Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.     

A pattern recognition algorithm for assessing trajectory completeness

Vehicular trajectories are widely used for car-following (CF) model calibration and validation, as they embody characteristics of individual driving behaviour (each trajectory reflects an individual driver). Previous studies have highlighted that the trajectories should contain all the major vehicular interactions (driving regimes) between the leader and the follower for reliable CF model calibration and validation. Based on Dynamic Time Warping and Bottom-Up algorithms, this paper develops a pattern recognition algorithm for vehicle trajectories (PRAVT) to objectively, accurately, and automatically differentiate different driving regimes in a trajectory and then select the most complete trajectories (i.e. trajectories containing a maximum number of regimes). PRAVT is rigorously tested using synthetic data and then applied to the NGSIM data. We have observed that the NGSIM data are dominated by the trajectories which contain only three regimes, namely acceleration, deceleration, and following, 77% of the trajectories lack the standstill regime, and no trajectory in the NGSIM data is complete. These findings’ impact on how to properly utilize NGSIM data can be profound. Given the extensive use of the NGSIM data in the traffic flow community, this paper also provides insights about the types of regimes contained in each trajectory of the NGSIM data.     

A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections

This paper proposes a reformulation of count models as a special case of generalized ordered-response models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in practice. A composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Arlington, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files between 2003 and 2009, resulting in 1190 intersection-year observations. The results reveal the presence of intersection-specific time-invariant unobserved components influencing crash propensity and a spatial lag structure to characterize spatial dependence. Roadway configuration, approach roadway functional types, traffic control type, total daily entering traffic volumes and the split of volumes between approaches are all important variables in determining crash frequency at intersections.     

深水缓波型刚性立管的设计方法

缓波型刚性悬链线立管由于其良好的力学性能和较低的成本,适用于深水油气田开发。本文详述了缓波型刚性悬链线立管的设计准则和设计流程,给出了分布式浮块的设计方法和等效原则,推导了等效水动力参数的计算公式。通过静力分析,得到了不同浮块的布置位置和浮力系数对立管形态、顶端张力、弯矩分布的影响;通过动力分析,得到了沿管等效应力响应的分布规律,为缓波型刚性悬链线立管的设计提供了有益的参考。     

着水姿态对大型水陆两栖飞机着水性能的影响

针对着水姿态对水陆两栖飞机船体着水性能的影响,通过单船身(机身)模型着水试验与仿真,分析着水载荷和运动响应,单船身模型可排除气动力及复杂的水面效应等因素引起的试验与仿真的差异。结果表明仿真与试验结果一致性较好,压力分布满足设计要求;初始姿态角对压力分布及加速度的时历变化影响不大,但艉部着水前期姿态角会出现减小的现象;加速度和姿态角峰值随着初始姿态角的增大会出现明显的波谷型变化。综合看来,该型水陆两栖飞机着水性能在6°-8°姿态角范围内较优,7°最佳。     

水面小尺度漂浮平台动力学响应分析

针对由弹性连接件形成的多浮体系统,基于修正的莫里森公式应用微元法推导了主平台和浮囊的受力模型,建立了水面小尺度漂浮平台的动力学方程,并分析了连接件刚度和浮囊直径对主平台幅值运动响应的影响。通过仿真和试验结果对比验证了理论模型的正确性。结果表明,连接件刚度对主平台幅值运动响应的影响远大于浮囊直径,主平台的幅值响应随连接件刚度呈"W"变化,最优的连接件刚度为40×10~4N/m;主平台的幅值响应随浮囊直径呈微弱增长,最优的浮囊直径为2.5 m。研究结果可为水面小尺度漂浮平台的弹性连接件和浮囊参数的设计提供依据。     

信号动态检测系统的应用

信号动态检测系统为信号设备动态检查,信号设备运用质量分析,指导现场维修带来了便利,同时提高了劳动生产效率,减轻了现场劳动强度,保障了运输安全。     

含大量块石、碎石回填土强夯地基加固效果检测

通过某一工程实例来说明采用载荷试验、动力触探试验、瑞利波试验检测含大量块石、碎石回填土强夯地基加固效果。     

LY温拌剂对沥青混合料性能影响研究

LY温拌剂是一种使用多种废旧材料复合而成的温拌添加剂。通过对LY温拌沥青混合料和热拌沥青混合料的性能测试及比较,结果表明：添加该温拌剂对沥青混合料的性能提升效果明显,优于对应热拌沥青混合料。     

顶置凸轮轴配气机构有限元动力模型计算及试验研究

采用有限元动力模型，对顶置凸轮轴配气机构进行了动力分析计算，在已知凸轮升程数据的条件下，求出凸轮轴不同转速时的气门运动规律，配气机构各阶自振频率和振动振型，并研究了油温，油压及混入气泡量不同时，气门间隙液压调节器对配气机构运动特性的影响。