{"title":"何时何地主动预测南非交通事故:我们的机器学习竞赛获胜方法","authors":"S. Afolabi, Warrie Usenobong Warrie, O. Banjo, Opeoluwa Iwashokun, Abimbola Olawale, Naledi Ngqambela, Fata Soliu, Olawumi Olasunkanmi, Folorunso Sakinat, Sibusiso Sibusiso Matshika","doi":"10.1504/ijsss.2021.116374","DOIUrl":null,"url":null,"abstract":"South Africa (SA) records high mortality originating from traffic accident annually making the country to be ranked highly among nations with the highest traffic mortality globally. There is seemingly no study that has attempted to forecast when and where next accident will occur in SA. This study aims to use machine learning method to predict traffic accident in SA for every hour ranging between 1 January and 31 March 2019 at a segment ID. We obtained details of accidents that occurred in Cape Town, SA between 2016 and 2019 SANRAL, Uber Movement and Cape Town FMS via Zindi competition platform. This research adopted Catboost and LightGBM models to predict the traffic incident occurrence. Our model shows a F1 score of 0.11. The results of this research will aid prediction of accident occurrence at a particular road segment hourly.","PeriodicalId":89681,"journal":{"name":"International journal of society systems science","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"When and where Proactively predicting traffic accident in South Africa: our machine learning competition winning approach\",\"authors\":\"S. Afolabi, Warrie Usenobong Warrie, O. Banjo, Opeoluwa Iwashokun, Abimbola Olawale, Naledi Ngqambela, Fata Soliu, Olawumi Olasunkanmi, Folorunso Sakinat, Sibusiso Sibusiso Matshika\",\"doi\":\"10.1504/ijsss.2021.116374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"South Africa (SA) records high mortality originating from traffic accident annually making the country to be ranked highly among nations with the highest traffic mortality globally. There is seemingly no study that has attempted to forecast when and where next accident will occur in SA. This study aims to use machine learning method to predict traffic accident in SA for every hour ranging between 1 January and 31 March 2019 at a segment ID. We obtained details of accidents that occurred in Cape Town, SA between 2016 and 2019 SANRAL, Uber Movement and Cape Town FMS via Zindi competition platform. This research adopted Catboost and LightGBM models to predict the traffic incident occurrence. Our model shows a F1 score of 0.11. The results of this research will aid prediction of accident occurrence at a particular road segment hourly.\",\"PeriodicalId\":89681,\"journal\":{\"name\":\"International journal of society systems science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of society systems science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijsss.2021.116374\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of society systems science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijsss.2021.116374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
When and where Proactively predicting traffic accident in South Africa: our machine learning competition winning approach
South Africa (SA) records high mortality originating from traffic accident annually making the country to be ranked highly among nations with the highest traffic mortality globally. There is seemingly no study that has attempted to forecast when and where next accident will occur in SA. This study aims to use machine learning method to predict traffic accident in SA for every hour ranging between 1 January and 31 March 2019 at a segment ID. We obtained details of accidents that occurred in Cape Town, SA between 2016 and 2019 SANRAL, Uber Movement and Cape Town FMS via Zindi competition platform. This research adopted Catboost and LightGBM models to predict the traffic incident occurrence. Our model shows a F1 score of 0.11. The results of this research will aid prediction of accident occurrence at a particular road segment hourly.