{"title":"基于信号交叉口交通特征分布的VISSIM参数标定","authors":"N. Li, Yujie Sun","doi":"10.1109/DDCLS.2018.8515913","DOIUrl":null,"url":null,"abstract":"In order to increase the accuracy of traffic simulation and better reproduce the real traffic condition at signalized intersections, this paper proposed a parameter calibration method based on the traffic distribution rules at signalized intersections. First, after qualitatively analyzing the traffic condition at signalized intersections based on dynamic traffic features, this paper selected the key parameters that need to be calibrated. Then, regarding the selected key parameters, this paper first designed and implemented the collecting method. Then filtered and analyzed the data, and acquired the distribution pattern of each key parameter at signalized intersection. Finally, in order to validate the calibration process based on vehicle types through simulation, this paper chose travel time and number of stops as validation parameters. The results showed that there had been a great increase in the accuracy after calibration. The maximum inaccuracy among all evaluation parameters was 14.6%, which indicated that the calibration process based on traffic characteristics distribution at signalized intersections was effective.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"97 1","pages":"150-153"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"VISSIM Parameter Calibration Based on Traffic Characteristics Distribution at Signalized Intersections\",\"authors\":\"N. Li, Yujie Sun\",\"doi\":\"10.1109/DDCLS.2018.8515913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to increase the accuracy of traffic simulation and better reproduce the real traffic condition at signalized intersections, this paper proposed a parameter calibration method based on the traffic distribution rules at signalized intersections. First, after qualitatively analyzing the traffic condition at signalized intersections based on dynamic traffic features, this paper selected the key parameters that need to be calibrated. Then, regarding the selected key parameters, this paper first designed and implemented the collecting method. Then filtered and analyzed the data, and acquired the distribution pattern of each key parameter at signalized intersection. Finally, in order to validate the calibration process based on vehicle types through simulation, this paper chose travel time and number of stops as validation parameters. The results showed that there had been a great increase in the accuracy after calibration. The maximum inaccuracy among all evaluation parameters was 14.6%, which indicated that the calibration process based on traffic characteristics distribution at signalized intersections was effective.\",\"PeriodicalId\":6565,\"journal\":{\"name\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":\"97 1\",\"pages\":\"150-153\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2018.8515913\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2018.8515913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VISSIM Parameter Calibration Based on Traffic Characteristics Distribution at Signalized Intersections
In order to increase the accuracy of traffic simulation and better reproduce the real traffic condition at signalized intersections, this paper proposed a parameter calibration method based on the traffic distribution rules at signalized intersections. First, after qualitatively analyzing the traffic condition at signalized intersections based on dynamic traffic features, this paper selected the key parameters that need to be calibrated. Then, regarding the selected key parameters, this paper first designed and implemented the collecting method. Then filtered and analyzed the data, and acquired the distribution pattern of each key parameter at signalized intersection. Finally, in order to validate the calibration process based on vehicle types through simulation, this paper chose travel time and number of stops as validation parameters. The results showed that there had been a great increase in the accuracy after calibration. The maximum inaccuracy among all evaluation parameters was 14.6%, which indicated that the calibration process based on traffic characteristics distribution at signalized intersections was effective.