Time Series Analysis of Road Traffic Accidents in Ghana

Twenefour, Frank B. K.; Ayitey, Emmanuel; Kangah, Justice; Brew, Lewis

doi:10.9734/ajpas/2021/v11i230262

Cited by 4 publications

(9 citation statements)

References 8 publications

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“…The Box-Jenkins model is efficient for analyzing data and forecasting by many authors [17]. This method of forecasting implements knowledge of autocorrelation analysis based on autoregressive integrated moving average models.…”

Section: Box and Jenkins Methodsmentioning

confidence: 99%

Time series modeling of road traffic accidents in Amhara Region

Getahun

2021

J Big Data

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Road traffic accidents (RTA) are commonly encountered incidents that can cause injuries, death, and property damage to members of society. Ethiopia is one of the highest incident rates of road traffic accidents. Report of Transport and Communication from 2012 to 2014, shows an increment in the number of traffic accidents in Ethiopia. Amhara region accounted for 27.3% of the total road traffic accident-related deaths in Ethiopia during the year 2008/9, which is the highest share among all regions in Ethiopia. The current research aims to model the trend of injury, fatal and total road traffic accidents in the Amhara region from September 2013 to May 2017. Monthly reported traffic accidents were obtained from the traffic department of the Amhara region police commission. The most universal class of models for forecasting time series data called Auto-regressive Integrated Moving Averages (ARIMA) models were applied to model the trends and patterns of road traffic accident cases in the Amhara region. The average number of observed injury RTA, fatal RTA, and total RTA were 27.2, 14, and 78.2 per month respectively. It was observed that a relatively large number of RTA’s are reported on Tuesday, Thursday, and Saturday relative to other days of the week. The data also reveals that more than 60% of accidents involve drivers between the ages of 18–30 years. ARIMA (2,0,0) (1,0,0) ARIMA (2,0,0) and ARIMA (2,0,0) (1,1,0) were fitted as the best model for total injury accidents, fatal RTA and total RTA data respectively. A 48 months forecast was made based on the fitted models and it can be concluded that road traffic accident cases would continue at the non-decreasing rate in the Amhara region for the predicted periods. Therefore, the findings of this study draw attention to the importance of implementing improved better policies and close monitoring of road trafficking to change the existing non-decreasing trend of road traffic accidents in the region.

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Section: Box and Jenkins Methodsmentioning

confidence: 99%

Time series modeling of road traffic accidents in Amhara Region

Getahun

2021

J Big Data

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“…Ihuezeet al [8] and Twenefouret al [9] used auto regressive integrated moving average with exogeneous (ARIMAX) and autoregressive moving average (ARMA) models, respectively, which are classical time-series techniques. Nanga et al [10] and Yousefzadeh-Chabok et al [11] preferred the SARIMA model.…”

Section: Related Workmentioning

confidence: 99%

“…It can be said that it is an important factor if it is related to the population of the regions where traffic accident prediction studies are conducted. In the literature, some classical time series studies have been carried out with the data obtained in Nigeria [8], Iran [11] and Ghana [9,10]. Ihueze et al [8] and Twenefour et al [9] predicted crash frequency, while Yousefzadeh-Chabok et al [11] estimated fatalities.…”

Section: Related Workmentioning

confidence: 99%

“…In the literature, some classical time series studies have been carried out with the data obtained in Nigeria [8], Iran [11] and Ghana [9,10]. Ihueze et al [8] and Twenefour et al [9] predicted crash frequency, while Yousefzadeh-Chabok et al [11] estimated fatalities. In classical time-series studies, the size of the dataset obtained is also important.…”

Section: Related Workmentioning

confidence: 99%

“…In classical time-series studies, the size of the dataset obtained is also important. Accordingly, the longest-term data set [9] is 30 years, while the shortest-term data set [11] is 6 years. Ihueze et al [8] and Twenefour et al [9] used 1 year of the dataset for testing.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid CNN-LSTM Model for Traffic Accident Frequency Forecasting During the Tourist Season

2022

Teh. vjesn.

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Population density in major tourist centers of the world increases significantly during the tourist season. Estimating the frequency of traffic accidents during the upcoming tourist season is of particular interest to many stakeholders, such as local governments. The objective of this study is to propose a hybrid deep learning model, based on convolutional neural network (CNN) and long short term memory (LSTM) models to predict the frequency of traffic accidents during the tourism season. The dataset used in the study includes daily frequencies of traffic accidents with fatalities and injuries that occurred in Antalya between January 2012 and December 2017. In the next phase of the study, seasonal autoregressive integrated moving average (SARIMA), Facebook prophet and deep learning methods including LSTM and the proposed Hybrid CNN-LSTM were tested to predict traffic accident frequencies in Antalya. The experimental results show that the root mean square error (RMSE) of the proposed model is less than 2480, 13266 and 186 compared to SARIMA, prophet and LSTM models, respectively. Also, the R-squared value of the proposed model is greater than 0.016, 0.103 and 0.001 compared to SARIMA, prophet and LSTM models, respectively. It is clear that the proposed hybrid CNN-LSTM model was more successful in predicting traffic accidents when compared to the other models.

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Identifying emerging hot spots of road traffic injury severity using spatiotemporal methods: longitudinal analyses on major roads in Ghana from 2005 to 2020

Mesic,

Damsere-Derry,

Feldacker

et al. 2024

BMC Public Health

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Background Although road traffic injuries and deaths have decreased globally, there is substantial national and sub-national heterogeneity, particularly in low- and middle-income countries (LMICs). Ghana is one of few countries in Africa collecting comprehensive, spatially detailed data on motor vehicle collisions (MVCs). This data is a critical step towards improving roadway safety, as accurate and reliable information is essential for devising targeted countermeasures. Methods Here, we analyze 16 years of police-report data using emerging hot spot analysis in ArcGIS to identify hot spots with trends of increasing injury severity (a weighted composite measure of MVCs, minor injuries, severe injuries, and deaths), and counts of injuries, severe injuries, and deaths along major roads in urban and rural areas of Ghana. Results We find injury severity index sums and minor injury counts are significantly decreasing over time in Ghana while severe injury and death counts are not, indicating the latter should be the focus for road safety efforts. We identify new, consecutive, intensifying, and persistent hot spots on 2.65% of urban roads and 4.37% of rural roads. Hot spots are intensifying in terms of severity and frequency on major roads in rural areas. Conclusions A few key road sections, particularly in rural areas, show elevated levels of road traffic injury severity, warranting targeted interventions. Our method for evaluating spatiotemporal trends in MVC, road traffic injuries, and deaths in a LMIC includes sufficient detail for replication and adaptation in other countries, which is useful for targeting countermeasures and tracking progress.

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Time Series Analysis of Road Traffic Accidents in Ghana

Cited by 4 publications

References 8 publications

Time series modeling of road traffic accidents in Amhara Region

Time series modeling of road traffic accidents in Amhara Region

A Hybrid CNN-LSTM Model for Traffic Accident Frequency Forecasting During the Tourist Season

Identifying emerging hot spots of road traffic injury severity using spatiotemporal methods: longitudinal analyses on major roads in Ghana from 2005 to 2020

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