Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints
Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness o...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023097529 |
| _version_ | 1797383996054700032 |
|---|---|
| author | Edmund F. Agyemang Joseph A. Mensah Eric Ocran Enock Opoku Ezekiel N.N. Nortey |
| author_facet | Edmund F. Agyemang Joseph A. Mensah Eric Ocran Enock Opoku Ezekiel N.N. Nortey |
| author_sort | Edmund F. Agyemang |
| collection | DOAJ |
| description | Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana's Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data. SARIMA(0,1,1)×(1,0,0)12 was found to be the suitable model for forecasting RTAs with a log-likelihood value of −266.28, AIC value of 538.56, AICc value of 538.92, BIC value of 545.35. The findings disclosed that the SARIMA(0,1,1)×(1,0,0)12 model developed outperforms FB-Prophet with a forecast accuracy of 93.1025% as clearly depicted by the model's MAPE of 6.8975% and a Theil U1 statistic of 0.0376 compared to the FB-Prophet model's respective forecasted accuracy and Theil U1 statistic of 84.3569% and 0.1071. A Ljung-Box test on the residuals of the estimated SARIMA(0,1,1)×(1,0,0)12 model revealed that they are independent and free from auto/serial correlation. A Box-Pierce test for larger lags also revealed that the proposed model is adequate for forecasting. Due to the high forecast accuracy of the proposed SARIMA model, the study recommends the use of the proposed SARIMA model in the analysis of road traffic accidents in Ghana. |
| first_indexed | 2024-03-08T21:29:03Z |
| format | Article |
| id | doaj.art-469d3be1932e41f4a53bb7489dc689a2 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T21:29:03Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-469d3be1932e41f4a53bb7489dc689a22023-12-21T07:33:46ZengElsevierHeliyon2405-84402023-12-01912e22544Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepointsEdmund F. Agyemang0Joseph A. Mensah1Eric Ocran2Enock Opoku3Ezekiel N.N. Nortey4Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana; School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley, USA; Department of Computer Science, Ashesi University, No. 1 University Avenue, Berekuso, Accra, Eastern Region, Ghana; Corresponding author at: Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana.Department of Computer Science, Ashesi University, No. 1 University Avenue, Berekuso, Accra, Eastern Region, GhanaDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, GhanaDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana; Department of Computer Science, Ashesi University, No. 1 University Avenue, Berekuso, Accra, Eastern Region, GhanaDepartment of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, GhanaRoad traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana's Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data. SARIMA(0,1,1)×(1,0,0)12 was found to be the suitable model for forecasting RTAs with a log-likelihood value of −266.28, AIC value of 538.56, AICc value of 538.92, BIC value of 545.35. The findings disclosed that the SARIMA(0,1,1)×(1,0,0)12 model developed outperforms FB-Prophet with a forecast accuracy of 93.1025% as clearly depicted by the model's MAPE of 6.8975% and a Theil U1 statistic of 0.0376 compared to the FB-Prophet model's respective forecasted accuracy and Theil U1 statistic of 84.3569% and 0.1071. A Ljung-Box test on the residuals of the estimated SARIMA(0,1,1)×(1,0,0)12 model revealed that they are independent and free from auto/serial correlation. A Box-Pierce test for larger lags also revealed that the proposed model is adequate for forecasting. Due to the high forecast accuracy of the proposed SARIMA model, the study recommends the use of the proposed SARIMA model in the analysis of road traffic accidents in Ghana.http://www.sciencedirect.com/science/article/pii/S2405844023097529Road traffic accidentSARIMAFacebook ProphetPotential changepointsGhana |
| spellingShingle | Edmund F. Agyemang Joseph A. Mensah Eric Ocran Enock Opoku Ezekiel N.N. Nortey Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints Heliyon Road traffic accident SARIMA Facebook Prophet Potential changepoints Ghana |
| title | Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints |
| title_full | Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints |
| title_fullStr | Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints |
| title_full_unstemmed | Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints |
| title_short | Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints |
| title_sort | time series based road traffic accidents forecasting via sarima and facebook prophet model with potential changepoints |
| topic | Road traffic accident SARIMA Facebook Prophet Potential changepoints Ghana |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023097529 |
| work_keys_str_mv | AT edmundfagyemang timeseriesbasedroadtrafficaccidentsforecastingviasarimaandfacebookprophetmodelwithpotentialchangepoints AT josephamensah timeseriesbasedroadtrafficaccidentsforecastingviasarimaandfacebookprophetmodelwithpotentialchangepoints AT ericocran timeseriesbasedroadtrafficaccidentsforecastingviasarimaandfacebookprophetmodelwithpotentialchangepoints AT enockopoku timeseriesbasedroadtrafficaccidentsforecastingviasarimaandfacebookprophetmodelwithpotentialchangepoints AT ezekielnnnortey timeseriesbasedroadtrafficaccidentsforecastingviasarimaandfacebookprophetmodelwithpotentialchangepoints |