School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Chile; Instituto Sistemas Complejos de Ingeniería (ISCI), Chile.
Civil Engineering Department, Universidad de Chile, Chile; Instituto Sistemas Complejos de Ingeniería (ISCI), Chile.
Accid Anal Prev. 2020 Mar;137:105436. doi: 10.1016/j.aap.2020.105436. Epub 2020 Jan 31.
Previous real-time crash prediction models have scarcely used data disaggregated by vehicle type such as light, heavy and motorcycles. Thus, little effort has been made to quantify the impact of flow composition variables as crash precursors. We analyze the advantages of having access to this data by analyzing two scenarios, namely, with aggregated and disaggregated data. For each case, we build Logistics Regressions and Support Vector Machines models to predict accidents in a major urban expressway in Santiago, Chile. Our results show that having access to disaggregated data by vehicle type increases the prediction power up to 30 % providing, at the same time, much better intuition about the actual traffic conditions that may lead to accidents. These results may be useful when evaluating technology investments and developments in urban freeways.
先前的实时事故预测模型很少使用按车辆类型（如轻型、重型和摩托车）细分的数据。因此，几乎没有努力量化流量组成变量作为事故前兆的影响。我们通过分析两种情况来分析访问此数据的优势，即聚合数据和细分数据。对于每种情况，我们构建逻辑回归和支持向量机模型来预测智利圣地亚哥一条主要城市高速公路上的事故。我们的研究结果表明，通过车辆类型访问细分数据可以将预测能力提高高达 30%，同时提供对可能导致事故的实际交通状况的更好理解。这些结果在评估城市高速公路的技术投资和发展时可能会很有用。
