摘要
为了研究城市居民在公交场站污染物暴露水平下的影响程度,分析交通、公交场站、气象条件及周边环境对污染物浓度的影响,通过测量早晚高峰以及平峰期间的PM2.5浓度、CO浓度、风速、温度、车流量等10个因素,采用平均影响值(MIV)与BP(back propagation)神经网络相结合的方法,确定公交场站污染物暴露水平的主要影响因素,并在此基础上建立MIV-BP神经网络测评模型。评估结果表明:公交场站PM2.5的暴露浓度与公交停靠站类型、车流量、小车流量、大车流量、风速、湿度、降雨量有关;CO的暴露浓度与瞬时停靠车辆数、公交停靠站类型、湿度、风速、温度、车流量有关;改进后的MIV-BP神经网络模型较BP神经网络具有更高的预测精度和准确度,可有效对公交场站污染物暴露水平进行测评。
To study the influence degree of pollutants exposure levels on urban residents at bus terminals and analyze the influence of traffic,the bus terminal,meteorological condition,surroundings on pollutant concentration,10 factors including PM2.5 concentration,CO concentration,wind speed,temperature,and vehicle flow etc.during peak hours in the morning,evening,and peak periods were measured.The main influencing factors of pollutant exposure levels in bus stations were determined by using the combination of mean impact value(MIV)and BP(back propagation)neural network,and on this basis,a MIV-BP neural network assessment model was established.The final evaluation results indicate that the exposure concentration of PM2.5 at the bus station is related to the type of bus stop,traffic flow,car flow,traffic flow,wind speed,humidity,and rainfall.CO exposure concentration is related to the number of instantaneously parked vehicles,the type of bus stop,humidity,wind speed,temperature,and traffic flow.The improved MIV-BP neural network model has higher prediction accuracy and degree of accuracy than the original BP neural network,and can effectively evaluate the pollution exposure level of bus stop.
作者
徐磊
张学连
肖琼
彭金栓
XU Lei;ZHANG Xue-lian;XIAO Qiong;PENG Jin-shuan(College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)
出处
《科学技术与工程》
北大核心
2020年第30期12664-12671,共8页
Science Technology and Engineering
基金
重庆市自然科学基金(cstc2018jcyjAX0288)
重庆市教委科学技术研究项目(KJ1600540)
山地城市交通系统与安全重庆市重点实验室开放基金(KTSS201602)。
