Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

Reliability Analysis of Satellite Turntable System under Multiple Operation Modes Based on Multi-Valued Decision Diagrams

As a payload support system deployed on satellites,the turntable system is often switched among different working modes during the on-orbit operation,which can experience great state changes.In each mode,the missions to be completed are different,consecutive and non-over-lapping,from which the turntable system can be considered to be a phased-mission system(PMS).Reliability analysis for PMS has been widely studied.However,the system mode cycle characteristic has not been taken into account before.In this paper,reliability analysis method of the satellite turntable system is proposed considering its multiple operation modes and mode cycle characteristic.Firstly,the multi-valued decision diagrams(MDD)manipulation rules between two adjacent mission cycles are proposed.On this basis,MDD models for the turntable system in different states are established and the reliability is calculated using the continuous time Markov chains(CTMC)method.Finally,the comparative study is carried out to show the effectiveness of our proposed method.

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive Real-Time Control of Flexible Manufacturing Systems

This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.

Modeling and Design of Real-Time Pricing Systems Based on Markov Decision Processes

A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.

Variance minimization for continuous-time Markov decision processes: two approaches

This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance minimization optimality equation and the existence of a variance minimal policy that is canonical, but also the existence of solutions to the two variance minimization optimality inequalities and the existence of a variance minimal policy which may not be canonical. An example is given to illustrate all of our conditions.

A Simple Additive Weighting Method for Time-SeriesMultiindices Decision Making and Its Applications

This paper takes the evaluation of overall economic benefit by an example and proposes a simple additive weighting method for time-series multiindices decision making. The method can automatically determine the weight coefficients among the multiindices and the years respectively and it also can obtain the objective evaluation results and conclusions.

Variance Optimization for Continuous-Time Markov Decision Processes

This paper considers the variance optimization problem of average reward in continuous-time Markov decision process (MDP). It is assumed that the state space is countable and the action space is Borel measurable space. The main purpose of this paper is to find the policy with the minimal variance in the deterministic stationary policy space. Unlike the traditional Markov decision process, the cost function in the variance criterion will be affected by future actions. To this end, we convert the variance minimization problem into a standard (MDP) by introducing a concept called pseudo-variance. Further, by giving the policy iterative algorithm of pseudo-variance optimization problem, the optimal policy of the original variance optimization problem is derived, and a sufficient condition for the variance optimal policy is given. Finally, we use an example to illustrate the conclusion of this paper.

Adaptive Spectr um Decision Method for Heterogeneous Cognitive Radio Networks

To improve spectrum utilization and minimize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Secondary User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum decision, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the interrupted service of SU as newly incoming and re-decision process is initialized to find available spectrum in a First-Available-First-Access (FAFA) fashion. Effect of sensing error in PHY layer is also analyzed in terms of extended average residual time. Simulation results show that, for relatively low arriving rate of SU traffic, the proposed spectrum decision method yields at least a delay reduction of 39.5% compared with non-adaptive method. The proposed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.

Canadian children’s and youth’s adherence to the 24-h movement guidelines during the COVID-19 pandemic: A decision tree analysis

Purpose:The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more and less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-2019(COVID-19)outbreak.Methods:Data for this study were from a nationally representative sample of 1472 Canadian parents(Meanage=45.12,SD=7.55)of children(511 years old)or youth(1217 years old).Data were collected in April 2020 via an online survey.Survey items assessed demographic,behavioral,social,micro-environmental,and macro-environmental characteristics.Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation(physical activity(PA),screen time,and sleep)were generated.Results:Results revealed specific combinations of adherence and non-adherence characteristics.Characteristics associated with adherence to the recommendation(s)included high parental perceived capability to restrict screen time,annual household income ofCAD 100,000,increases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,being a boy,having parents younger than 43 years old,and small increases in children’s and youth’s sleep duration since the COVID-19 outbreak began.Characteristics associated with non-adherence to the recommendation(s)included low parental perceived capability to restrict screen time,youth aged 1217 years,decreases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,primary residences located in all provinces except Quebec,low parental perceived capability to support children’s and youth’s sleep and PA,and annual household income ofCAD 99,999.Conclusion:Our results show that specific characteristics interact to contribute to(non)adherence to the movement behavior recommendations.Results highlight the importance of targeting parents’perceived capability for the promotion of children’s and youth’s movement behaviors during challenging times of the COVID-19 pandemic,paying particular attention to enhancing parental perceived capability to restrict screen time.

A Cooperation-planning Model Based on Bilevel Programming Decision

This paper is based on a resource constrained active network project;the constraint of the local resource and the time constraint of the cooperation resource are considered simultaneously.And the respective benefit of the manager and cooperation partners is also considered simultaneously.And a cooperation planning model based on bilevel multi-objective programming is de- signed,according to the due time and total cost.And an extended CNP based on the permitted range for resource and time requests is presented.A larger task set in scheduling cycle is on the permitting for the request of cooperation resource and time while the task manager itself may be permitted biding for tasks.As a result,the optimization space for the cooperation planning is enlarged.So not every bidding task is successfully bid by invitee,and the task manager itself takes on some bidding tasks.Finally,the genetic algorithm is given and the validity and feasibility of the model is proved by a case.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Analysis and Study of Parallel Processing Mode inVLDB Decision Support System

Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).

Maximum Expected Benefit-Oriented Optimal Maintenance Decision for Existing Bridges

Abstract An optimal maintenance program is the key to making appropriate decisions to minimize cost and maintain an appropriate lev- el of safety. In this paper, the strategy on repairing and strengthening of existing bridges based on time-dependent reliability was analyzed with the maximum expected benefit as the objective function. Risk ranking and lifecycle cost analysis were considered in the reliability-based bridge assessment. A practical application of risk-ranking decision was illustrated herein based on updated inspection informarion with the bridge at survival age 35. The effects of improvement of live load and difference of repair methods on time-dependent reliability of existing bridges were studied. The decision method can be used in real projects, with the cost of failure consequence and the risk of failure considered.

深地工程多维信息感知与智能建造的发展与展望

随着大数据、云计算、人工智能等数字技术的加速演进,各领域智能化、信息化、数字化已成为未来的大势所趋。深地工程作为国家重大战略科技问题,必然面临智能化升级。然而,深部岩体“三高一扰动”的复杂特征给深地工程智能化转型带来严峻的挑战。为实现深地工程与数字技术的高效融合,研究基于“感知-传送-解译-分析-决策”的智能化实践路径,系统回顾了地下工程中智能感知、实时传输、信息解译、数据分析、智能决策等领域的代表性研究进展,并针对性提出了“多感知、快响应、大数据、优方法、精模型、强平台、易推广”的深地工程智能建造发展方向。研究表明:①前沿的深地工程感知技术包括:光纤传感器、MEMS传感器、计算机视觉、自动化机器人等,待数据采集完毕后,通过兼具配置简单、容错能力强、可移动性好等优点的无线通信协议完成数据的实时响应,以实现深地工程监测数据的精准感知与实时传输;②深地工程原位监测技术获取的数据类型主要包括图像、波、点云等,对原始数据解译及分析的模型众多,采用新一代的人工智能技术,如:人工神经网络和深度学习技术,可显著提高解译与分析的效率;③智能决策系统具备高效的学习能力,能够适应复杂环境下的不确定性,通过循环自主学习,以进行决策问题的智能解答。当前,我国深地工程智能建造的政策与产业体系已基本建立,大量智能建造系统已应用于实践。基于此,从智能感知与信息解译、围岩评价及安全评估、围岩控制与动态修复、平台开发及应用推广等4个方面展望了数智化深地工程的发展方向,进而构建了基于多源信息的深地工程围岩稳定性综合评价与分析系统构想。

劳动生产率异质性如何影响劳动者退休时间决策?

基于人口老化与财务平衡推定提高退休年龄是劳动生产率同质假设的静态逻辑。理论上,最优退休时间取决于劳动者退休后消费水平与休闲收益的权衡。劳动生产率是能够同时影响消费水平与休闲收益的本质性变量,对劳动者退休时间决策具有双重效应。劳动生产率异质性使不同劳动者、不同时期的退休时间决策具有明显的异质特征;国家与社会有必要对异质性劳动生产率导致的异质性的劳动者最优退休时间作出回应,从而适时优化现行退休时间政策。模拟分析表明,劳动生产率异质性决定了最优退休时间的异质特征,两者之间存在显著的反向关系;在基准情境下我国劳动者的最优退休时间约为63.16岁;若要维持60岁的法定退休年龄,则必须保持9%的劳动生产率增长率。因此,摒弃劳动生产率同质假设并重视通过劳动生产率路径缓解养老金压力、实施激励相容的弹性退休计划、不断提高劳动者与社会的劳动生产率等有助于改进我国退休政策的有效性和可持续性。

新安煤矿人员定位系统升级改造的研究与应用

煤矿井下人员定位系统是加强入井人员管理、提升生命保障、促进煤矿安全生产的高端技术。新安煤矿在用的KJ251型人员定位系统安装于2011年,采用RFID定位技术实现区域定位,该技术具有实现简单、系统建设成本低的特点。随着煤矿井下智能化的推进,对人员定位提出了更高的需求,现主要存在浏览器内核版本、系统中心站软件及数据库架构满足不了当前信息化发展需求、系统数据不稳定和系统未配备唯一检卡装置等问题。结合煤矿井下特殊的作业环境,煤矿井下人员定位系统可实现对入井人员的实时监测、精确定位、轨迹回放、考勤管理、报表查询、信息网络发布、双向通信、人机交互、紧急搜救、生产调度等功能,为煤矿安全生产以及紧急救援提供第一手可靠的决策实时信息。

执行时间预测驱动的工作流作业调度

针对工作流作业调度问题,提出使用关键路径法进行工作流的执行时间预测和资源分配。工作流执行时间预测算法使用并行应用有向无环图描述工作流中子作业的执行顺序。基于此顺序,为子作业进行系统资源的逻辑分配。根据子作业的特征和资源分配信息,使用梯度提升决策树进行子作业执行时间预测,并计算工作流的关键路径。关键路径上所有子作业的完成时间之和即为工作流的执行时间。若预测的工作流执行时间满足用户要求,则根据子作业执行顺序和资源分配方案进行作业调度,执行工作流。对比实验表明,两个工作流的执行时间预测误差分别为5.72%和1.57%。与Spark默认调度算法相比,工作流调度算法将两个工作流的完成时间分别缩短了15.71%和15.44%。

考虑综合经济性的风光配电网储能优化配置

在配电网中配置储能系统可以有效缓解风光接入对配电网产生的影响,提升对风光的消纳能力、减少电压偏差等,而储能接入的位置和容量不同会对配电网产生不同的影响。首先分析储能建设过程中的主要费用,考虑基于分时电价下的网损成本和峰谷差收益,以年综合成本、配电网的日节点电压偏差和最小为目标,建立配电网储能选址定容模型,采用改进多目标遗传二代算法(improved multi-objective nondominated sorting genetic Ⅱ algorithm, INSGA-Ⅱ)对模型进行求解,利用改进理想点决策法(improved ideal point based decision, IIPBD)来选取最优配置方案,最后在IEEE-33节点模型中对方案进行验证。仿真结果表明:使用INSGA-Ⅱ算法进行求解时最终决策出的储能配置方案实现了成本最小,并且显著提升了电压的稳定性,验证了改进后算法的优良性和所提出的储能优化配置方案的有效性。

考虑健康信念的游客分时预约行为分析

为探索新冠肺炎疫情背景下景区分时预约策略、游客健康信念和风险预防行为之间的关系,需考虑健康信念,分析疫情防控期间游客分时预约行为机理。采用意愿调查法获取旅游信息、健康信念、分时预约行为意向数据,分析各预约时段余票比例和门票预售期等信息对游客分时预约行为的影响。基于Nested Logit(NL)模型,建立考虑健康信念的游客游玩日期-时段预约选择联合模型,并进行敏感性分析。结果表明:预约信息、健康信念对游客预约节假日或非节假日游玩有显著影响;票价、各预约时段余票比例和健康信念对游客游玩时段预约选择行为有显著影响。游客感知新冠的严重性由1上升至5时,选择节假日出行的概率由30.46%下降至12.28%。研究可为景区分时预约策略的制定提供参考依据。

相似还是互补?时间个性异质性对创业团队决策绩效的影响

基于IPO理论框架构建研究模型,揭示时间个性异质性对创业团队决策绩效的影响及时间压力的调节作用,基于创业模拟实验设计进行实验研究,对提出的模型和假设进行检验。研究发现:时间个性异质性对团队决策绩效存在显著正向影响,团队时间心智模型在其中发挥中介作用;时间压力调节时间个性异质性和团队时间心智模型之间的作用关系,但对团队决策绩效的调节作用不显著。研究结论能够完善现有文献对团队异质性研究的不足,为理论分歧提供新的学理解释,并为创业团队提高决策绩效提供实践启示。