多源交通信息条件下的出行行为选择研究

在考虑多源交通信息可能导致信息过剩,从而影响决策者出行选择的条件下,引入决策论的思想,通过对多源交通信息方式发布的特点分析,应用模糊物元理论,建立个体出行的行为选择模型,在熵值法确定客观权重的条件下,根据贴近度的大小对决策方案进行选择,克服出行者的主观经验判断。通过实例计算,证明了不确定型决策在多源交通信息影响下出行者路径选择方面的可用性,客观上能为其提供最优的出行路径。     

轿车车门的模态计算和组合式优化分析

以模态分析理论为基础,通过有限元软件HyperWorks对车门进行了模态分析,考察了该车门的刚度特性;并用拓扑优化中的变密度法,通过HyperWorks对轿车车门进行了拓扑与形貌的组合分析,得到了高刚度、轻质量的设计方案.     

基于EI及MAC算法的连续刚构桥传感器优化布置

介绍了有效独立法(EI)和模态保证准则(MAC)的工作性能与适用范围,并结合各自优点,得到了一种新型的混合算法.将此方法运用到以龙河大桥为研究对象的连续刚构桥传感器布置上.结果表明:基于EI及MAC混合算法得到的测点截面位置有别于人为凭借经验将传感器布置在根部、边跨及中跨1/4倍数截面等位置;通过MAC值验算,连续刚构...     

RV传动中曲柄轴的模态分析

在完成对RV-250AII型减速器实体模型建模的基础上,采用有限元分析软件Ansys,对RV传动中的关键部件曲柄轴分别进行自由边界支撑和实际边界支撑条件下的模态分析.通过对其固有频率等仿真计算结果进行分析,获得了可能引起整机共振的零件本身的固有频率范围.通过分析位移和节点变形图以获得零件上发生最大刚体位移的位置,为优化设计提供参考.提出改进建议,分别对增加曲柄轴轴颈和减少行星轮质量两种方案进行分析,验证了对改变固有频率及避免共振的影响,同时通过比较得知减轻行星轮质量的方案更优.     

交通信息对驾驶员逐日路径选择行为影响研究

在无信息、发布历史信息和发布预测信息3种信息条件下,分别建立了驾驶员的路径理解行程时间期望值的更新模型.通过建立一个含有2条平行路径的简单路网,对3种交通信息条件下驾驶员逐日路径选择进行了仿真,结果表明:交通信息对驾驶员的作用与驾驶员路径选择的随机程度和对信息依赖程度有关;在3种交通信息条件下,路网均不能达到用户均衡平衡状态;交通信息的预测方法不同,预测信息对驾驶员路径选择的影响有差异.     

鼓式制动器的噪声原因及模态分析

由鼓式制动器关键部件的实际工况,对鼓式制动器的制动底板、制动蹄等构件实现了对应的模态分析,分别抽选了后八阶的模态并归纳了对应的振型情况,由振型图和总结的分析列表,得到了鼓式制动器主要零部件的模态分析结果与达到固有频率的共振评价,为鼓式制动器设计阶段的结构强度分析提供了一定的侧面参考,具有实际的工程意义。     

道路交通统计生命价值测算模型及其验证

基于参数服从SB分布的混合Logit 模型进行道路交通统计生命价值的测算研究. 首先，结合意愿选择法和正交实验法设计出行路径选择调查问卷；然后，基于死亡风险系数服从对数正态分布和SB分布的混合Logit 模型，构建统计生命价值测算模型；接着以大连市私家车出行者为调查对象获得调查数据，并利用Monte Carlo 仿真方法进行模型参数标定；最后，对模型进行比较分析，并获得统计生命价值的测算值. 研究结果表明：死亡风险参数服从限制域为(0.0, 0.5)SB分布的混合Logit 模型，精确性更高且更合理；道路交通统计生命价值测算值为 105.76万元，这一结果可以作为道路交通安全项目经济评价的参考数据.     

Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets

This paper presents an investigation of the temporal evolution of commuting mode choice preference structures. It contributes to two specific modelling issues: latent modal captivity and working with multiple repeated crossectional datasets. In this paper latent modal captivity refers to captive reliance on a specific mode rather than all feasible modes. Three household travel survey datasets collected in the Greater Toronto and Hamilton Area (GTHA) over a ten-year time period are used for empirical modelling. Datasets collected in different years are pooled and separate year-specific scale parameters and coefficients of key variables are estimated for different years. The empirical model clearly explains that there have been significant changes in latent modal captivity and the mode choice preference structures for commuting in the GTHA. Changes have occurred in the unexplained component of latent captivities, in transportation cost perceptions, and in the scales of commuting mode choice preferences. The empirical model also demonstrates that pooling multiple repeated cross-sectional datasets is an efficient way of capturing behavioural changes over time. Application of the proposed mode choice model for practical policy analysis and forecasting will ensure accurate forecasting and an enhanced understanding of policy impacts.     

Development and validation of a direct mode choice model

A direct discrete mode choice model is introduced using relative attributes of competing modes as well as socioeconomic characteristics of travelers. The model is calibrated and validated for two available historic databases in the Dallas–Fort Worth region. The validation is conducted against the outputs of a current nested logit model used by the regional planning organization as well as the observed values based on transit ridership surveys for a newly inaugurated commuter rail service. The calibrated model is applied after the introduction of this new transit mode. The results show that the estimated mode shares by the proposed model have a statistically better consistency with the observed values than the estimates of the conventional nested logit model. Unlike the logit model, the structure of the direct model based on relative attributes also has the advantage of not needing recalibration each time a new travel mode is introduced. The model is found to be easier to calibrate and produces more accurate results than the nested logit model, commonly used by many metropolitan planning organizations.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.