Model for Predicting Roadside Concentrations of Traffic Pollutants  

作　　者：杨忠振 苗国强 王璐 

机构地区：[1]College of Transportation and Logistics, Dalian Maritime University, Dalian 116026, China

出　　处：《Tsinghua Science and Technology》2007年第2期178-183,共6页清华大学学报（自然科学版（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 50422282)

摘　　要：An analytical model is presented to estimate traffic pollutant concentrations based on an artificial neural network （ANN） approach. The model can analyze the highly nonlinear relationship between the traffic flow attributes, meteorological conditions, road spatial characteristics, and the traffic pollutant concentrations This study analyzes the multiple factors that affect the pollutant concentration and establishes the model structure using the ANN technique. Collected data for the pollutant concentrations as functions of vadant factors was used to train the ANN model. A method was developed to automatically measure the traffic flow attributes, such as traffic flow, vehicle speed, and flow composition from video data. The results indicate that the model can reliably forecast CO2 concentrations along the roads.An analytical model is presented to estimate traffic pollutant concentrations based on an artificial neural network （ANN） approach. The model can analyze the highly nonlinear relationship between the traffic flow attributes, meteorological conditions, road spatial characteristics, and the traffic pollutant concentrations This study analyzes the multiple factors that affect the pollutant concentration and establishes the model structure using the ANN technique. Collected data for the pollutant concentrations as functions of vadant factors was used to train the ANN model. A method was developed to automatically measure the traffic flow attributes, such as traffic flow, vehicle speed, and flow composition from video data. The results indicate that the model can reliably forecast CO2 concentrations along the roads.

关 键 词：artificial neural network pollutant concentration traffic flow virtual loop 

分 类 号：U491.92[交通运输工程—交通运输规划与管理]