Motor Vehicles Forecasting in Kolhapur City Using Combined Grey Model
Kolhapur city has witnessed consistent growth in motor vehicles (MV), and an accurate forecast is essential. To this end, a combined grey model was developed by combining the grey model (GM(1,1)) and the simple linear regression (SLR) model. The new model, named the grey simple linear regression mod...
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| Vydané v: | KSCE Journal of Civil Engineering Ročník 27; číslo 6; s. 2385 - 2391 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Seoul
Korean Society of Civil Engineers
01.06.2023
Springer Nature B.V 대한토목학회 |
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| ISSN: | 1226-7988, 1976-3808 |
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| Abstract | Kolhapur city has witnessed consistent growth in motor vehicles (MV), and an accurate forecast is essential. To this end, a combined grey model was developed by combining the grey model (GM(1,1)) and the simple linear regression (SLR) model. The new model, named the grey simple linear regression model (abbreviated as GSLRM), is newly utilised for MV prediction. A total of five years (2008–2012) of MV data were employed. The accuracy of the proposed GSLRM was compared with the GM(1,1) and SLR models in terms of the mean absolute percentage error (MAPE). The results revealed that all models meet high accuracy (MAPE < 10%). However, the GSLRM was slightly more accurate (MAPE = 3.85%) than the competing models. Moreover, with a reasonable development coefficient value (
a
≤ 0.3), the GSLRM could be used for mid-long-term forecasts. Subsequently, the GSLRM was used to forecast MV for the next seven years (2013–2019). The forecast results showed that the total MV would increase continuously. In summary, the GSLRM proved its reliability and validity in forecasting the total MV in Kolhapur city, and it can assist the government in drafting relevant policies. Moreover, this study also attempted to investigate the relationship between the population and RMV growth and found that population could be one of the responsible factors. |
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| AbstractList | Kolhapur city has witnessed consistent growth in motor vehicles (MV), and an accurate forecast is essential. To this end, a combined grey model was developed by combining the grey model (GM(1,1)) and the simple linear regression (SLR) model. The new model, named the grey simple linear regression model (abbreviated as GSLRM), is newly utilised for MV prediction. A total of five years (2008 – 2012) of MV data were employed. The accuracy of the proposed GSLRM was compared with the GM(1,1) and SLR models in terms of the mean absolute percentage error (MAPE). The results revealed that all models meet high accuracy (MAPE < 10%). However, the GSLRM was slightly more accurate (MAPE = 3.85%) than the competing models. Moreover, with a reasonable development coefficient value (a ≤ 0.3), the GSLRM could be used for mid-long-term forecasts. Subsequently, the GSLRM was used to forecast MV for the next seven years (2013 – 2019). The forecast results showed that the total MV would increase continuously. In summary, the GSLRM proved its reliability and validity in forecasting the total MV in Kolhapur city, and it can assist the government in drafting relevant policies. Moreover, this study also attempted to investigate the relationship between the population and RMV growth and found that population could be one of the responsible factors. KCI Citation Count: 0 Kolhapur city has witnessed consistent growth in motor vehicles (MV), and an accurate forecast is essential. To this end, a combined grey model was developed by combining the grey model (GM(1,1)) and the simple linear regression (SLR) model. The new model, named the grey simple linear regression model (abbreviated as GSLRM), is newly utilised for MV prediction. A total of five years (2008–2012) of MV data were employed. The accuracy of the proposed GSLRM was compared with the GM(1,1) and SLR models in terms of the mean absolute percentage error (MAPE). The results revealed that all models meet high accuracy (MAPE < 10%). However, the GSLRM was slightly more accurate (MAPE = 3.85%) than the competing models. Moreover, with a reasonable development coefficient value (a ≤ 0.3), the GSLRM could be used for mid-long-term forecasts. Subsequently, the GSLRM was used to forecast MV for the next seven years (2013–2019). The forecast results showed that the total MV would increase continuously. In summary, the GSLRM proved its reliability and validity in forecasting the total MV in Kolhapur city, and it can assist the government in drafting relevant policies. Moreover, this study also attempted to investigate the relationship between the population and RMV growth and found that population could be one of the responsible factors. Kolhapur city has witnessed consistent growth in motor vehicles (MV), and an accurate forecast is essential. To this end, a combined grey model was developed by combining the grey model (GM(1,1)) and the simple linear regression (SLR) model. The new model, named the grey simple linear regression model (abbreviated as GSLRM), is newly utilised for MV prediction. A total of five years (2008–2012) of MV data were employed. The accuracy of the proposed GSLRM was compared with the GM(1,1) and SLR models in terms of the mean absolute percentage error (MAPE). The results revealed that all models meet high accuracy (MAPE < 10%). However, the GSLRM was slightly more accurate (MAPE = 3.85%) than the competing models. Moreover, with a reasonable development coefficient value ( a ≤ 0.3), the GSLRM could be used for mid-long-term forecasts. Subsequently, the GSLRM was used to forecast MV for the next seven years (2013–2019). The forecast results showed that the total MV would increase continuously. In summary, the GSLRM proved its reliability and validity in forecasting the total MV in Kolhapur city, and it can assist the government in drafting relevant policies. Moreover, this study also attempted to investigate the relationship between the population and RMV growth and found that population could be one of the responsible factors. |
| Author | Shinde, Sagar Maruti Karjinni, Vilas Vijay |
| Author_xml | – sequence: 1 givenname: Sagar Maruti orcidid: 0000-0001-5201-6029 surname: Shinde fullname: Shinde, Sagar Maruti email: sagar.shinde@seti.edu.in organization: Shivaji University, Dept. of Civil Engineering, Sanjeevan Engineering and Technology Institute – sequence: 2 givenname: Vilas Vijay surname: Karjinni fullname: Karjinni, Vilas Vijay organization: Kolhapur Institute of Technology’s College of Engineering (Autonomous) |
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| Cites_doi | 10.9790/3021-026113821390 10.3390/ijerph14030262 10.1007/s10661-005-9043-2 10.3390/su8101037 10.1016/j.jart.2015.06.009 10.3390/en12020236 10.1016/j.enpol.2009.03.039 10.1016/j.techfore.2006.07.010 10.14706/JECOSS11126 10.1016/j.jhydrol.2021.127249 10.3923/itj.2017.11.16 10.1016/S0167-6911(82)80025-X 10.1371/journal.pone.0201987 10.5430/bmr.v5n3p86 10.1680/jcien.18.00014 10.1002/for.2353 10.7708/ijtte.2013.3(2).04 10.3390/su9071181 10.1080/02522667.2005.10699664 10.1023/A:1015138118757 |
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| Keywords | Combined model Simple linear regression model Forecast accuracy Motor vehicles Grey model |
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| SubjectTerms | Accuracy Civil Engineering Engineering Engines Environmental Engineering Forecasting Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Motor vehicles Regression analysis Regression models Vehicles 토목공학 |
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