The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems.Monitoring and accurately forecasting urban traffic operation is...The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems.Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion.Compared with traditional short-time traffic prediction,this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index(TPI).The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation,including day of week,time period,public holiday,car usage restriction policy,special events,etc.Based on long-term historical TPI data,this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm(XGBoost).The model validation results show that the model prediction accuracy can reach higher than 90%.Compared with other prediction models,including Bayesian Ridge,Linear Regression,ElatsicNet,SVR,the XGBoost model has a better performance,and proves its superiority in large high-dimensional data sets.The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.展开更多
This paper presents a new index system for the performance evaluation and network planning of multimedia communication systems using measurement on actual systems to support several different traffic types. In this in...This paper presents a new index system for the performance evaluation and network planning of multimedia communication systems using measurement on actual systems to support several different traffic types. In this index system, we develop an expert system to evaluate the performance of such multimedia communication networks including channel utilization and call blocking probability and packet delay, and apply the network planning methods to optimize the networks and forecast the demand of the growing multimedia communications systems. Two important planning problems for the multimedia communication systems are presented: optimization problem for construction of the world system and forecast problem for increasing traffic demands. We first discuss analysis methods, performance measures for the multimedia communication systems. Then, we describe network planning methods for the multimedia communication systems and give some efficiency network planning methods. Finally, we present some results studied in traffic forecast for the campus network and show the effectiveness of these methods.展开更多
针对交通运行指数(Traffic Performance Index,TPI)合理值取值问题,将宏观基本图(Mac⁃roscopic Fundamental Diagram,MFD)模型与交通运行指数模型相结合,提出理论效能最优交通指数的求解方法.首先,利用各等级道路交通流基本图模型,建立...针对交通运行指数(Traffic Performance Index,TPI)合理值取值问题,将宏观基本图(Mac⁃roscopic Fundamental Diagram,MFD)模型与交通运行指数模型相结合,提出理论效能最优交通指数的求解方法.首先,利用各等级道路交通流基本图模型,建立全路网MFD,寻找路网理论效能最优状态点;其次,以速度为连接,将该点映射至基于严重拥堵里程比的交通运行指数模型中,得到理论效能最优交通指数;最后,以北京市全路网及行政区为例进行实证研究.结果表明:全路网的理论效能最优交通指数为6.42,东城区、西城区、海淀区和朝阳区理论效能最优交通指数依次为6.86,6.80,6.76,4.58.朝阳区理论效能最优交通指数最低,其路网性能优于其他三区.该方法为交通管理和交通出行提供直观参考,并为制定交通管理政策提供理论支撑.展开更多
基金funded by the National Natural Science Foundation of China(NFSC)(No.52072011)。
文摘The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems.Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion.Compared with traditional short-time traffic prediction,this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index(TPI).The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation,including day of week,time period,public holiday,car usage restriction policy,special events,etc.Based on long-term historical TPI data,this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm(XGBoost).The model validation results show that the model prediction accuracy can reach higher than 90%.Compared with other prediction models,including Bayesian Ridge,Linear Regression,ElatsicNet,SVR,the XGBoost model has a better performance,and proves its superiority in large high-dimensional data sets.The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.
基金This work was supported partly by National Natural Science Foundation of China under Grant No.79990583 and 70221001
文摘This paper presents a new index system for the performance evaluation and network planning of multimedia communication systems using measurement on actual systems to support several different traffic types. In this index system, we develop an expert system to evaluate the performance of such multimedia communication networks including channel utilization and call blocking probability and packet delay, and apply the network planning methods to optimize the networks and forecast the demand of the growing multimedia communications systems. Two important planning problems for the multimedia communication systems are presented: optimization problem for construction of the world system and forecast problem for increasing traffic demands. We first discuss analysis methods, performance measures for the multimedia communication systems. Then, we describe network planning methods for the multimedia communication systems and give some efficiency network planning methods. Finally, we present some results studied in traffic forecast for the campus network and show the effectiveness of these methods.
文摘针对交通运行指数(Traffic Performance Index,TPI)合理值取值问题,将宏观基本图(Mac⁃roscopic Fundamental Diagram,MFD)模型与交通运行指数模型相结合,提出理论效能最优交通指数的求解方法.首先,利用各等级道路交通流基本图模型,建立全路网MFD,寻找路网理论效能最优状态点;其次,以速度为连接,将该点映射至基于严重拥堵里程比的交通运行指数模型中,得到理论效能最优交通指数;最后,以北京市全路网及行政区为例进行实证研究.结果表明:全路网的理论效能最优交通指数为6.42,东城区、西城区、海淀区和朝阳区理论效能最优交通指数依次为6.86,6.80,6.76,4.58.朝阳区理论效能最优交通指数最低,其路网性能优于其他三区.该方法为交通管理和交通出行提供直观参考,并为制定交通管理政策提供理论支撑.
