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加拿大卡尔加里大学汉特教授、加拿大滑铁卢大学傅立平教授学术报告

发布时间:2014-04-28 12:21:00    


题目一:The Production, Exchange, Consumption Allocation System(PECAS) model
报告人: Dr. John Douglas Hunt  
题目二:Advancing Transportation Safety and Sustainability Using Big Data
报告人: Dr. Liping Fu   
时 间:2014年4月29日(星期二)上午9:00-11:30
地 点:航海楼九楼报告厅

汉特博士和PECAS模型简单介绍
    汉特博士 (Dr. John Douglas Hunt) 是加拿大卡尔加里大学土木工程系教授、系副主任(详情请参照:http://schulich.ucalgary.ca/civil/JohnHunt)和加拿大HBA Specto公司的创始人 (详情请参照:www.hbaspecto.com)。汉特博士24岁时既被聘为国际一流大学 – 加拿大阿尔伯塔大学教授。后师从Prof. Marcial Echenique 于1988年获得英国剑桥大学博士。汉特博士是世界上最为著名的土地交通整体规划的专家之一,他在钻研和吸收各种土地交通整体规划模型(比如:MEPLAN和TRANUS)的基础之上,创建了自己的模型 – PECAS (Production, Exchange, Consumption Allocation System – 生产、交换、消费分配系统)。这个系统解决了传统土地交通整体规划在理论基础、数据收集和模型计算等方面的很多问题,为世界上公认的最为先进的模型之一。现今,该模型已经被广泛推广应用在北美,多个亚洲大学也应用了该模型在在学术或初步工程咨询领域做了一些工作。如下是该模型被应用的城市或者州(省)的清单:
美国:
1.俄勒冈州土地交通模型(2000年至今)(Oregon Transportation Land Use Model Improvement Program: 2000-Present)
2.萨克拉曼多市(美国加州首府) PECAS模型(2007-2009年)(Sacramento PECAS Model: 2007-2009)
3.圣地亚哥市PECAS模型(2007年至今)(San Diego PECAS Model: 2007-Present)
4.加州PECAS模型(2007年至今)(California PECAS Model: 2007-Present)
5.亚特兰大市PECAS模型(2007年至今)(Atlanta PECAS Model: 2007-Present)
6.巴尔的摩市PECAS模型(2007-2011年)(Baltimore PECAS Model: 2007-2011)
7.俄亥俄州PECAS模型 (2005 – 2008年) (Ohio PECAS Model:2005-2008)
8.蒙哥马利市PECAS模型* (2006-2010年)(Montgomery PECAS Model:2006-2010)
加拿大:
1.阿尔伯塔省PECAS模型*(2005 – 至今)(Alberta PECAS Model:2005-Present)
2.卡尔加里市PECAS模型* (2009 – 至今) (Calgary PECAS Model: 2009-Present)
3.埃德蒙顿市PECAS模型* (2010-至今)(Edmonton PECAS Model: 2010-Present)
印度:
1.新德里市PECAS模型* (2008-至今)(New Delhi PECAS Model: 2008-Present)
中国:
1.北京市PECAS模型* (2010-至今)(Beijing PECAS Model: 2010-Present) 
备注:以上模型标注有*者为学术研究或者初步咨询项目

附 
付立平博士简介
Dr. Liping Fu is a Professor in the Department of Civil and Environmental Engineering and Director of the Innovative Transportation System Solutions (iTSS) Lab at the University of Waterloo.  Dr. Fu's research interest specifically focuses on evaluation and optimisation of large, complex traffic and transportation service systems where uncertainty and dynamics play a major role, and on the development of decision support tools for use in managing these systems. Dr. Fu has a long track record of research contributions to the areas of intelligent transportation systems, public transit, road safety, traffic impact on air quality, and winter road maintenance. Dr. Fu holds several international patents and software copyrights for vehicle routing and scheduling, highway-railway grade crossings safety analysis, and winter road surface condition monitoring.  He has provided technical services to many transportation agencies, including Transport Canada, Ministry of Transportation Ontario, and several municipalities in Canada.  Dr. Fu has served on numerous technical committees of various professional organizations, including Transportation Research Board’s Committee, Editorial Advisory Board of the journal of Transportation Research, Intelligent Transportation Systems Society of Canada, Canadian Urban Transit Association, and Institute of Transportation Engineers.  He was the past Chair of Transportation of Division of Canadian Society for Civil Engineering.  Dr. Fu received Transportation Association of Canada (TAC)'s 2011 Academic Merit Award sponsored by Transport Canada for his long-term contribution to the advancement of the academic field and to the development of tomorrow's transportation leaders.  Dr. Fu is also a Visiting Professor at the Southwest Jiaotong University.
    付立平博士现任加拿大滑铁卢大学土木与环境工程系教授,滑铁卢大学创新运输系统实验室主任,身兼加拿大土木工程学会运输委员会主席,交通运输研究年会辅助公交委员会委员等多项学术委员会职务,担任多项交通运输类及运筹学类学术期刊编辑和审稿人,并为数家运输机构包括加拿大运输部、安大略省运输部等提供咨询服务。付博士侧重于研究大型复杂的交通运输服务系统的优化和评估工作,并开发管理这些系统的所需的决策支持工具,主要致力于以下方面的工作: 冬季冰雪条件下的道路交通问题及相应的维护管理系统开发, 铁路公路交通安全理论和分析, 智能型车队系统的规划优化和评估研究, 基于实时信息下的公共交通运输调度模型研究, 启发式最优路径算法在ITS中的应用.
    付立平博士研究成果包括100多篇期刊和会议论文,其中有34篇论文被SCI数据库收录,52篇论文被EI数据库收录。拥有一项国际专利,并开发了一套商业路径调度系统正被美国和加拿大采用,同时还参与了数项交通仿真系统和网页程序GradeX的开发等,主持包括加拿大运输部、加拿大自然科学和工程研究理事会在内的国家级项目30余项,参与书籍Transportation Statistics的第二章Traffic Safety Study: Empirical Bayes or Hierarchical Bayes的写作,论文Evaluating Advanced Pickup and Delivery Systems: a Simulation Study荣获2000年加拿大交通研究论坛会议论文竞赛二等奖,由于科研成果突出,数年来曾被多个国际知名的科研机构、高校、学术会议邀请交流访问和发言,国际交通学术会议邀请发言和担任主席,参加和出席包括第一届交通信息与安全国际学术会议(ICTIS 2011)、第三届交通运输工程国际学术会议(ICTE2011)、加拿大土木工程协会年会暨第八届国际交通专业会议(CSCE2010)、国际城市运输研究交流研讨会(International Workshop for Urban Transportation Research Communication Between CSCE and UT-MOELAB)在内的多个国际交通学术会议。并于2008年1月至2008年6月被邀请至香港理工大学土木与结构工程系做访问学者。
由于长期致力于交通运输领域的学术贡献,以及培养未来的运输业人才,付立平博士荣获了加拿大交通运输协会2011年学术优异奖。该奖项是由加拿大运输部支持,由一个独立评判小组根据候选人对于学术领域的贡献,评选出的交通领域拥有优秀的领导力、卓越才干、丰硕成果的专家学者。

Advancing Transportation Safety and Sustainability Using Big Data
使用大数据推进交通安全和可持续性发展
Abstract
Transportation around the world continues to face significant challenges such as persistent safety problems, rising traffic congestion, high energy consumption and emissions.   These challenges can be potentially addressed using the latest developments in information technologies and increasing availability of Big Data. Big Data presents a unique opportunity to apply massive volumes of data from multiple sources for timelier optimization of transportation services and network capacity, while improving the safety and experience of travelers. However, before these potentials can be fully realized, many technical challenges must first be addressed including: the incompleteness and heterogeneity of available data, responsiveness and timeliness of decision-making and information provision, and consideration of complex traveler behavior and responses. In this talk, I will discuss the development and advancement of Big Data technologies and analytics and their applications for managing and controlling road transportation systems for maximum safety, efficiency, and sustainability. Specifically, I will present research and development opportunities in three major areas: (i) Collection of Big Data: design of novel dedicated and crowdsourcing techniques for collecting real-time road and traffic condition data and developing new integrated driver, road condition, and vehicle monitoring systems; (ii) Estimation and Prediction with Big Data: development of models and algorithms for estimating and predicting road, traffic, air and driver conditions using various Big Data sources such as video traffic surveillance systems, loop detectors, WIFI detectors, mobile phones, biometric sensors, and social media; (iii) Optimization Using Big Data: development of systematic optimization models and algorithms for intelligently managing travel demand, traffic, and travel experience.

  

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