先生を東大にお招きし，以下の講演会を実施します．Yin先生が東大の工学系研究科フェロー (※) に就任されたことを記念しての会ではありますが，講演のみの聴講も可能ですので，皆様積極的なご参加をよろしくお願いいたします．
※The title of “Fellow, School of Engineering, The University of Tokyo” will be granted to persons who have their main base of activity at institutions abroad and who have carried out distinguished achievements in scholarship or education in the engineering field as well as meritorious service to the education or research at this school through exchanges with it and whose continued support via exchanges can be expected.
【Yafeng Yin教授 東京大学工学系研究科フェロー就任の記念講演】
■場所：東京大学工学部11号館 1階 HASEKO-KUMAホール＋ラウンジ
■日にち：10/11 (金)
■スケジュール：
17:00-18:00 ウェルカム＠ラウンジ
18:00-19:00 記念講演＋記念写真撮影＠KUMAホール
19:30-21:00 記念パーティー（東大キャンパス内，場所未定，5000円程度を想定）
■主催：東京大学 社会基盤学科 交通・都市・国土学研究室 / Transportation and Urban Research Hub at UT
■実施形式: ハイブリッド (オンラインの場合は18-19時のみ)
■申込フォーム：https://forms.gle/uR9NYv2Vsc3Q82M27
■記念講演概要：
Title: Modeling Mobility: The Quest for Behavioral Realism in Travel Forecasting
Abstract: Travel forecasting stands at the forefront of shaping future transportation landscapes, providing essential insights into the patterns of people and goods movement within a region. This modeling domain is crucial for guiding infrastructure development, policy adjustments, and the strategic planning to support growth. In this presentation, we delve into the transformative journey of travel forecasting methods over the past seven decades, tracing their evolution from the aggregate, zone-based four-step models from the 1950s to today’s sophisticated micro-behavioral activity-based models. We explore the paradigm shift in transportation network modeling, highlighting the progression towards increased behavioral realism. This shift has seen the conceptualization of travelers evolve from perfectly rational actors with deterministic behavior, to ‘economic individuals’ maximizing random utility, and finally to ‘social beings’ with bounded rationality. Our discussion highlights the interdisciplinary contributions from operations research, economics, and machine learning that have significantly enriched methodological approaches in travel forecasting. Furthermore, we examine the burgeoning role of artificial intelligence in travel forecasting, focusing on its potential to revolutionize model development.