{"title":"北京地区电动与非电动自行车碰撞伤害严重程度影响因素调查","authors":"Yuxuan Xing, Zhiyuan Sun, Duo Wang","doi":"10.1109/ICITE50838.2020.9231401","DOIUrl":null,"url":null,"abstract":"The objective of this study was to identify influence factors on injury severity of electric and non-electric bicycle crashes and discuss the differences between them in Beijing, China. Generalized linear model (GLM) and classification and regression tree (CART) were proposed to investigate significant influence factors and the importance order of influence factors, respectively. Based on GLM, seven factors were significant in electric bicycle crashes whereas five factors were significant in non-electric bicycle crashes. CART implied the most important factors was type of motor vehicle both in electric and non-electric bicycle crashes. However, other important factors showed different characteristic in the two type of crashes. This paper gives detailed information for electric and non-electric bicycle crashes, which provides reference for government to implement measures precisely.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Investigating Influence Factors on Injury Severity of Electric and Non-electric Bicycle Crashes in Beijing\",\"authors\":\"Yuxuan Xing, Zhiyuan Sun, Duo Wang\",\"doi\":\"10.1109/ICITE50838.2020.9231401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this study was to identify influence factors on injury severity of electric and non-electric bicycle crashes and discuss the differences between them in Beijing, China. Generalized linear model (GLM) and classification and regression tree (CART) were proposed to investigate significant influence factors and the importance order of influence factors, respectively. Based on GLM, seven factors were significant in electric bicycle crashes whereas five factors were significant in non-electric bicycle crashes. CART implied the most important factors was type of motor vehicle both in electric and non-electric bicycle crashes. However, other important factors showed different characteristic in the two type of crashes. This paper gives detailed information for electric and non-electric bicycle crashes, which provides reference for government to implement measures precisely.\",\"PeriodicalId\":112371,\"journal\":{\"name\":\"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITE50838.2020.9231401\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITE50838.2020.9231401","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating Influence Factors on Injury Severity of Electric and Non-electric Bicycle Crashes in Beijing
The objective of this study was to identify influence factors on injury severity of electric and non-electric bicycle crashes and discuss the differences between them in Beijing, China. Generalized linear model (GLM) and classification and regression tree (CART) were proposed to investigate significant influence factors and the importance order of influence factors, respectively. Based on GLM, seven factors were significant in electric bicycle crashes whereas five factors were significant in non-electric bicycle crashes. CART implied the most important factors was type of motor vehicle both in electric and non-electric bicycle crashes. However, other important factors showed different characteristic in the two type of crashes. This paper gives detailed information for electric and non-electric bicycle crashes, which provides reference for government to implement measures precisely.