scholarly journals Optimization Model of Traffic Sensor Layout considering Traffic Big Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xu Sun ◽  
Zixiu Bai ◽  
Kun Lin ◽  
Pengpeng Jiao ◽  
HuaPu Lu
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Optimization Model ◽  
Programming Model ◽  
Influential Factors ◽  
Urban Traffic ◽  
Traffic Information ◽  
Impact Factors ◽  
System Cost ◽  
The Impact

In order to improve the accuracy, reliability, and economy of urban traffic information collection, an optimization model of traffic sensor layout is proposed in this paper. Considering the impact of traffic big data, a set of impact factors for traffic sensor layout is established, including system cost, multisource data sharing, data demand, sensor failures, road infrastructure, and sensor type. The impacts of these influential factors are taken into account in the traffic sensor layout optimization problem, which is formulated in the form of multiobjective programming model that includes minimum system cost, maximum truncation flow, minimum path coverage, and an origin-destination (OD) coverage constraint. The model is solved by the tolerant lexicographic method based on a genetic algorithm. A case study shows that the model reflects the influence of multisource data sharing and fault conditions and satisfies the origin-destination coverage constraint to achieve the multiobjective optimization of traffic sensor layout.

scholarly journals Comprehend the Performance of MapReduce Programming model for K-Means algorithm on Hadoop Cluster

2019 ◽  
Vol 8 (9) ◽  
pp. 1600-1603
Keyword(s):  
Big Data ◽  
Programming Model ◽  
Primary Memory ◽  
Model Based ◽  
Considerable Research ◽  
Memory Size ◽  
Input Size ◽  
Mapreduce Model ◽  
Hadoop Cluster ◽  
The Impact

MapReduce is a programming model used for processing Big Data. There are had been considerable research in improvement of performance of MapReduce model. This paper examines performance of MapReduce model based on K Means algorithm inside the Hadoop cluster. Different input size had been taken on various configurations to discover the impact of CPU cores and primary memory size. Results of this evaluation had been shown that the number of cores had maximum impact of the performance of MapReduce model.

scholarly journals Optimal Model of Emergency Medical Supplies Stowage under Traffic Control during the Initial Period of the Epidemic Based on Characteristics Analysis of COVID-19 Epidemic

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoyuan Wu ◽  
Fengping Wu ◽  
Mengke Li ◽  
Yingwen Ji
Keyword(s):  
Traffic Control ◽  
Optimization Model ◽  
Programming Model ◽  
Initial Period ◽  
Optimal Decision ◽  
Medical Supplies ◽  
Emergency Medical ◽  
Epidemic Period ◽  
Epidemic Area ◽  
The Impact

In the initial stage, the epidemic area is relatively concentrated, and some traffic modes may be subject to traffic control. In this period, the timely delivery of adequate emergency medical supplies to the epidemic points will play an important role in controlling the spread of the epidemic. However, the existing emergency medical supplies loading optimization model has not taken the initial period of the epidemic as the research time nor fully considered the traffic control situation in that period. Therefore, combined with the characteristics of the initial epidemic period of COVID-19, this study establishes an optimization model for emergency medical supplies stowage at the rescue point, considering the variation in demand for different kinds of medical supplies at the epidemic point in different cycles and the impact of traffic control on the mode of transportation. The model is an integer programming model. The objective function is the least total cost, including total transportation cost and total inventory cost. The constraints include the supply limit of each medical material that can be provided by the rescue point, the transportation capacity limit of the transportation mode, the demand constraints, inventory constraints, nonnegative constraints, and integer variable constraints of various medical supplies in each cycle of the epidemic location. Finally, combining the development of the epidemic situation in Wuhan January 18–23, 2020, a case study was carried out, and the optimal combination of different transportation modes and different stowage schemes in different periods of the rescue point was obtained, which verified the feasibility and practicality of the model. The model constructed in this study can provide a theoretical reference to the optimal decision-making plan of emergency medical supplies of the implementation of traffic control during the initial period of emergency public health events.

Prediction of Mine Inrush Water Based on BP Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Mine Water ◽  
Influential Factors ◽  
Water Inflow ◽  
Impact Factors ◽  
Artificial Neural ◽  
Inrush Water ◽  
The Impact

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

scholarly journals Multiobjective Optimization Design of Toll Plaza

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kai Wang ◽  
Peng Wang ◽  
Xin Chen ◽  
Lu-Ting Zhao
Keyword(s):  
Optimization Design ◽  
Programming Model ◽  
Construction Cost ◽  
Impact Factors ◽  
Accident Rate ◽  
Decision Variables ◽  
Toll Plaza ◽  
Function Relationship ◽  
The Impact

This paper mainly studies the optimization design of toll plaza, including the determination of the number of tollbooths and the design of the shape and size of the toll plaza. The optimization objectives are the construction cost of toll plaza, the throughput, and the accident rate. Through the analysis on the structure of toll plaza and the traffic flow, we determine the impact factors for optimization targets and select the number of tollbooths, the length of entrance and exit queue area, and the ingress and egress angles as decision variables and then build the function relationship between construction cost, accident rate, throughput, and decision variables. Then based on those functions to build the mathematical programming model, so as to get the optimal design plan.

Collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles

2018 ◽  
Vol 32 (22) ◽  
pp. 1850253
Author(s):  
Zhi-Yuan Sun ◽  
Yue Li ◽  
Wen-Cong Qu ◽  
Yan-Yan Chen
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Optimization Model ◽  
Programming Model ◽  
Urban Traffic ◽  
Middle Level ◽  
Internet Of Vehicles ◽  
Model Based ◽  
Level Model ◽  
Upper Level

In order to improve the comprehensive effect of Urban Traffic Control System (UTCS) and Urban Traffic Flow Guidance System (UTFGS), this paper puts forward a collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles (IOV). With consideration of dynamic constraints of UTCS and UTFGS, UTCS is taken as the fast variable, and UTFGS is taken as the slow variable in the collaboration optimization modeling. The conception of Variable Cycle Management (VCM) is presented to solve the mathematical modeling problem under the background of the two variables. A unified framework for VCM is proposed based on IOV. The delay and travel time are calculated based on lane-group-based cell transmission model (LGCTM). The collaboration optimization problem is abstracted into a tri-level programming model. The upper level model is a cycle length optimization model based on multi-objective programming. The middle level model is a dynamic signal control decision model based on fairness analysis. The lower level model is a user equilibrium model based on average travel time. A Heuristic Iterative Optimization Algorithm (HIOA) is set up to solve the tri-level programming model. The upper level model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA II), the middle level model and the lower level model are solved by Method of Successive Averages (MSA). A case study shows the efficiency and applicability of the proposed model and algorithm.

scholarly journals RESEARCH ON GIS MASSIVE TRAFFIC DATA ANALYSIS PLATFORM BASED ON HADOOP

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 613-621
Author(s):  
J. Y. Liu ◽  
C. Y. Yang ◽  
P. Wang ◽  
Y. Z. Ya
Keyword(s):  
Data Analysis ◽  
Programming Model ◽  
Urban Traffic ◽  
Traffic Data ◽  
File Storage ◽  
Data Volume ◽  
Scientific Technical ◽  
Visualization Technology ◽  
Analysis Platform ◽  
The Impact

Abstract. In view of the limitations of storage and calculation of mass traffic data in traditional GIS platform, this paper uses efficient and scientific technical means to analyze the data, and proposes a Hadoop-based GIS mass traffic data analysis platform. The platform uses MapReduce as a distributed computing programming model to analyze massive data for urban traffic decision-making, and uses HDFS distributed file storage framework to store and manage massive traffic data at TB level or even PB level. Finally, the results are displayed by using geographic information system spatial visualization technology, and the impact of the data volume and the number of nodes in the cluster on the calculation time-consuming is analyzed and compared. The experimental results show that the use of distributed multi-node cluster can effectively improve the storage and computing efficiency of massive traffic data, and greatly accelerate the total task scheduling time.

scholarly journals The Experimental Study of Performance Impairment of Big Data Processing in Dynamic and Opportunistic Environments

2020 ◽  
pp. 776-789
Author(s):  
Wei Li ◽  
◽  
William W. Guo
Keyword(s):  
Big Data ◽  
Impact Strength ◽  
Data Processing ◽  
Impact Factors ◽  
Distributed Environments ◽  
Distributed Environment ◽  
Big Data Processing ◽  
Performance Impairment ◽  
Result Analysis ◽  
The Impact

In contrast to HPC clusters, when big data is processing in a distributed, particularly dynamic and opportunistic environment, the overall performance must be impaired and even bottlenecked by the dynamics of overlay and the opportunism of computing nodes. The dynamics and opportunism are caused by churn and unreliability of a generic distributed environment, and they cannot be ignored or avoided. Understanding impact factors, their impact strength and the relevance between these impacts is the foundation of potential optimization. This paper derives the research background, methodology and results by reasoning the necessity of distributed environments for big data processing, scrutinizing the dynamics and opportunism of distributed environments, classifying impact factors, proposing evaluation metrics and carrying out a series of intensive experiments. The result analysis of this paper provides important insights to the impact strength of the factors and the relevance of impact across the factors. The production of the results aims at paving a way to future optimization or avoidance of potential bottlenecks for big data processing in distributed environments.

scholarly journals Monitoring of the impact factors of employee compensation incentive of big data enterprises based on structural equations

2020 ◽  
Vol 214 ◽  
pp. 01008
Author(s):  
Shen Qing Qing ◽  
Feng Jiang Hua
Keyword(s):  
Big Data ◽  
Interpersonal Relationships ◽  
Influence Factors ◽  
Empirical Method ◽  
Structural Equations ◽  
Impact Factors ◽  
Good Communication ◽  
Welfare Benefits ◽  
And Performance ◽  
The Impact

Through the perspective of employees of big data enterprises in Jiangsu, Zhejiang and Shanghai, data were obtained in the form of questionnaire research, the significance of the influence factors of compensation incentive is evaluated, and the use of the empirical method of structural equations is obtained, and all indicators play a positive incentive role. Among them, the four indicators of salary performance, prospect promotion, equity incentives and welfare benefits are highly motivating. At the same time, four countermeasures are put forward:1. While implementing long-term and short-term salary incentives, pay attention to the principle of ability and performance first; 2. Provide basic benefits and improve the retirement mechanism for employees; 3. Strengthen humanistic care and create simple interpersonal relationships and good communication atmosphere; 4. Improve the post promotion mechanism, clear employee career channel.

scholarly journals A TRIPLE SCREENING METHOD FOR THE IMPACT FACTORS OF HIGH STOCK TURNOVER

2021 ◽  
Author(s):  
YU-YANG PEI ◽  
XIAO-WEN XUE
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Screening Method ◽  
Influential Factors ◽  
Impact Factors ◽  
Feature Engineering ◽  
Analysis Method ◽  
Insufficient Information ◽  
Small Clusters ◽  
The Impact

Taking 2680 stocks selected from the market as the research object, this paper proposes a triple screening method for the influential factors of high transfer of stocks. Firstly, we preprocess the data and eliminate the variables with insufficient information. Secondly, cluster analysis was used to eliminate small clusters. Finally, according to the method of feature engineering, we further screen the features. In addition, this paper uses factor analysis method for reference to calculate the profitability of listed companies. The effect is remarkable.

Sign in / Sign up

Export Citation Format

Share Document