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| Reference | Sample size/period | Method/location | Findings |
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| Adebola et al. [3] | 51 years (1960–2013) | ARIMA model, Nigeria | Road injury and fatal crashes will increase in the following 7 years; however, the models should be used with caution for predicting future conditions beyond the forecasted period |
| Atubi, 2013 [4] | 32 years (1970–2001) | Time-series analysis, Nigeria | Road traffic accidents were generally on the decrease |
| Mutangi, 2015 [5] | 17 years (1997–2013) | ARIMA model, Zimbabwe | Forecasting of the number of road traffic crashes using a white noise process is difficult because the values at different times are statistically independent |
| Avuglah et al. [6] | 20 years (1991–2011) | ARIMA model, Ghana | The trends and patterns of road crashes were studied, and a five-year prediction was made. The study showed an increasing trend for the coming five years. |
| Parvareh et al. [7] | 72 months (2009–2015) | ARIMA model, Kurdistan Province, Iran | Injury crashes for the following 24 months were predicted. Motorcyclists and pedestrian injuries showed a seasonal pattern. Results showed an increasing trend in the frequency of nonfatal injuries and a decline in fatalities. |
| Zolala et al. [8] | 36 months (2013–2015) | SARIMA model, Kermanshah Province, Iran | Traffic fatalities showed a decreasing trend in the following years |
| Yousefzadeh-Chabok et al. [9] | 84 months (2007–2013) | ARIMA model, Zanjan Province, Iran | They found similar results to Zolala et al., 2016 [8] |
| Yuan et al., 2013 [10] | 60 years (1951–2011) | ARIMA model, China | Road traffic crashes would be increasing in year 2012 |
| Rohayu et al. [11] | 40 years (1972–2010) | ARIMA model, Malaysia | They predicted road fatalities for the following five years (2015 and 2020) |
| Al-Ghamdi, 1995 [12] | 11 years (1980–1991) | ARIMA model, Saudi Arabia | An increase in the studied types of crashes was revealed |
| Al-Zyood, 2017 [13] | 18 years (1998–2016) | ARIMA model, Saudi Arabia | Car crashes, injuries, and fatalities were forecasted for the following 7 years; all will be increasing |
| Al-Omari et al. [14] | 13 years (1998–2010) | Multiple-linear regression and logarithmic models, Jordan | Traffic crash frequencies and fatalities in terms of motorization level were modeled; traffic crashes were continuously increasing |
| Jadaan et al. [15] | 10 years (2000–2009) | Multiple-linear regression, Jordan | Fatalities were correlated with population, number of registered vehicles, and total length of paved roads; the crash cost will be increasing |
| Ljubič et al. [16] | 21 years (1979–1999) | Simple statistical visualization and graphic presentation, UK | A clustering approach was used to find trends of road crashes through different time segments |
| Colum, 2018 [17] | Weekly data (2005–2014) | ARIMA model, UK | Crashes, casualties, and vehicles were modeled. The models are useful in making future predictions. |
| Tang et al. [18] | 8 years (2005–2012) | Negative binomial (NB) and random parameters negative binomial (RPNB), USA | The RPNB model provides better predictions when applied to within-sample observations, while the NB model provides better predictions when applied to out-of-sample observations |
| Liu and Sharma, 2017 [19] | 10 years (2006–2015) | Integrated nested Laplace approximation (INLA) to estimate Bayesian spatiotemporal models, USA | Spatial and temporal correlations are critical in crash frequency modeling; the vehicle-miles-traveled was the only significant variable. Fatal crashes showed decreasing trends |
| Russo et al. [21] | 5 years (2006–2010) | Negative binomial regression, Italy | The AADT, lane width, curvature change rate, length, and vertical grade are important variables in explaining the severity of crashes. The HSM overestimates the crash frequency for rural undivided roads. |
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