Modeling the charging and route choice behavior of BEV drivers

Due to the limited cruising range of battery electric vehicle (BEV), BEV drivers show obvious difference in travel behavior from gasoline vehicle (GV) drivers. To analyze BEV drivers’ charging and route choice behaviors, and extract the differences between BEV and GV drivers’ travel behavior, two multinomial logit-based and two nested logit-based models are proposed in this study based on a stated preference survey. The nested structure consists of two levels: the upper level represents the charging decision, and the lower level shows the route choices corresponding to the charging and no-charging situations respectively. The estimated results demonstrate that the nested structure is more appropriate than the multinomial structure. Meanwhile, it is observed that the initial state of charge (SOC) at origin of BEV is the most important factor that affects the decision of charging or not, and the SOC at destination becomes an important impact factor affecting BEV drivers’ route choice behavior. As for the route choice behavior when BEV has charging demand, the charging station attributes such as charging time and charging station’s location have significant influences on BEV drivers’ decision-making process. The results also show that BEV drivers incline to choose the routes with charging station having less charging time, being closer to origin and consistent with travel direction. Finally, based on the proposed models, a series of numerical analysis has been conducted to verify the effect of range anxiety on BEV charging and route choice behavior and to reveal the variation of comfortable initial SOC at origin with travel distance. Meanwhile, the effects of charging time and distance from origin to charging station also have been discussed.     

Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions

This research intends to explore external factors affecting driving safety and fuel consumption, and build a risk and fuel consumption prediction model for individual drivers based on natural driving data. Based on 120 taxi drivers’ natural driving data during 4 months, driving behavior data under various conditions of the roadway, traffic, weather, and time of day are extracted. The driver's fuel consumption is directly collected by the on-board diagnostics (OBD) unit, and safety index is calculated based on Data Threshold Violations (DTV) and Phase Plane Analysis with Limits (PPAL) considering speed, longitudinal and lateral acceleration. By using a linear mixed model explaining the fixed effect of the external conditions and the random effect of the driver, the influences of various external factors on fuel consumption and safety are analyzed and discussed. The prediction model lays a foundation for drivers' fuel consumption and risk prediction in different external conditions, which could help improve individual driving behavior for the benefit of both fuel consumption and safety.     

基于注意力机制的多模态自动驾驶行为决策模型

针对现有端到端自动驾驶模型未考虑驾驶场景中不同区域的重要性和不同语义类别之间的关系而导致预测准确率低的问题，受驾驶人注意力机制和现有端到端自动驾驶模型的启发，充分考虑驾驶场景的动态变化、驾驶场景的语义信息和深度信息对驾驶行为决策的影响，以连续多帧驾驶场景的RGB图像为输入，构建一种基于注意力机制的多模态自动驾驶行为预测模型，实现对方向盘转角和车速的准确预测。首先，通过语义分割模型和单目深度估计模型分别获取RGB图像的语义图像和深度图像；其次，为剔除与驾驶行为决策无关信息，以神经科学和空间抑制理论为基础，设计一种拟人化注意力机制作为能量函数来计算驾驶场景中不同区域的重要度；为学习语义图像中与驾驶行为决策最为相关类别之间的关系，采用图注意力网络(Graph Attention Network,GAT)对驾驶场景的语义图像进行特征提取；然后，以保留RGB特征为原则对提取的驾驶场景的图像特征、语义特征和深度特征进行融合，采用卷积长短期记忆网络(Convolutional Long Short Term Memory,ConvLSTM)实现融合特征在连续多帧之间的传递，进而实现下一帧驾驶场景对应驾驶行为的预测；最后，与其他模型的对比试验、消融试验、泛化试验和特征可视化试验来充分验证所提出自动驾驶行为预测模型的性能。试验结果表明：与其他驾驶行为预测模型相比，所提出模型的训练误差为0.021 2，预测准确率为86.97%，均方误差为0.031 5，其驾驶行为的预测性能优于其他模型；连续多帧的语义图像和深度图像、拟人化注意力机制和面向语义特征提取的GAT有助于提升驾驶行为预测的性能；该模型具有较好的泛化能力,其做出驾驶行为预测所依赖的特征与经验丰富的驾驶人所关注的特征基本一致。     

圆钢管混凝土柱低周反复加载试验研究

为了研究钢管混凝土柱在低周反复荷载作用下组合材料横截面刚度对其性能的影响,以果园港二期工程上的钢管混凝土柱为原型,对3根钢管混凝土柱进行低周反复加载试验。通过控制试件钢管厚度进行物理模型试验,研究钢管厚度对钢管混凝土柱耗能性能、承载性能、强度退化、刚度退化、延性和变形能力的影响。试验结果表明:随着试件钢管厚度的增加,试件的能量耗散系数与等效黏滞阻尼系数均随之减小,试件的耗能能力随之变弱;钢管厚度越小,钢管混凝土柱试件的耗能性能越好。钢管越厚,对核心混凝土的约束作用就越强,强度退化就越弱,试件塑性变形能力就越好。钢管越厚,外包钢管对核心混凝土约束作用就越强,水平承载能力越高。     

风化岩层中开口钢管桩沉桩性状*

终锤贯入度、桩端持力层和极限承载力是保证基桩施工质量的要素。采用高应变全程动测试桩研究不同壁厚开口钢管桩在珊瑚礁灰岩地质中的沉桩性状,主要包括测试承载力、沉桩贯入度、打桩应力以及桩身传递能量等关键参数,并通过分析480根开口钢管桩的沉桩规律,得出以下结论:基桩持力层应选择强风化珊瑚礁灰岩,终锤判定条件应同时考虑终锤贯入度和入强风化岩深度2个指标。     

钢纤维混凝土隧道衬砌破坏模式及承载能力研究

分析归纳了钢纤维混凝土的基本特性,对素混凝土、钢筋混凝土以及钢纤维混凝土衬砌的变形特性进行了比较分析;通过模型试验,研究了钢纤维的阻裂作用,提出了考虑拉力软化关系的钢纤维混凝土衬砌结构的破坏模式和承载能力分析方法。其研究成果可供钢纤维混凝土衬砌设计参考和利用。     

互见中船舶的不协调行为

驾驶员操船行为的不协调是造成海难事故的重要因素之一。结合实例,阐述了互见中船舶不协调行为对航行安全的危害、产生原因以及各种预防措施。     

Reintroducing attitude theory in travel behavior research: The validity of an interactive interview procedure to predict car use

A methodological challenge is to develop methods which satisfy the need in transport planning of accurately forecasting travel behavior. Drawing on a review of the current state of attitude theory, it is argued that successfully forecasting travel behavior relies on a distinction between planned, habitual, and impulsive travel. Empirical illustrations are provided in the form of stated-response data from two experiments investigating the validity of an interactive interview procedure to predict household car use for different types of trips, either before or after participants were required to reduce use.