Modeling lane-changing behavior in a connected environment: A game theory approach

Vehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. A connected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior, as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information. This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment. A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed. Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model.     

From behavioral psychology to acceleration modeling: Calibration,validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

We investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory – prospect theory – into an efficient microscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment.After model formulation, we explore different model disaggregate and aggregate characteristics making sure that fidelity is kept in terms of equilibrium properties. Significant effort is then dedicated to calibrating and validating the model using microscopic trajectory data. A modified genetic algorithm is adopted for this purpose while focusing on capturing inter-driver heterogeneity for each of the parameters. Using the calibration exercise as a starting point, simulation sensitivity analysis is performed to reproduce different fundamental diagram regions and to explore rear-end collisions related properties. In terms of fundamental diagram regions, the model in hand is able to capture traffic breakdowns and different instabilities in the congested region represented by flow-density data points scattering. In terms of incident related measures, the effect of heterogeneity in both psychological factors and execution/perception errors on the accidents number and their distribution is studied. Through sensitivity analysis, correlations between the crash-penalty, the negative coefficient associated with losses in speed, the positive coefficient associated with gains in speed, the driver’s uncertainty, the anticipation time and the reaction time are retrieved. The formulated model offers a better understanding of driving behavior, particularly under extreme/incident conditions.     

Impact of stop-and-go waves and lane changes on discharge rate in recovery flow

In an effort to uncover traffic conditions that trigger discharge rate reductions near active bottlenecks, this paper analyzed individual vehicle trajectories at a microscopic level and documented the findings. Based on an investigation of traffic flow involving diverse traffic situations, a driver’s tendency to take a significant headway after passing stop-and-go waves was identified as one of the influencing factors for discharge rate reduction. Conversely, the pattern of lane changers caused a transient increase in the discharge rate until the situation was relaxed after completing the lane-changing event. Although we observed a high flow from the incoming lane changers, the events ultimately caused adverse impacts on the traffic such that the disturbances generated stop-and-go waves. Based on this observation, we regard upstream lane changes and stop-and-go waves as the responsible factors for the decreased capacity at downstream of active bottlenecks. This empirical investigation also supports the resignation effect, the regressive effect, and the asymmetric behavioral models in differentiating acceleration and deceleration behaviors.     

双层圆柱壳典型基座振动波传递特性优化分析

采用波动分析法,根据不均质结构中的阻抗特性和波型转换,分析振动噪声在典型双层壳结构中的传递特性。以此为切入点,采用有限元/边界元（FEM/BEM）耦合法分析人为构造的传递损失基座的减振降噪性能。通过对组合板振动波的传递特性的分析可以得出,当定常结构发生突变时,结构阻抗也会随之发生变化,导致结构之间的阻抗失配,从而使得振动波在突变截面处发生反射和透射,降低振动波的传递效率,阻隔振动波能量向下游结构传递。然后,据此理论设计了传递损失基座,并用有限元/边界元耦合法验证了传递损失基座的减振降噪性能。     

基于BIM的高铁工程量清单EBS＼WBS研究

为解决BIM与现行铁路工程量清单计价标准的差异,促进BIM技术在我国高铁信息化建设和造价精细化管理中的发展,基于BIM对铁路工程量清单分解结构进行优化。分析现行铁路工程量清单计价标准与铁路BIM标准在工程分解结构上的异同,运用模块化理论提出基于BIM的高铁工程模块化分解思路,并对国内外建筑信息分类体系、交付精度标准等进行分析。建立了符合BIM建模特点且满足总承包模式及全过程造价管理的三级高铁工程量清单EBS\WBS,并进一步定义各级分解结构的特征描述类型。研究结论:铁路工程量清单与铁路BIM标准在工程分解的原则、对象以及信息表达方式方面均有差异,而基于BIM的三级高铁工程量清单与铁路BIM标准可在EBS\WBS上达成协同,为实现基于BIM的高铁工程量清单计价模式奠定了基础。     

基于前景理论和直觉模糊集的编组站布局调整决策研究

为提升编组站布局调整方案的决策水平,考虑未来不同的运输需求增长状态对决策者行为心理的影响,提出基于前景理论与直觉模糊集的编组站布局调整决策模型。首先,构建编组站布局调整方案比选指标体系,涵盖空间分布的合理性、适应性、协调性、对社会的影响、经济性5个指标层;其次,利用直觉模糊集表征决策者对方案属性的主观评价值及犹豫度,并构造得分函数得到方案属性的综合得分值;然后,引入前景理论考虑决策者在不同运输需求增长状态下的不确定型偏好,结合灰靶决策理论构造三参照点计算编组站布局调整方案的综合前景价值;最后采用VIKOR决策方法对方案进行优选。通过某枢纽编组站布局调整实例,研究结果表明,A2为最优调整方案,与实际确定方案相吻合,验证了决策模型的有效性和实用性。     

基于智能手机图像采集方法的沥青路面表面离析评价指标优选

为优选沥青路面表面离析评价指标,室内成型了3种不同级配类型的沥青混合料车辙板试件;采用智能手机,提出了一种图像采集方法,获取了沥青混合料表面图像;基于盒子分形维数和多重分形谱算法计算试件表面二值图像的分形维数和多重分形谱指标,依托Image-Pro Plus图像处理软件提取二值图像凹形分布百分数和宏观构造宽度指标,采用室内铺砂法测试表面构造深度,计算平均构造深度来评价沥青混合料表面离析;分析了不同级配组成沥青混合料表面凹形区域分布特征,研究了沥青混合料表面离析评价指标间灰关联熵。研究结果表明：基于智能手机来获取二值图像的可靠性分析误差率不超过3%,说明基于智能手机获取试件表面图像方法具有较高可重复性;沥青混合料表面凹形构造具有显著分形特征,集料颗粒粒径越大,表面构造分形越复杂;基于图像处理方法的沥青路面表面离析评价指标与平均构造深度指标间存在线性相关性,但相关程度不一;同一级配沥青混合料表面分形特征指标计算误差范围较大,而凹形分布百分数指标误差范围最小,仅为±1.89%,且其与平均构造深度指标的熵关联度最高,为0.996 2,其次分别是多重分形谱、分型维数和宏观构造宽度指标;综合考虑推荐沥青混合料表面凹形分布百分数可作为可靠的表面离析评价指标。     

基于数据分类的高速铁路运营事故预测方法

高速铁路运营事故预测方法是度量铁路安全管理水平的重要指标.为提高高速铁路的安全运营水平,引入工业数据分类方法,分析反向传播(BP)神经网络和灰色模型在高速铁路安全运营事故预测过程中的适应性.首先,运用事故次数、事故联动系数、月均事故率3个参数对高速铁路安全运营水平进行度量;然后,根据工业数据分类方法判别高速铁路运营事故数据属于块状型,据此建立反向传播(BP)神经网络运营事故预测模型;针对运营事故数据具有波动大的特点,利用均值聚类方法建立K-GM(1,3)预测模型.以近年来高速铁路运营事故数据为样本对模型进行训练和分析,结果表明:BP神经网络、K-GM(1,3)、GM(1,3)预测模型的预测误差分别为8.92%,13.68%,345.25%,BP神经网络在高速铁路安全运营事故预测过程中的适应性要优于灰度模型.     

无限区域二维势流直接边界元法精度分析

边界元法作为一种重要的数值方法已在许多领域得到广泛应用,但在船舶水动力势流理论数值计算方面,有关直接边界元法的研究并不充分,尤其是在船舶兴波阻力势流理论求解方面,以往的“面元法”通常是基于Hess-Smith法的间接法,这类方法在理论和数值计算上都存在着缺陷。针对船舶水动力势流理论计算,采用直接边界元法,对二维势流无界绕流算例进行系统的数值计算,并根据二维势流问题的计算结果详细探讨边界单元离散形式和单元上的数值积分方法对计算精度的影响,各项数值计算均以Matlab软件编程实现。结果表明,采用常数单元和龙贝格积分法能够得到较准确的结果,且计算速度较快。     

一种加载集总器件的可调三维周期结构

结合实际周期结构单元的特点,从三维周期结构出发,推导三维周期结构电磁波传输原理。运用商业软件数值仿真计算方法,研究三维周期结构单元尺寸、介质层厚度对其频响特性的影响;从电磁波入射角度和集总器件电抗值出发,对不同参数进行仿真计算,并比较不同参数对谐振特性的影响。实现第1谐振点稳定,第2谐振点可调谐的谐振特性。此外,将三维周期结构加入到天线设计中,研究该结构对天线辐射特性的影响,发现该三维结构可有效改善天线的方向性。