{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:23:44Z","timestamp":1793467424098,"version":"4.5.0"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNPQ"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%.<\/jats:p>","DOI":"10.3390\/ijgi12060227","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T02:12:45Z","timestamp":1685585565000},"page":"227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A Machine Learning Approach for Classifying Road Accident Hotspots"],"prefix":"10.3390","volume":"12","author":[{"given":"Brunna de Sousa Pereira","family":"Amorim","sequence":"first","affiliation":[{"name":"Computer Science Department, Federal University of Campina Grande, Rua Aprigio Veloso, 882-Universit\u00e1rio, Campina Grande 58429-900, Paraiba, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2199-8191","authenticated-orcid":false,"given":"Anderson Almeida","family":"Firmino","sequence":"additional","affiliation":[{"name":"Computer Science Department, Federal University of Campina Grande, Rua Aprigio Veloso, 882-Universit\u00e1rio, Campina Grande 58429-900, Paraiba, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2200-1405","authenticated-orcid":false,"given":"Cl\u00e1udio de Souza","family":"Baptista","sequence":"additional","affiliation":[{"name":"Computer Science Department, Federal University of Campina Grande, Rua Aprigio Veloso, 882-Universit\u00e1rio, Campina Grande 58429-900, Paraiba, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3731-6431","authenticated-orcid":false,"given":"Geraldo Braz","family":"J\u00fanior","sequence":"additional","affiliation":[{"name":"Applied Computing Center, Federal University of Maranh\u00e3o, Av. dos Portugueses, 1966-Vila Bacanga, S\u00e3o Lu\u00eds 65080-805, Maranh\u00e3o, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4921-0626","authenticated-orcid":false,"given":"Anselmo Cardoso de","family":"Paiva","sequence":"additional","affiliation":[{"name":"Applied Computing Center, Federal University of Maranh\u00e3o, Av. dos Portugueses, 1966-Vila Bacanga, S\u00e3o Lu\u00eds 65080-805, Maranh\u00e3o, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco Edeverton de Almeida","family":"J\u00fanior","sequence":"additional","affiliation":[{"name":"Computer Science Department, Federal University of Campina Grande, Rua Aprigio Veloso, 882-Universit\u00e1rio, Campina Grande 58429-900, Paraiba, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.trpro.2020.03.057","article-title":"Human factors contributing to the road traffic accident occurrence","volume":"45","author":"Mikulec","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jsr.2021.09.002","article-title":"Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field","volume":"79","author":"Haghani","year":"2021","journal-title":"J. Saf. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101436","DOI":"10.1016\/j.ecoinf.2021.101436","article-title":"Application of geographical information system (GIS) in reducing accident blackspots and in planning of a safer urban road network: A review","volume":"66","author":"Singh","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Naboureh, A., Feizizadeh, B., Naboureh, A., Bian, J., Blaschke, T., Ghorbanzadeh, O., and Moharrami, M. (2019). Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090371"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hazaymeh, K., Almagbile, A., and Alomari, A.H. (2022). Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11040260"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, C., Li, S., and Shan, J. (2021). Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050286"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mesquitela, J., Elvas, L.B., Ferreira, J.C., and Nunes, L. (2022). Data Analytics Process over Road Accidents Data; A Case Study of Lisbon City. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11020143"},{"key":"ref_8","first-page":"57","article-title":"Augmenting Classifiers Performance through Clustering: A Comparative Study on Road Accident Data","volume":"8","author":"Kumar","year":"2018","journal-title":"Int. J. Inf. Retr. Res. (IJIRR)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113855","DOI":"10.1016\/j.eswa.2020.113855","article-title":"Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning","volume":"167","author":"Sangare","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.aap.2016.02.011","article-title":"Investigating driver injury severity patterns in rollover crashes using support vector machine models","volume":"90","author":"Chen","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6047","DOI":"10.1016\/j.eswa.2013.05.027","article-title":"Analysis of traffic accident severity using decision rules via decision trees","volume":"40","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Z., Guo, X., and Sun, J. (2018, January 29\u201331). Analysis and Research on the Temporal and Spatial Correlation of Traffic Accidents and Illegal Activities. Proceedings of the International Conference on Cloud Computing and Security, Singapore.","DOI":"10.1007\/978-3-030-00021-9_38"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.aap.2014.09.006","article-title":"A stable and optimized neural network model for crash injury severity prediction","volume":"73","author":"Zeng","year":"2014","journal-title":"Accid. Anal. Prev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s40537-015-0035-y","article-title":"A data mining framework to analyze road accident data","volume":"2","author":"Kumar","year":"2015","journal-title":"J. Big Data"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gao, Z., Pan, R., Yu, R., and Wang, X. (2018, January 15\u201317). Research on Automated Modeling Algorithm Using Association Rules for Traffic Accidents. Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China.","DOI":"10.1109\/BigComp.2018.00027"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Katsoukis, A., Iliadis, L., Konguetsof, A., and Papadopoulos, B. (2018, January 3\u20135). Classification Of Road Accidents Using Fuzzy Techniques. Proceedings of the 2018 Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, Greece.","DOI":"10.1109\/INISTA.2018.8466291"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ryder, B., and Wortmann, F. (2017, January 11\u201315). Autonomously detecting and classifying traffic accident hotspots. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA.","DOI":"10.1145\/3123024.3123199"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.cstp.2021.12.009","article-title":"Traffic accident prediction model for rural highways in Pernambuco","volume":"10","author":"Macedo","year":"2022","journal-title":"Case Stud. Transp. Policy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"B\u00fclb\u00fcl, H.\u0130., Kaya, T., and Tulgar, Y. (2016, January 18\u201320). Analysis for Status of the Road Accident Occurance and Determination of the Risk of Accident by Machine Learning in Istanbul. Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA.","DOI":"10.1109\/ICMLA.2016.0075"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11809","DOI":"10.1109\/ACCESS.2019.2892135","article-title":"Exploring risk factors with crashes by collision type at freeway diverge areas: Accounting for unobserved heterogeneity","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aap.2014.11.005","article-title":"Application of classification algorithms for analysis of road safety risk factor dependencies","volume":"75","author":"Kwon","year":"2015","journal-title":"Accid. Anal. Prev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tambouratzis, T., Souliou, D., Chalikias, M., and Gregoriades, A. (2010, January 18\u201323). Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596610"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Satu, M.S., Ahamed, S., Hossain, F., Akter, T., and Farid, D.M. (2017, January 21\u201323). Mining traffic accident data of N5 national highway in Bangladesh employing decision trees. Proceedings of the 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh.","DOI":"10.1109\/R10-HTC.2017.8289059"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.aap.2017.08.008","article-title":"Comparison of four statistical and machine learning methods for crash severity prediction","volume":"108","author":"Iranitalab","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1007\/s41019-017-0044-2","article-title":"Using GUHA data mining method in analyzing road traffic accidents occurred in the years 2004\u20132008 in Finland","volume":"2","author":"Turunen","year":"2017","journal-title":"Data Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, J., and Ohsawa, Y. (2016, January 11\u201314). Evaluating model of traffic accident rate on urban data. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland.","DOI":"10.15439\/2016F195"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Richard, R., and Ray, S. (2017, January 11\u201314). A tale of two cities: Analyzing road accidents with big spatial data. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258334"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ren, H., Song, Y., Wang, J., Hu, Y., and Lei, J. (2018, January 4\u20137). A deep learning approach to the citywide traffic accident risk prediction. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569437"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). Why should i trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote. Sens."},{"key":"ref_31","first-page":"1263","article-title":"Learning from imbalanced data","volume":"1","author":"He","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_32","first-page":"89","article-title":"Traffic accident analysis using machine learning paradigms","volume":"29","author":"Chong","year":"2005","journal-title":"Informatica"},{"key":"ref_33","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Olson, R.S., Bartley, N., Urbanowicz, R.J., and Moore, J.H. (2016, January 20\u201324). Evaluation of a Tree-Based Pipeline Optimization Tool for Automating Data Science. Proceedings of the Genetic and Evolutionary Computation Conference 2016 (GECCO `16), Denver, CO, USA.","DOI":"10.1145\/2908812.2908918"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1093\/bioinformatics\/btz470","article-title":"Scaling tree-based automated machine learning to biomedical big data with a feature set selector","volume":"36","author":"Le","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_37","first-page":"39","article-title":"Performance evaluation of lazy, decision tree classifier and multilayer perceptron on traffic accident analysis","volume":"41","author":"Tiwari","year":"2017","journal-title":"Informatica"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tiwari, P., Kumar, S., and Kalitin, D. (2017, January 24\u201325). Road-user specific analysis of traffic accident using data mining techniques. Proceedings of the International Conference on Computational Intelligence, Communications, and Business Analytics, Kolkata, India.","DOI":"10.1007\/978-981-10-6430-2_31"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"140197","DOI":"10.1109\/ACCESS.2019.2942647","article-title":"A Novel Identification Model for Road Traffic Accident Black Spots: A Case Study in Ningbo, China","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, L., Du, B., Hu, X., Sun, L., Lv, W., and Huang, R. (2019, January 19\u201323). Traffic Accident Prediction Based on Deep Spatio-Temporal Analysis. Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00195"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/227\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:46:03Z","timestamp":1760125563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["ijgi12060227"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12060227","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}