{"title":"纽约市交通事故的时间序列分析","authors":"Khaled Shaaban, M. Ibrahim","doi":"10.1109/ietc54973.2022.9796728","DOIUrl":null,"url":null,"abstract":"In New York City, traffic crashes are one of the main causes of fatalities in the city. This study presents a comprehensive time series analysis of road crashes in the city from 2013 to 2019. The crash data were collected, organized, and analyzed at different time levels: yearly, seasonally, monthly and hourly bases. Forecasting of the total number of crashes in the years 2020 to 2025 was conducted using the Box-Jenkins method based on the autoregressive integrated moving average (ARIMA) model. The model was statistically validated using a modified Box-Pierce (Ljung-Box) Chi-Square test. The proposed model was also used for backward prediction of the year 2019 to compare with actual observations. The predicted results showed a good agreement with the actual observed results. The results also showed a strong potential of having a reduction in the total number of crashes in the future.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Time-Series Analysis of Traffic Crashes in New York City\",\"authors\":\"Khaled Shaaban, M. Ibrahim\",\"doi\":\"10.1109/ietc54973.2022.9796728\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In New York City, traffic crashes are one of the main causes of fatalities in the city. This study presents a comprehensive time series analysis of road crashes in the city from 2013 to 2019. The crash data were collected, organized, and analyzed at different time levels: yearly, seasonally, monthly and hourly bases. Forecasting of the total number of crashes in the years 2020 to 2025 was conducted using the Box-Jenkins method based on the autoregressive integrated moving average (ARIMA) model. The model was statistically validated using a modified Box-Pierce (Ljung-Box) Chi-Square test. The proposed model was also used for backward prediction of the year 2019 to compare with actual observations. The predicted results showed a good agreement with the actual observed results. The results also showed a strong potential of having a reduction in the total number of crashes in the future.\",\"PeriodicalId\":251518,\"journal\":{\"name\":\"2022 Intermountain Engineering, Technology and Computing (IETC)\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Intermountain Engineering, Technology and Computing (IETC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ietc54973.2022.9796728\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Intermountain Engineering, Technology and Computing (IETC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ietc54973.2022.9796728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Time-Series Analysis of Traffic Crashes in New York City
In New York City, traffic crashes are one of the main causes of fatalities in the city. This study presents a comprehensive time series analysis of road crashes in the city from 2013 to 2019. The crash data were collected, organized, and analyzed at different time levels: yearly, seasonally, monthly and hourly bases. Forecasting of the total number of crashes in the years 2020 to 2025 was conducted using the Box-Jenkins method based on the autoregressive integrated moving average (ARIMA) model. The model was statistically validated using a modified Box-Pierce (Ljung-Box) Chi-Square test. The proposed model was also used for backward prediction of the year 2019 to compare with actual observations. The predicted results showed a good agreement with the actual observed results. The results also showed a strong potential of having a reduction in the total number of crashes in the future.
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