{"title":"移动计算流量仿真模型的图形化分析","authors":"W. Suh","doi":"10.1109/ICITCS.2013.6717839","DOIUrl":null,"url":null,"abstract":"As more roadside and in-vehicle sensors are deployed, variety of traffic data are becoming available in real time. These real time traffic data are shared among vehicles and between vehicles and traffic management centers through wireless communication. This course of events creates an opportunity for mobile computing and online traffic simulations. This paper presents the fundamental analytical background on mobile computing based ad hoc distributed simulation model. Rollback process in the ad hoc distributed simulation is graphically described. Flow rate diagram and cumulative number of vehicle diagram shows that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold. Two main criteria to measure the system's predictability are presented: 1) length of prediction horizon, that is, how far in advance of the current wall-clock time the system provides estimates and 2) how accurate the estimates are at specific prediction horizon, i.e. how accurate is the estimate (compared with the actual traffic conditions). These two criteria and the relation between logical process simulations and the real time field data driven simulation client data are graphically demonstrated as well.","PeriodicalId":420227,"journal":{"name":"2013 International Conference on IT Convergence and Security (ICITCS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graphical Analysis of Mobile Computing Traffic Simulation Model\",\"authors\":\"W. Suh\",\"doi\":\"10.1109/ICITCS.2013.6717839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As more roadside and in-vehicle sensors are deployed, variety of traffic data are becoming available in real time. These real time traffic data are shared among vehicles and between vehicles and traffic management centers through wireless communication. This course of events creates an opportunity for mobile computing and online traffic simulations. This paper presents the fundamental analytical background on mobile computing based ad hoc distributed simulation model. Rollback process in the ad hoc distributed simulation is graphically described. Flow rate diagram and cumulative number of vehicle diagram shows that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold. Two main criteria to measure the system's predictability are presented: 1) length of prediction horizon, that is, how far in advance of the current wall-clock time the system provides estimates and 2) how accurate the estimates are at specific prediction horizon, i.e. how accurate is the estimate (compared with the actual traffic conditions). These two criteria and the relation between logical process simulations and the real time field data driven simulation client data are graphically demonstrated as well.\",\"PeriodicalId\":420227,\"journal\":{\"name\":\"2013 International Conference on IT Convergence and Security (ICITCS)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on IT Convergence and Security (ICITCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITCS.2013.6717839\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on IT Convergence and Security (ICITCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITCS.2013.6717839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graphical Analysis of Mobile Computing Traffic Simulation Model
As more roadside and in-vehicle sensors are deployed, variety of traffic data are becoming available in real time. These real time traffic data are shared among vehicles and between vehicles and traffic management centers through wireless communication. This course of events creates an opportunity for mobile computing and online traffic simulations. This paper presents the fundamental analytical background on mobile computing based ad hoc distributed simulation model. Rollback process in the ad hoc distributed simulation is graphically described. Flow rate diagram and cumulative number of vehicle diagram shows that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold. Two main criteria to measure the system's predictability are presented: 1) length of prediction horizon, that is, how far in advance of the current wall-clock time the system provides estimates and 2) how accurate the estimates are at specific prediction horizon, i.e. how accurate is the estimate (compared with the actual traffic conditions). These two criteria and the relation between logical process simulations and the real time field data driven simulation client data are graphically demonstrated as well.