The treatment of uncertainty in the dynamic origin–destination estimation problem using a fuzzy approach

Regardless of existing types of transportation and traffic model and their applications, the essential input to these models is travel demand, which is usually described using origin–destination (OD) matrices. Due to the high cost and time required for the direct development of such matrices, they are sometimes estimated indirectly from traffic measurements recorded from the transportation network. Based on an assumed demand profile, OD estimation problems can be categorized into static or dynamic groups. Dynamic OD demand provides valuable information on the within-day fluctuation of traffic, which can be employed to analyse congestion dissipation. In addition, OD estimates are essential inputs to dynamic traffic assignment (DTA) models. This study presents a fuzzy approach to dynamic OD estimation problems. The problems are approached using a two-level model in which demand is estimated in the upper level and the lower level performs DTA via traffic simulation. Using fuzzy rules and the fuzzy C-Mean clustering approach, the proposed method treats uncertainty in historical OD demand and observed link counts. The approach employs expert knowledge to model fitted link counts and to set boundaries for the optimization problem by defining functions in the fuzzification process. The same operation is performed on the simulation outputs, and the entire process enables different types of optimization algorithm to be employed. The Box-complex method is utilized as an optimization algorithm in the implementation of the approach. Empirical case studies are performed on two networks to evaluate the validity and accuracy of the approach. The study results for a synthetic network and a real network demonstrate the robust performance of the proposed method even when using low-quality historical demand data.     

Airline-driven ground delay programs: A benefits assessment

Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines’ diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP and is the most important technological contribution of this paper. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compared to the state-of-the-research centralized approaches. Moreover, the cost reduction benefits of the resultant airline-driven GDP designs are equitably distributed across different airlines.     

Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.     

大数据背景下的交通模型发展思考

将传统交通调查和模型与新的信息技术相结合,根据大数据的特征改进交通模型的方法体系,是交通模型发展面临的极大挑战.分析交通大数据对交通模型研发的促进作用,特别是在人口及就业岗位分布、综合交通网络数据和基于交通调查的模型参数等核心数据方面的支撑作用.从相关定义、市场细分以及信息完整性三个方面阐述大数据与交通模型的一致性问题.深入讨论应用大数据改进交通调查技术、4D模型研发、出行分布目的地选择模型、轨道交通出行接驳模型、出行成本校核等交通模型关键技术.通过对比大数据和交通模型的决策分析过程,指出大数据与交通模型互动发展的途径.     

Individual trip rate transferability analysis based on a decision tree approach

Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.     

船舶推进感应电机无速度传感器技术研究

将全阶状态观测器技术应用于船舶推进感应电机无速度传感器控制系统中,实现对推进电机转速的实时辨识.以定子电流和转子磁链为状态变量,构建全阶状态观测器.再以全阶状态观测器为可调模型,驱动电机为参考模型,使用模型参考自适应的方法对电机转速进行辨识.针对转速估算系统低速区域不稳定问题,设计出能保证系统在低速时保持稳定的反馈增益矩阵设计准则.在Matlab/Simulink仿真环境下进行仿真.     

Estimation of population origin–interchange–destination flows on multimodal transit networks

Previous research has combined automated fare-collection (AFC) and automated vehicle-location (AVL) data to infer the times and locations of passenger origins, interchanges (transfers), and destinations on multimodal transit networks. The resultant origin–interchange–destination flows (and the origin–destination (OD) matrices that comprise those flows), however, represent only a sample of total ridership, as they contain only those journeys made using the AFC payment method that have been successfully recorded or inferred. This paper presents a method for scaling passenger-journey flows (i.e., linked-trip flows) using additional information from passenger counts at each station gate and bus farebox, thereby estimating the flows of non-AFC passengers and of AFC passengers whose journeys were not successfully inferred.The proposed method is applied to a hypothetical test network and to AFC and AVL data from London’s multimodal public transit network. Because London requires AFC transactions upon both entry and exit for rail trips, a rail-only OD matrix is extracted from the estimated multimodal linked-trip flows, and is compared to a rail OD matrix generated using the iterative proportional fitting method.     

榄香烯乳联合化疗对小鼠Lewis肺癌的抑瘤作用

目的观察榄香烯乳联合化疗对小鼠Lewis肺癌的抑瘤作用,探讨其抑瘤作用机制。方法 32只C57BL小鼠右腋下接种Lewis肺癌细胞成瘤,随机分为4组:肺癌模型组、化疗组(足叶乙甙3mg/kg+顺铂3mg/kg,6mg/kg)、榄香烯乳组(100mg/kg)、榄香烯乳+化疗组(106mg/kg),每组8只。连续给药7d后,观察脾脏及胸腺指数变化,测定抑瘤率及抑制转移率,并进行组织病理学检测以及透明质酸酶(HAase)和组织金属蛋白酶抑制剂-1(TIMP-1)蛋白表达测定。结果与单纯化疗组比较,榄香烯乳联合化疗组抑瘤率及抑制转移率明显升高,肺癌小鼠脾脏指数及胸腺指数显著增加,瘤组织细胞形态结构得到改善,TIMP-1蛋白阳性表达率降低明显。结论榄香烯乳联合化疗对小鼠Lewis肺癌的抑瘤作用明显,其作用机制与增强细胞外基质及免疫调节有关。     

基于优化赋权法的船型技术经济模糊综合评判

多目标、多准则决策问题中,决策结果的准确性与属性权重的合理性和评价系统的可行度密切相关。分析了主观赋权法、客观赋权法的优缺点, 提出了基于优化的组合赋权方法。该方法应用序列二次优化方法求解组合权向量,使最终的组合权向量与专家统计评分法、层次分析法、信息熵法、变异系数法这四种单一的主客观赋权方法相似度最高。将这种组合赋权方式与模糊综合评价法结合应用于秦皇岛至温州电厂航线散装运煤船方案决策分析,并与灰色关联评价法结果比较,决策结果证明该综合方法能合理、客观的给出排序方案,同时也能推广应用于船舶与海洋工程决策的相关领域。     

基于三角面积坐标法的纤维梁-柱单元研究

借助三角面积坐标法对三角形网格内的纤维应变和纤维应力进行线性插值,利用三角面积坐标的数值特性对纤维梁-柱单元截面刚度矩阵中除扭转项外的其余项进行解析推导,并得出纤维梁-柱单元截面抗力的近似表达式。数值研究表明:对于具有相同纤维单元数的纤维梁-柱单元,三角面积坐标法可以提高算法的计算精度。