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Motor Vehicles Forecasting in Kolhapur City Using Combined Grey Model
https://orcid.org/https://orcid.org/0000-0001-5201-6029
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.
Motor Vehicles Forecasting in Kolhapur City Using Combined Grey Model
https://orcid.org/https://orcid.org/0000-0001-5201-6029
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.
Motor Vehicles Forecasting in Kolhapur City Using Combined Grey Model
https://orcid.org/https://orcid.org/0000-0001-5201-6029
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.
Motor Vehicles Forecasting in Kolhapur City Using Combined Grey Model
KSCE J Civ Eng
Shinde, Sagar Maruti (author) / Karjinni, Vilas Vijay (author)
KSCE Journal of Civil Engineering ; 27 ; 2385-2391
2023-06-01
7 pages
Article (Journal)
Electronic Resource
English
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