{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T14:30:13Z","timestamp":1761921013764,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811668890"},{"type":"electronic","value":"9789811668906"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-6890-6_36","type":"book-chapter","created":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T14:02:28Z","timestamp":1646488948000},"page":"485-497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting and Analysing Pedestrian Injury Severity: A Machine Learning-Based Approach"],"prefix":"10.1007","author":[{"given":"Anjali","family":"Rao","sequence":"first","affiliation":[]},{"given":"Sobhan","family":"Sarkar","sequence":"additional","affiliation":[]},{"given":"Anima","family":"Pramanik","sequence":"additional","affiliation":[]},{"given":"J.","family":"Maiti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,6]]},"reference":[{"issue":"1","key":"36_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"36_CR2","unstructured":"Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Boca Raton"},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.aap.2016.02.011","volume":"90","author":"C Chen","year":"2016","unstructured":"Chen C, Zhang G, Qian Z, Tarefder RA, Tian Z (2016) Investigating driver injury severity patterns in rollover crashes using support vector machine models. Accid Anal & Prev 90:128\u2013139","journal-title":"Accid Anal & Prev"},{"issue":"sup1","key":"36_CR4","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1080\/19439962.2016.1162891","volume":"9","author":"FJ Cunto","year":"2017","unstructured":"Cunto FJ, Ferreira S (2017) An analysis of the injury severity of motorcycle crashes in brazil using mixed ordered response models. J Trans Safety & Secur 9(sup1):33\u201346","journal-title":"J Trans Safety & Secur"},{"issue":"4","key":"36_CR5","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/S0001-4575(03)00059-9","volume":"36","author":"PE G\u00e5rder","year":"2004","unstructured":"G\u00e5rder PE (2004) The impact of speed and other variables on pedestrian safety in maine. Accid Anal & Prev 36(4):533\u2013542","journal-title":"Accid Anal & Prev"},{"issue":"5","key":"36_CR6","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/S0001-4575(99)00100-1","volume":"32","author":"EA LaScala","year":"2000","unstructured":"LaScala EA, Gerber D, Gruenewald PJ (2000) Demographic and environmental correlates of pedestrian injury collisions: a spatial analysis. Accid Anal & Prev 32(5):651\u2013658","journal-title":"Accid Anal & Prev"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Liu J, Hainen A, Li X, Nie Q, Nambisan S (2019) Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach. Accid Anal & Prev 132:105272","DOI":"10.1016\/j.aap.2019.105272"},{"issue":"3","key":"36_CR8","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1136\/ip.2006.013458","volume":"13","author":"BP Loo","year":"2007","unstructured":"Loo BP, Tsui K (2007) Factors affecting the likelihood of reporting road crashes resulting in medical treatment to the police. Inj Prev 13(3):186\u2013189","journal-title":"Inj Prev"},{"issue":"1","key":"36_CR9","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.aap.2007.06.006","volume":"40","author":"JC Milton","year":"2008","unstructured":"Milton JC, Shankar VN, Mannering FL (2008) Highway accident severities and the mixed logit model: an exploratory empirical analysis. Accid Anal & Prev 40(1):260\u2013266","journal-title":"Accid Anal & Prev"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.autcon.2018.03.022","volume":"93","author":"CQ Poh","year":"2018","unstructured":"Poh CQ, Ubeynarayana CU, Goh YM (2018) Safety leading indicators for construction sites: a machine learning approach. Autom Constr 93:375\u2013386","journal-title":"Autom Constr"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Pramanik A, Harshvardhan, Djeddi C, Sarkar S, Maiti J (2020) Region proposal and object detection using hog-based cnn feature map. In: 2020 international conference on data analytics for business and industry: way towards a sustainable economy (ICDABI). IEEE, pp 1\u20135","DOI":"10.1109\/ICDABI51230.2020.9325708"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Pramanik A, Sarkar S, Maiti J (2019) Oil spill detection using image processing technique: an occupational safety perspective of a steel plant. In: Emerging technologies in data mining and information security, vol 814. Springer, Singapore, pp 247\u2013257","DOI":"10.1007\/978-981-13-1501-5_21"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Pramanik A, Sarkar S, Maiti J (2021) A real-time video surveillance system for traffic pre-events detection. Accid Anal & Prev 154:106019","DOI":"10.1016\/j.aap.2021.106019"},{"key":"36_CR14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ins.2021.01.039","volume":"566","author":"A Pramanik","year":"2021","unstructured":"Pramanik A, Sarkar S, Maiti J, Mitra P (2021) Rt-gsom: rough tolerance growing self-organizing map. Inf Sci 566:19\u201337","journal-title":"Inf Sci"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Sarkar S, Baidya S, Maiti J (2017) Application of rough set theory in accident analysis at work: a case study. In: ICRCICN 2017, pp 245\u2013250","DOI":"10.1109\/ICRCICN.2017.8234514"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Sarkar S, Chain M, Nayak S, Maiti J (2019) Decision support system for prediction of occupational accident: a case study from a steel plant. In: Emerging technologies in data mining and information security, vol 813. Springer, Singapore, pp 787\u2013796","DOI":"10.1007\/978-981-13-1498-8_69"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Sarkar S, Ejaz N, Maiti J (2018) Application of hybrid clustering technique for pattern extraction of accident at work: a case study of a steel industry. In: 2018 4th international conference on recent advances in information technology (RAIT), IIT Dhanbad. IEEE, pp 1\u20136","DOI":"10.1109\/RAIT.2018.8389052"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Sarkar S, Maiti J (2020) Machine learning in occupational accident analysis: a review using science mapping approach with citation network analysis. Saf Sci 131:104900","DOI":"10.1016\/j.ssci.2020.104900"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Sarkar S, Pateshwari V, Maiti J (2017) Predictive model for incident occurrences in steel plant in India. In: ICCCNT 2017, pp 1\u20135","DOI":"10.1109\/ICCCNT.2017.8204077"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Sarkar S, Pramanik A, Khatedi N, Maiti J (2020) An investigation of the effects of missing data handling using \u2018r\u2019-packages. In: Data engineering and communication technology. Springer, Singapore, pp 275\u2013284","DOI":"10.1007\/978-981-15-1097-7_24"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Sarkar S, Pramanik A, Maiti J, Reniers G (2020) Predicting and analyzing injury severity: a machine learning-based approach using class-imbalanced proactive and reactive data. Saf Sci 125:104616","DOI":"10.1016\/j.ssci.2020.104616"},{"key":"36_CR22","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ssci.2019.05.009","volume":"118","author":"S Sarkar","year":"2019","unstructured":"Sarkar S, Raj R, Vinay S, Maiti J, Pratihar DK (2019) An optimization-based decision tree approach for predicting slip-trip-fall accidents at work. Saf Sci 118:57\u201369","journal-title":"Saf Sci"},{"key":"36_CR23","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/978-3-030-71804-6_19","volume":"1322","author":"S Sarkar","year":"2021","unstructured":"Sarkar S, Vinay S, Djeddi C, Maiti J (2021) Text mining-based association rule mining for incident analysis: a case study of a steel plant in india. Pattern Recog Artif Intell 1322:257","journal-title":"Pattern Recog Artif Intell"},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.cor.2018.02.021","volume":"106","author":"S Sarkar","year":"2019","unstructured":"Sarkar S, Vinay S, Raj R, Maiti J, Mitra P (2019) Application of optimized machine learning techniques for prediction of occupational accidents. Comput & Oper Res 106:210\u2013224","journal-title":"Comput & Oper Res"},{"issue":"2","key":"36_CR25","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/S0001-4575(02)00149-5","volume":"36","author":"RJ Schneider","year":"2004","unstructured":"Schneider RJ, Ryznar RM, Khattak AJ (2004) An accident waiting to happen: a spatial approach to proactive pedestrian planning. Accid Anal & Prev 36(2):193\u2013211","journal-title":"Accid Anal & Prev"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Singh K, Raj N, Sahu S, Behera R, Sarkar S, Maiti J (2015) Modelling safety of gantry crane operations using petri nets. Int J Injury Control Safety Prom 1\u201312","DOI":"10.1080\/17457300.2015.1056809"},{"issue":"6","key":"36_CR27","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11348","volume":"10","author":"F Tang","year":"2017","unstructured":"Tang F, Ishwaran H (2017) Random forest missing data algorithms. Stati Anal Data Min: ASA Data Sci J 10(6):363\u2013377","journal-title":"Stati Anal Data Min: ASA Data Sci J"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Vapnik V (1995) The nature of statistical learning theory, vol 8. Search PubMed, p 188","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Verma A, Chatterjee S, Sarkar S, Maiti J (2018) Data-driven mapping between proactive and reactive measures of occupational safety performance. In: Industrial safety management- 21st century perspective of Asia. Springer, Singapore, pp 53\u201363","DOI":"10.1007\/978-981-10-6328-2_5"},{"issue":"6","key":"36_CR30","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1016\/j.aap.2007.02.009","volume":"39","author":"S Wong","year":"2007","unstructured":"Wong S, Sze NN, Li YC (2007) Contributory factors to traffic crashes at signalized intersections in hong kong. Accid Anal & Prev 39(6):1107\u20131113","journal-title":"Accid Anal & Prev"},{"key":"36_CR31","first-page":"72","volume":"1","author":"F Ye","year":"2014","unstructured":"Ye F, Lord D (2014) Comparing three commonly used crash severity models on sample size requirements: multinomial logit, ordered probit and mixed logit models. Anal Methods Accid Res 1:72\u201385","journal-title":"Anal Methods Accid Res"}],"container-title":["Advances in Intelligent Systems and Computing","Proceedings of the Seventh International Conference on Mathematics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-6890-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T12:42:46Z","timestamp":1700311366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-6890-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811668890","9789811668906"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-6890-6_36","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}