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1、基于精确传感网络的智能交通系统交通流模型摘要:首先介绍了基于精确传感网络的智能交通系统(its)相对于传统交通流传感器网络的优势；然后基于组合预测理论对这类网络的基本交通流模型进行了研究，在模型中引入了更加精确的交通流物理量，包括旅行时间、路段上游及下游的分类交通流量等变量，使所建立模型的可解释信息量更加丰富和易懂，该模型算法为动态算法。交通实测数据实验证明模型的拟合精度较高，拟合值与真值的平均绝对误差值控制在9s以内，平均相对误差值控制在5%以内，综合各个时段来看，预测的准确度都在90%以上。最后总结了基于精确传感器网络的智能交通系统在实际交
2、通应用中的重大价值。关键词:精确传感网络;智能交通系统；旅行时间;分类交通流量;交通流模型wangtao*,lizhishuschoolofcomputerscience,sichuanuniversity，chengdusichuan610064,china;2.schoolofinformationscienceandtechnology,chengduuniversity,chengdusichuan610106，chinaabstract:inthepresentpaper,theadvantageofprecisesensornet
3、workcomparedtothetraditionalsensornetworkisintroducedfirstly.thenthetrafficflowmodelofprecisesensornetworkisbuilt,toimprovetheinterpretabilityofthemodel,thepreciselicenseplateidentificationdataareused,variablessuchasthespacetraveltimeandtheclassifiedtrafficflowareintroduced.
4、theestablishedmodelisdynamicinessence.theexperimentalresultsshowthatthefittingaccuracyishigherandthemeanabsoluteerrorbetweenfittedandstandardvalueislessthan9seconds,themeanrelativeerrorislessthan5%,themodelhasahighdegreeofaccuracyabove90%.finally,theapplicationsofprecisesens
5、ornetworkinrealtrafficenvironmentareconcluded.theadvantageofprecisesensornetworkcomparedtothetraditionalsensornetworkwasintroducedfirstly.thenthetrafficflowmodelofprecisesensornetworkwasbuilt.toimprovetheinterpretabilityofthemodel,thepreciselicenseplateidentificationdatawere
6、used,andthevariablessuchasthespacetraveltimeandtheclassifiedtrafficflowwereintroduced.theestablishedmodelwasdynamicinessence.theexperimentalresultsshowthatthefittingaccuracyishigherandthemeanabsoluteerrorbetweenfittedandstandardvalueislessthan9seconds,andthemeanrelativeerror
7、islessthan5%.themodelhasahighdegreeofaccuracyabove90%.finally,thegreatvalueofprecisesensornetworkinrealtrafficenvironmentwassummarized.keywords:precisesensornetwork;intellignettransportsystem(its);traveltime;classifiedtrafficflow;trafficflowmodel0引言随着传感器、嵌入式技术和短距离无线通信技术的进展，人
8、类能够实现物理世界更深入的感知。2005年国际电信联盟(internationaltelecommunicationunion,itu)提出了“the
