On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

The Macroscopic Fundamental Diagram (MFD) has been recognized as a powerful framework to develop network-wide control strategies. Recently, the concept has been extended to the three-dimensional MFD, used to investigate traffic dynamics of multi-modal urban cities, where different transport modes compete for, and share the limited road infrastructure. In most cases, the macroscopic traffic variables are estimated using either loop detector data (LDD) or floating car data (FCD). Taking into account that none of these data sources might be available, in this study we propose novel estimation methods for the space-mean speed of cars based on: (i) the automatic vehicle location (AVL) data of public transport where no FCD is available; and (ii) the fused FCD and AVL data sources where both are available, but FCD is not complete. Both methods account for the network configuration layout and the configuration of the public transport system. The first method allows one to derive either uni-modal or bi-modal macroscopic fundamental relationships, even in the extreme cases where no LDD nor FCD exist. The second method does not require a priori knowledge about FCD penetration rates and can significantly improve the estimation accuracy of the macroscopic fundamental relationships. Using empirical data from the city of Zurich, we demonstrate the applicability and validate the accuracy of the proposed methods in real-life traffic scenarios, providing a cross-comparison with the existing estimation methods. Such empirical comparison is, to the best of our knowledge, the first of its kind. The findings show that the proposed AVL-based estimation method can provide a good approximation of the average speed of cars at the network level. On the other hand, by fusing the FCD and AVL data, especially in case of sparse FCD, it is possible to obtain a more representative outcome regarding the performance of multi-modal traffic.     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.     

Urban traffic state estimation: Fusing point and zone based data

Loop detectors are the oldest and widely used traffic data source. On urban arterials, they are mainly installed for signal control. Recently state-of-the art Bluetooth MAC Scanners (BMS) has significantly captured the interest of stakeholders for exploiting it for area-wide traffic monitoring. Loop detectors provide flow – a fundamental traffic parameter; whereas BMS provides individual vehicle travel time between BMS stations. Hence, these two data sources complement each other, and if integrated should increase the accuracy and reliability of the traffic state estimation.This paper proposed a model that integrates loops and BMS data for seamless travel time and density estimation for urban signalised network. The proposed model is validated using both real and simulated data and the results indicate that the accuracy of the proposed model is over 90%.     

IN VITRO CO-STIMULATORY ACTIVITY OF HUMAN B7.2(IgV+C)PROTEIN PRODUCED BY ENGINEERED BACTERIA

It was well known that "two signals" wereboth necessary to activate T lymphocytes. The firstsignal was provided by TCR--CD, which was en-gaged with antigen peptide--MHC complex, and thesecond, also named the costimulatory signal, wasprovided by the interaction between costimulatorymolecules B7. l and B7. 2 on antigen presenting celland its counter--receptor CD28/CTLA4 on T cell.The first signal alone without costimulation willlead T cell to anergy or apoptosis['~'2. B7. 2 mayplay a cri…     

舰船雷达与AIS信息融合技术

雷达和自动识别系统(Automatic Identification System,AIS)是配备最多的2类安全助航设备,在海上船舶导航领域发挥着不可替代的作用。由于雷达与AIS提供的信息具有良好的互补性和冗余性,二者融合可以改善探测性能、提高导航系统的精度和可靠性,是近年来海上导航技术的重点发展方向。本文首先介绍雷达/AIS信息融合技术,接着总结其国内外研究现状,并重点阐述航迹关联和信息融合两项关键技术的研究现状,最后提出雷达/AIS信息融合技术发展中待解决的问题,并展望其未来发展趋势。     

Safety Protection Method of Rehabilitation Robot Based on fNIRS and RGB-D Information Fusion

In order to improve the safety protection performance of the rehabilitation robot,an active safety protection method is proposed in the rehabilitation scene.The oxyhemoglobin concentration information and RGB-D information are combined in this method,which aims to realize the comprehensive monitoring of the invasion target,the patient's brain function movement state,and the joint angle in the rehabilitation scene.The main focus is to study the fusion method of the oxyhemoglobin concentration information and RGB-D information in the rehabilitation scene.Frequency analysis of brain functional connectivity coefficient was used to distinguish the basic motion states.The human skeleton recognition algorithm was used to realize the angle monitoring of the upper limb joint combined with the depth information.Compared with speed and separation monitoring,the protection method of multi-information fusion is safer and more comprehensive for stroke patients.By building the active safety protection platform of the upper limb rehabilitation robot,the performance of the system in different safety states is tested,and the safety protection performance of the method in the upper limb rehabilitation scene is verified.     

高速公路互通立交匝道非完整缓和曲线计算

在高速公路互通立交匝道线形设计中，非完整缓和曲线上任意一点坐标计算较为困难，通过公式推导，提出一种简易适用的计算方法，其在工程中的应用具有一定的指导作用。     

基于时延数据融合的港口船舶监控策略研究

在对现有港口船舶航行监控方法以及导航技术详细分析的基础上,结合基于Kalman滤波的数据融合技术,讨论了利用雷达、GPS以及AIS数据进行港口船舶导航算法和监控策略的设计问题。针对现有的时延平滑估计存在的缺点,提出一种基于时延的集中式预测估计融合算法。新算法具有很高的实时性和可实施性,同时,将新算法与现有的时延平滑估计融合算法相集成,提出一种完整的船舶导航和监控策略,计算机仿真算例验证了新算法的优越性,同时显示了新监控策略的有效性。     

VTS中雷达和AIS信息融合算法探讨

提出VTS中雷达和AIS信息时间统一性的算法,采用K近邻域法和基于统计的加权估计法对雷达和AIS信息进行相关判别和融合,提高了VTS对目标的跟踪性能和可靠性。     

关于赶工费用的不完全信息博弈分析

业主与承包商签订“赶工附加协议”的过程是双方针对赶工费用博弈的过程,本文分析了双方的谈判策略,并运用博弈模型进行了均衡分析,以期能指导双方的谈判,实现更优的经济效率.