基于出行时空特征的交通行为分析
报告题目: 基于出行时空特征的交通行为分析
报告人: 林宏志
点评人: 舒嘉教授、刘新旺教授
报告时间: 2013年3月22日下午3:40
报告地点：九龙湖经济管理学院B201会议室
报告内容摘要：
Trip-based approach and activity-based approach are two extremes in the use of activity related information when developing travel demand models. Creating lifestyle clusters for a population is a compromise between the two. On the one hand, it has taken into account travel-activity patterns in the development of the clusters. On the other hand, the clusters represent homogenous groups of individuals and simple activity-based travel demand models can be developed for each cluster. However, the development of such clusters requires knowledge of activity-travel patterns of individuals, which can only be obtained from a large-scale survey. It is still an open question how to create travel/activity- related lifestyle clusters using readily available socio-demographic data (such as census data) alone.
As a first attempt, Support Vector Machine (SVM) is used to develop classification functions that based on readily available information only. The methodologies proposed are applied to a sub-urban area in Hong Kong. Six lifestyle clusters are first produced using factor analysis and cluster analysis. SVM is then used to develop classification functions that are based on fewer variables. Results show that the two sets of lifestyle clusters are similar and that the SVM outperforms other traditional classification methods.
报告人简介：
林宏志，男，1981年生，博士。2000年9月至2004年7月，合肥工业大学管理学院信息管理与信息系统专业获学士学位，毕业论文题目为交通网络的最短路问题研究，从此开始进入交通研究领域。2004年9月，中国科学技术大学管理学院管理科学与工程专业硕博连读研究生，在完成基础课程学习后，于2005年9月转入香港城市大学商学院管理科学系联合培养，系统学习和研究了的交通行为模型。2009年11月，博士毕业后进入香港科技大学工学院土木与环境工程系从事博士后研究工作，研究的重点是基于交通网络的行为分析和最优化分析。2011年8月入职东南大学经管学院管理科学与工程系。
