Multi-channel signal parameters joint optimization for GNSS terminals

Traditional global navigation satellite system(GNSS)terminals for satellite navigation adopt independent channels to track the signals from different satellites, which results in a lack of information interaction between the channels. Inspired by the vector tracking idea, and drawing lessons from the principle that in the position domain the Taylor expanded pseudorange observations can be used for positioning via the least squares method, this paper proposes a novel least squares-based multi-channel parameter joint estimation(MPJE) method in the signal domain, which not only retains the advantages of channel fusion, but also maintains the flexibility and diversity of the localization algorithm. With achieving optimal carrier to noise ratio as the goal, the proposed method obtains the required code loop and carrier loop parameters for signal tracking in the domain of whole channels. Experimental results indicate that this method fully achieves the assistant fusion advantages of frequency lock loop(FLL), phase lock loop(PLL)and delay lock loop(DLL), making good use of the robustness and dynamic properties of the FLL and the measurement accuracy of the DLL, and is helpful for achieving stable and accurate signal tracking under weak signals and high dynamic stress environments.

Study on the Signal Control System of Urban Traffic with a Genetic Algorithm

In the paper the aim and meaning of traffic microscopic simulation is discussed first, and then three sub-models of the system are established, e. i. the model for random generation of the vehicles, the model for car-following and lane change influenced by an adjacent vehicle, and the model for control and optimization of intersection with signal. Optimization of the traffic signal timing with a genetic algorithm and a microscopic simulation is carried out. It represents a novel approach to solving optimal signal timing.

Effects of winter weather on traffic operations and optimization of signalized intersections

Adverse winter weather has always been a cause of traffic congestion and road collisions.To mitigate the negative impacts of winter weather, transportation agencies are under increasing pressure to introduce weather responsive traffic management strategies.Currently, most traffic signal control systems are designed for normal weather conditions and are therefore suboptimal regarding efficiency and safety for controlling traffic during winter snow events due to changes in traffic patterns and driver behaviors. The main objective of this research is to explore how to modify pre-timed traffic signal control parameters under adverse weather conditions to increase traffic efficiency and road safety.This research consists of two main components. First, we examine the impacts of winter weather on three key traffic parameters, i.e., saturation flow rate, start-up lost time, and free flow speed. Secondly, we investigate the potential benefits of implementing weatherspecific signal control plans for uncoordinated intersections as well as coordinated corridors. Two case studies are conducted, each with varying levels of traffic demand and winter event severity, to compare the performance of different signal plans. Evaluation results from both Synchro and VISSIM show that implementing such signal plans is most beneficial for intersection with a medium level of traffic demand. It is also found that the benefit of implementing weather-responsive plans was more compelling at a coordinatedcorridor level than at an uncoordinated-intersection level.

On the Range of the Common Cycle Time in a Road Network

In a coordinated road network,the optimal common cycle time is determined by evaluating the performance of the network in the given range of cycles. Normally,this range is determined by users 'experience. And a large range of common cycle time,e. g. [30,200] is chosen,which requires long computation time. This study considers that the optimal common cycle time ranges between the minimal and maximal value of intersections' individual optimal cycle time. It is proved mathematically from the convexity condition,that the delay of the network and each individual intersection are convex functions of the cycle time according to Webster delay model. Finally,2 000 random cases for the network composed of two intersections and of eight intersections are created to underline the proposed conclusions. The results of all cases confirm the validity,and show up to 90% improvement in computation time to compare with experience range. The signal optimization tool,Synchro,is also used to validate the conclusion by 50 random cases. The results confirm reliability further.

‘Outbreak Gold Standard’Selection to Provide Optimized Threshold for Infectious Diseases Early-alert Based on China Infectious Disease Automated-alert and Response System

The China Infectious Disease Automated-alert and Response System（CIDARS） was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control（CDC） at all levels in China. In the CIDARS, thresholds are determined using the ？Mean＋2SD？ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the ？Mean ＋2SD？ method to the performance of 5 novel algorithms to select optimal ？Outbreak Gold Standard（OGS）？ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The ？Mean＋2 SD？, C1, C2, moving average（MA）, seasonal model（SM）, and cumulative sum（CUSUM） algorithms were applied. Outbreak signals for the predicted value（Px） were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A（chickenpox and mumps）, TYPE B（influenza and rubella） and TYPE C [hand foot and mouth disease（HFMD） and scarlet fever]. Optimized thresholds for chickenpox（P_（55））, mumps（P_（50））, influenza（P_（40）, P_（55）, and P_（75））, rubella（P_（45） and P_（75））, HFMD（P_（65） and P_（70））, and scarlet fever（P_（75） and P_（80）） were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

DOA estimation via sparse recovering from the smoothed covariance vector

A direction of arrival（DOA） estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector（SCV） is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors（MMVs）. Simulation results validate the effectiveness and efficiency of the proposed algorithm.

High-Resolution Traffic Flow Prediction Model Based on Deep Learning

The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing optimization.Based on the view of the platoon dispersion model,the relationship between vehicle arrival at the downstream intersection and vehicle departure from the upstream intersection was analyzed.Then,a high-resolution traffic flow prediction model based on deep learning was developed.The departure flow rate from the upstream and the arrival flow rate at the downstream intersection was taking as the input and output in the proposed model,respectively.Finally,the parameters of the proposed model were trained by the field data,and the proposed model was implemented to forecast the arrival flow rate of the downstream intersection.Results show that the proposed model can better capture the fluctuant traffic flow and reduced MAE,MRE,and RMSE by 9.53%,39.92%,and 3.56%,respectively,compared with traditional models and algorithms,such as Robert­son's model and artificial neural network.Therefore,the proposed model can be applied for realtime adaptive signal timing optimization.

Gravitational Telescope

It’s proposed to use a global seismic antenna (GSA) as a gravitational telescope, arbitrary “quiet” seismic stations are its elements, and aperture of GSA must be of the order 10,000 km. The relative displacements of various points of the Earth are detected by GSA, these displacements are described as quasi-harmonic elliptical signals generated by gravitational waves, their amplitude ≈ 2.5 * 0?15 m. It is found that these waves cause deformation (strain) of the order h ≈ 10?21. Pulsars are a natural source of periodic waves. The fact of confident registration of gravitational wave is confirmed by detection of quasi-harmonic signals in the frequency band near 6.023 Hz for 90 hours (confidence probability of detection is close to 1). It is found that a small part of the rotation energy of associated pulsar (ε ≈ 10?5) is expended on the radiation corresponding to the gravitational wave.

Recent advances in traffic signal performance evaluation

Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions.However,the abundance of data sources,the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor.The review detailed in this paper examines the advances made in traffic signal performance evaluation.We establish the necessity for the evaluations,study the process of continuous improvement of traffic signal performance using the evaluations,and then examine multiple methodologies in a plethora of research endeavors.Particularly,we focus on probe vehicles and sensors data,the two major sources of data.We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures.We also review the nature of probe vehicles and the level of penetration.We then define and summarize performance measures derived from both sources,to aid in performance evaluations.For performance evaluation methods,we discuss the research studies and provide summaries including advantages and disadvantages of the methods used,as well as a holistic outlook for future research.This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher,traffic agencies,and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations.