Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India

Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehe...
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Autor*in:	Koramati, Siddardha [verfasserIn] Mukherjee, Arnab Majumdar, Bandhan Bandhu Kar, Arkamitra

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Crash prediction Secondary crash data Artificial neural network (ANN) Sensitivity analysis

Anmerkung:	© The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Journal of the Institution of Engineers (India) - [New Delhi] : Springer India, 2012, 104(2022), 1 vom: 25. Okt., Seite 63-80
Übergeordnetes Werk:	volume:104 ; year:2022 ; number:1 ; day:25 ; month:10 ; pages:63-80

Links:	Volltext

DOI / URN:	10.1007/s40030-022-00696-4

Katalog-ID:	SPR049252135

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520			\|a Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status.
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650		4	\|a Artificial neural network (ANN) \|7 (dpeaa)DE-He213
650		4	\|a Sensitivity analysis \|7 (dpeaa)DE-He213
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allfields	10.1007/s40030-022-00696-4 doi (DE-627)SPR049252135 (SPR)s40030-022-00696-4-e DE-627 ger DE-627 rakwb eng Koramati, Siddardha verfasserin aut Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. Crash prediction (dpeaa)DE-He213 Secondary crash data (dpeaa)DE-He213 Artificial neural network (ANN) (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Mukherjee, Arnab aut Majumdar, Bandhan Bandhu (orcid)0000-0001-7721-8436 aut Kar, Arkamitra aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 104(2022), 1 vom: 25. Okt., Seite 63-80 (DE-627)722236743 (DE-600)2677555-4 2250-2157 nnns volume:104 year:2022 number:1 day:25 month:10 pages:63-80 https://dx.doi.org/10.1007/s40030-022-00696-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 1 25 10 63-80
spelling	10.1007/s40030-022-00696-4 doi (DE-627)SPR049252135 (SPR)s40030-022-00696-4-e DE-627 ger DE-627 rakwb eng Koramati, Siddardha verfasserin aut Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. Crash prediction (dpeaa)DE-He213 Secondary crash data (dpeaa)DE-He213 Artificial neural network (ANN) (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Mukherjee, Arnab aut Majumdar, Bandhan Bandhu (orcid)0000-0001-7721-8436 aut Kar, Arkamitra aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 104(2022), 1 vom: 25. Okt., Seite 63-80 (DE-627)722236743 (DE-600)2677555-4 2250-2157 nnns volume:104 year:2022 number:1 day:25 month:10 pages:63-80 https://dx.doi.org/10.1007/s40030-022-00696-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 1 25 10 63-80
allfields_unstemmed	10.1007/s40030-022-00696-4 doi (DE-627)SPR049252135 (SPR)s40030-022-00696-4-e DE-627 ger DE-627 rakwb eng Koramati, Siddardha verfasserin aut Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. Crash prediction (dpeaa)DE-He213 Secondary crash data (dpeaa)DE-He213 Artificial neural network (ANN) (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Mukherjee, Arnab aut Majumdar, Bandhan Bandhu (orcid)0000-0001-7721-8436 aut Kar, Arkamitra aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 104(2022), 1 vom: 25. Okt., Seite 63-80 (DE-627)722236743 (DE-600)2677555-4 2250-2157 nnns volume:104 year:2022 number:1 day:25 month:10 pages:63-80 https://dx.doi.org/10.1007/s40030-022-00696-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 1 25 10 63-80
allfieldsGer	10.1007/s40030-022-00696-4 doi (DE-627)SPR049252135 (SPR)s40030-022-00696-4-e DE-627 ger DE-627 rakwb eng Koramati, Siddardha verfasserin aut Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. Crash prediction (dpeaa)DE-He213 Secondary crash data (dpeaa)DE-He213 Artificial neural network (ANN) (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Mukherjee, Arnab aut Majumdar, Bandhan Bandhu (orcid)0000-0001-7721-8436 aut Kar, Arkamitra aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 104(2022), 1 vom: 25. Okt., Seite 63-80 (DE-627)722236743 (DE-600)2677555-4 2250-2157 nnns volume:104 year:2022 number:1 day:25 month:10 pages:63-80 https://dx.doi.org/10.1007/s40030-022-00696-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 1 25 10 63-80
allfieldsSound	10.1007/s40030-022-00696-4 doi (DE-627)SPR049252135 (SPR)s40030-022-00696-4-e DE-627 ger DE-627 rakwb eng Koramati, Siddardha verfasserin aut Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. Crash prediction (dpeaa)DE-He213 Secondary crash data (dpeaa)DE-He213 Artificial neural network (ANN) (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Mukherjee, Arnab aut Majumdar, Bandhan Bandhu (orcid)0000-0001-7721-8436 aut Kar, Arkamitra aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 104(2022), 1 vom: 25. Okt., Seite 63-80 (DE-627)722236743 (DE-600)2677555-4 2250-2157 nnns volume:104 year:2022 number:1 day:25 month:10 pages:63-80 https://dx.doi.org/10.1007/s40030-022-00696-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 1 25 10 63-80
language	English
source	Enthalten in Journal of the Institution of Engineers (India) 104(2022), 1 vom: 25. Okt., Seite 63-80 volume:104 year:2022 number:1 day:25 month:10 pages:63-80
sourceStr	Enthalten in Journal of the Institution of Engineers (India) 104(2022), 1 vom: 25. Okt., Seite 63-80 volume:104 year:2022 number:1 day:25 month:10 pages:63-80
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topic_facet	Crash prediction Secondary crash data Artificial neural network (ANN) Sensitivity analysis
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container_title	Journal of the Institution of Engineers (India)
authorswithroles_txt_mv	Koramati, Siddardha @@aut@@ Mukherjee, Arnab @@aut@@ Majumdar, Bandhan Bandhu @@aut@@ Kar, Arkamitra @@aut@@
publishDateDaySort_date	2022-10-25T00:00:00Z
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author	Koramati, Siddardha
spellingShingle	Koramati, Siddardha misc Crash prediction misc Secondary crash data misc Artificial neural network (ANN) misc Sensitivity analysis Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India
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topic_title	Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India Crash prediction (dpeaa)DE-He213 Secondary crash data (dpeaa)DE-He213 Artificial neural network (ANN) (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213
topic	misc Crash prediction misc Secondary crash data misc Artificial neural network (ANN) misc Sensitivity analysis
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title_full	Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India
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title_sort	development of crash prediction model using artificial neural network (ann): a case study of hyderabad, india
title_auth	Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India
abstract	Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Machine learning (ML)-based prediction of urban crashes using secondary crash data derived from police crash databases is limited in India. This research demonstrated an artificial neural network (ANN) technique to formulate crash prediction models based on Hyderabad police data. A comprehensive crash database with data collected from Hyderabad police in 2015–2019 was used to develop a set of ANN models using various combinations of learning algorithms and activation functions. Among them, the ANN model with RMSProp and Sigmoid learning algorithm and Softmax and FTRL activation functions were observed to produce the best-fit prediction models. Based on a subsequent sensitivity analysis, “Cause of crash,” followed by “Road Geometry,” “Month of crash occurrence,” “Time of crash occurrence,” “crime vehicle type” and “victim vehicle type” were found to be sensitive factors influencing the crash outcome (fatal/non-fatal). Identification of such causative risk factors would help formulate targeted mitigation measures to improve the urban road safety status. © The Institution of Engineers (India) 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Development of Crash Prediction Model using Artificial Neural Network (ANN): A Case Study of Hyderabad, India
url	https://dx.doi.org/10.1007/s40030-022-00696-4
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author2	Mukherjee, Arnab Majumdar, Bandhan Bandhu Kar, Arkamitra
author2Str	Mukherjee, Arnab Majumdar, Bandhan Bandhu Kar, Arkamitra
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doi_str	10.1007/s40030-022-00696-4
up_date	2024-07-04T00:02:26.247Z
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