壁面吸声材料对汽车排气消声器性能的影响

用GT-Power软件对某汽车消声器进行建模和仿真．通过建立3组在消声器壳体内壁粘贴不同吸声材料的模型,对其进行仿真对比分析。发现在消声器壳体内壁粘贴吸声材料,能提高消声器的插入损失。且随材料密度的增加,插入损失在低频段有较大的增加,而在中高频段变化不大。实验数据验证了采用GT-Power对消声器的插入损失进行仿真分析其结果是可信的。     

盾构隧道管片接缝三元乙丙橡胶密封垫力学性能影响因素敏感度分析

为研究盾构隧道管片接缝三元乙丙橡胶密封垫力学性能的影响因素,首先,以实际工程为背景,采用自主设计的试验系统进行试验,并以此验证数值模型的可靠性; 然后,利用数值模型,采用正交分析方法,对密封垫断面参数(开孔形状、断面开槽数量、断面开孔率)、密封垫橡胶材料硬度、密封垫拼装姿态(张开量、错位量)进行六因素三水平的密封垫力学性能影响因素的敏感度分析; 最后,结合力学性能研究与防水性能预测需求,提出以闭合压缩力、密封垫接触应力分布作为分析指标。研究结果表明: 1)针对密封垫装配时所需的闭合压缩力,其影响因素主次顺序为张开量、断面开孔率、橡胶硬度、开槽数量、开孔形状、其他因素以及错位量; 2)密封垫接触应力分布的影响因素主次顺序为开孔形状、断面开孔率、错位量、其他因素、张开量、橡胶硬度、开槽数量。根据研究结果选取对密封垫力学性能影响较为显著的因素进行单因素分析,然后根据分析结果及橡胶垫密封垫防水性能预测方法, 提出背景工程管片接缝密封垫的优化方案。     

舰船用非金属材料污染散发特性及检测评价研究进展

舰船大量使用的非金属材料是舱室空气污染的主要来源之一。合理的非金属材料污染散发检测评价方法可有效指导舰船设计、建造、使用和维护期间的非金属材料选用,降低舱室空气污染散发,改善舱室空气品质。从舰船用非金属材料的散发特性出发,全面总结舰船用非金属材料散发特性的检测评价现状,指出当前我国舰船用非金属材料污染散发检测评价存在的不足和发展方向。该研究对舰船及其他人工环境相关规范和控制程序的制定与改进具有指导意义。

     

VMS下驾驶员路径选择行为分析与建模

为了实现交通流在路网中的合理分配,可变信息板(VMS)通过发布动态交通信息来引导驾驶员选择最合理路径.通过构造基于陈述偏好调查(SP调查)的诱导信息下驾驶员路径选择行为的有序多分类Logit模型,分析各因素对驾驶员路径选择行为的影响.分析结果表明,年龄、出行距离、可选路径、VMS信任度、改变路径频率对驾驶员路径选择行为的影响较大.在驾驶员个人属性中,年龄对驾驶员的路径选择行为影响最大;在出行属性中,驾驶员对出行距离和可选路径最为敏感;在对VMS的认知和服从属性中,驾驶员对VMS信任度越高,按照VMS诱导信息改变出行路径的概率越大;改变路径频率越高的驾驶员,越易改变出行路径.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.     

Traffic and emissions impact of congestion charging in the central Beijing urban area: A simulation analysis

Congestion charging is being considered as a potential measure to address the issue of substantially increased traffic congestion and vehicle emissions in Beijing. This study assessed the impact of congestion charging on traffic and emissions in Beijing using macroscopic traffic simulation and vehicle emissions calculation. Multiple testing scenarios were developed with assumptions in different charging zone sizes, public transit service levels and charging methods. Our analysis results showed that congestion charging in Beijing may increase public transit use by approximately 13%, potentially reduce CO and HC emissions by 60–70%, and reduce NOx emissions by 35–45% within the charging zone. However, congestion charging may also result in increased travel activities and emissions outside of the charging zone and a slight increase in emissions for the entire urban area. The size of charging zone, charging method, and charging rate are key factors that directly influence the impact of congestion charging; improved public transit service needs to be considered as a complementary approach with congestion charging. This study is used by Beijing Transportation Environment and Energy Center (BTEC) as reference to support the development of Beijing’s congestion charging policy and regulation.     

On the variance of recurrent traffic flow for statistical traffic assignment

This paper generalizes and extends classical traffic assignment models to characterize the statistical features of Origin-Destination (O-D) demands, link/path flow and link/path costs, all of which vary from day to day. The generalized statistical traffic assignment (GESTA) model has a clear multi-level variance structure. Flow variance is analytically decomposed into three sources, O-D demands, route choices and measurement errors. Consequently, optimal decisions on roadway design, maintenance, operations and planning can be made using estimated probability distributions of link/path flow and system performance. The statistical equilibrium in GESTA is mathematically defined. Its multi-level statistical structure well fits large-scale data mining techniques. The embedded route choice model is consistent with the settings of O-D demands considering link costs that vary from day to day. We propose a Method of Successive Averages (MSA) based solution algorithm to solve for GESTA. Its convergence and computational complexity are analyzed. Three example networks including a large-scale network are solved to provide insights for decision making and to demonstrate computational efficiency.     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.