Validation of an extended discrete first-order model with variable speed limits

This paper validates the prediction model embedded in a model predictive controller (MPC) of variable speed limits (VSLs). The MPC controller was designed based on an extended discrete first-order model with a triangular fundamental diagram. In our previous work, the extended discrete first-order model was designed to reproduce the capacity drop and the propagation of jam waves, and it was validated with reasonable accuracy without the presence of VSLs. As VSLs influence traffic dynamics, the dynamics including VSLs needs to be validated, before it can be applied as a prediction model in MPC. For conceptual illustrations, we use two synthetic examples to show how the model reproduces the key mechanisms of VSLs that are applied by existing VSL control approaches. Furthermore, the model is calibrated by use of real traffic data from Dutch freeway A12, where the field test of a speed limit control algorithm (SPECIALIST) was conducted. In the calibration, the original model is extended by using a quadrangular fundamental diagram which keeps the linear feature of the model and represents traffic states at the under-critical branch more accurately. The resulting model is validated using various traffic data sets. The accuracy of the model is compared with a second-order traffic flow model. The performance of two models is comparable: both models reproduce accurate results matching with real data. Flow errors of the calibration and validation are around 10%. The extended discrete first-order model-based MPC controller has been demonstrated to resolve freeway jam waves efficiently by synthetic cases. It has a higher computation speed comparing to the second-order model-based MPC.     

A methodology for evaluating the performance of model-based traffic prediction systems

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California.     

Studying the benefits of carpooling in an urban area using automatic vehicle identification data

Carpooling has been considered a solution for alleviating traffic congestion and reducing air pollution in cities. However, the quantification of the benefits of large-scale carpooling in urban areas remains a challenge due to insufficient travel trajectory data. In this study, a trajectory reconstruction method is proposed to capture vehicle trajectories based on citywide license plate recognition (LPR) data. Then, the prospects of large-scale carpooling in an urban area under two scenarios, namely, all vehicle travel demands under real-time carpooling condition and commuter vehicle travel demands under long-term carpooling condition, are evaluated by solving an integer programming model based on an updated longest common subsequence (LCS) algorithm. A maximum weight non-bipartite matching algorithm is introduced to find the optimal solution for the proposed model. Finally, road network trip volume reduction and travel speed improvement are estimated to measure the traffic benefits attributed to carpooling. This study is applied to a dataset that contains millions of LPR data recorded in Langfang, China for 1 week. Results demonstrate that under the real-time carpooling condition, the total trip volumes for different carpooling comfort levels decrease by 32–49%, and the peak-hour travel speeds on most road segments increase by 5–40%. The long-term carpooling relationship among commuter vehicles can reduce commuter trips by an average of 30% and 24% in the morning and evening peak hours, respectively, during workdays. This study shows the application potential and promotes the development of this vehicle travel mode.     

基于人头目标检测的商场人流量自动统计

针对商场人流量自动统计中行人遮挡情况下计数的困难,提出基于人头目标检测的商场人流量自动统计算法.首先提取Haar特征训练Adaboost人头目标分类器,再利用Camshift算法对目标进行跟踪,使用Kalman缩小跟踪目标的搜索范围,最后利用人头模板匹配方法,判断目标是否属于同一行人.实验结果表明,此方法平均正确率达到98.2%,且统计每个行人目标只需19 ms.     

基于双轴位置转台的捷联惯导系统级标定技术

本文研究一种基于双轴位置转台的激光陀螺捷联惯导系统的系统级标定方法。建立了附加约束条件的陀螺和加速度计标定模型,从捷联惯导系统的误差方程出发,建立30维Kalman滤波标定模型,以速度和位置作为观测量,并设计双轴位置旋转路径,对惯性仪表标定参数进行激励与辨识。仿真实验结果表明,该方法能够准确估计出陀螺和加速度计的15个标定误差参数,具有一定的参考价值。     

激光路面断面检测系统的功能与应用探讨

激光路面断面测试系统是先进的路面性能检测系统,它具有完善的路面性能指标采集和处理功能,能在不同行驶速度下检测各种干湿路面状况,准确、高效采集和处理路面状况性能数据.论文介绍了该激光路面断面测试系统的组成、构造及功能,并对其路用性能指标的标定和应用进行了探讨.     

基于ADVISOR的动力耦合系统参数匹配及仿真

混合动力汽车的动力耦合系统结构和参数匹配影响整车性能,通过对动力耦合系统的结构选择、参数匹配和控制策略的研究,以传统汽车的差速器作为动力耦合器并建立模型,并对此模型进行参数匹配,通过使用ADVISOR软件中修改控制策略模块,并将修改后的Simulink模型嵌入ADVISOR软件中进行仿真,验证选择方案对于改善汽车动力性和燃油经济性的有效性。     

柴油机车辆冒黑烟故障的原因分析

通过控制环境温度和中冷后进气温度,对某柴油发动机进行单变量因素台架试验,并对其结果影响进行分析;同时结合整车匹配设计与ECU数据标定,分析车辆运行过程中冒黑烟的原因及提出相应解决措施。     

关于图的谱半径的新的上界

主要得到了一个关于图的谱半径的新的上界,并举例说明在某些情况下我们的结果是好的.     

可调螺距螺旋桨舰船船-机-桨优化匹配

近年来,我国装备调距桨的舰船越来越多,但在调距桨的实际使用过程中还存在着船-机-桨难以全面实现优化匹配的情况,从而不能发挥调距桨的优势.基于船-机-桨的关系,结合调距桨的特点,提出了调距桨舰船任意负载下的最大允许航速以及减小油耗的计算方法,为该类舰船实现船-机-桨的优化匹配提供了参考.