Abstract
Around the world, road traffic accidents (RTAs) cause significant concerns for decision makers and researchers on traffic safety. The diversity, rarity, and interconnectivity of historical data on factors causing car accidents point to the need for more focused studies for analyzing, predicting, and visualizing the risk of accidents over the short and long term for preventive purposes. There are many techniques and tools applied to analyze, forecast, and visualize risk. Most RTA studies have applied linear time-series methods to forecasting the risk with limited studies applying machine-learning and deep-learning techniques, especially in Saudi Arabia. Recently, many global studies have applied long short-term memory (LSTM) networks, which can be used to automatically learn the temporal dependence structures for challenging time-series forecasting problems. This paper displays a tutorial for designing a prototype of an interactive analytical tool based on a multivariate LSTM model for time-series data to predict future car accidents, fatalities, and injuries in the Kingdom of Saudi Arabia (KSA). This interactive tool visualizes the real data with the predicted values regionally in a web browser with Python. The tutorial represents the annual data of the period between 1417 (1996) and 1433 (2013), then uses the data with some contributing factors, such as population, gender, nationality, number of vehicles, and length of road, to generate the input data and predict the future values of accidents, fatalities, and injuries up to the year 1452 (2030). After that the real and predicted values are visualized regionally on an interactive map that represents the degree of risk. Finally, the paper discusses the evaluation and utilization of the proposed prototype in the future in the field of road safety.
Highlights
Predicting road traffic accidents (RTAs) can improve road safety for both travelers and road-safety administrators
The autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) are two basic prediction methods that are notable for time-series prediction models based on regression
The results showed that the variables are cointegrated, meaning that there is a stable long-run relationship between RTA and its determinants, there may be deviation in the short-run; RTAs and the independent variables have bidirectional causality in the Kingdom of Saudi Arabia (KSA)
Summary
Predicting road traffic accidents (RTAs) can improve road safety for both travelers and road-safety administrators. Two approaches exist for estimating the time-series forecasting of RTAs. The first approach is a regression problem that forecasts the number of accidents based on the attributes of the accident dataset [5]. The autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) are two basic prediction methods that are notable for time-series prediction models based on regression [6] Most statistical models have difficulty dealing with complexity, randomness, irregularity, and the nonlinearity of real data, limiting the precision of their predictions; machine-learning models as in [7] can obtain more accurate predictions when compared with the traditional models because of repeated training iterations and learning approximation mechanisms [8]. In the case of time-series prediction, a lack of efficient processing of sequence dependencies between input variables is a problem in some machine-learning methods [9]
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