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首页> 外文会议>International conference on electrical, control computer engineering >Forecasting Road Deaths in Malaysia Using Support Vector Machine
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Forecasting Road Deaths in Malaysia Using Support Vector Machine

机译：使用支持向量机预测马来西亚的道路死亡

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An average of 6,350 people died every year in Malaysia due to road traffic accidents. A published data of Malaysian road deaths in 20 years since 1997 reveals that the number of fatalities has not really declined with a difference of less than 10% from one year to the next. Forecasting the number of fatalities is beneficial in planning a countermeasure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as autoregressive integrated moving average (ARIMA). The model was used in the previous Road Safety Plan of Malaysia to set a target death toll to be reduced in 2020, albeit being inaccurate. This study proposes a new approach in forecasting the road deaths, by means of a machine learning algorithm known as Support Vector Machine. The length of various types of road, number of registered vehicles and population were among the eight features used to develop the model. Comparison between the actual road deaths and the prediction demonstrates a good agreement, with a mean absolute percentage error of 2% and an R-squared value of 85%. The Linear kernel-based Support Vector Machine was found to be able to predict the road deaths in Malaysia with reasonable accuracy. The developed model could be used by relevant stakeholders in devising appropriate policies and regulations to reduce road fatalities in Malaysia.
机译：在马来西亚，每年平均有6,350人死于道路交通事故。自1997年以来的20年中，马来西亚道路死亡人数的公开数据显示，死亡人数并没有真正减少，每年与下一年的相差不到10％。预测死亡人数有助于规划减少死亡人数的对策。马来西亚道路死亡的预测模型是使用称为自回归综合移动平均值（ARIMA）的时间序列模型开发的。该模型在马来西亚先前的《道路安全计划》中用于设定目标死亡人数，尽管该数字不准确，但要在2020年降低。这项研究通过一种称为支持向量机的机器学习算法，提出了一种预测道路死亡的新方法。各种类型的道路长度，注册车辆的数量和人口是用于开发该模型的八个特征。实际道路死亡人数与预测之间的比较显示出良好的一致性，平均绝对百分比误差为2％，R平方值为85％。发现基于线性核的支持向量机能够以合理的精度预测马来西亚的道路死亡。相关利益攸关方可以使用已开发的模型来制定适当的政策和法规，以减少马来西亚的道路交通事故。

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