分析崩溃数据是一个复杂且劳动密集型的过程,需要仔细考虑多个相互依赖的建模方面。如功能形式,转换,可能的促成因素,相关性,和未观察到的异质性。有限的时间,知识,经验可能会导致过度简化,过度装配,或忽略重要见解的错误指定模型。本文提出了一个广泛的假设检验框架,包括多目标数学规划公式和求解算法,以估计同时考虑可能的影响因素的碰撞频率模型。转换,非线性,和相关的随机参数。数学规划公式最小化样本内拟合和样本外预测。为了解决数学程序的复杂性和非凸性,所提出的解决方案框架利用各种元启发式解决方案算法。具体来说,和声搜索对超参数的敏感性最小,实现对解决方案的有效搜索,而不受超参数选择的影响。使用两个真实世界的数据集和一个合成数据集来评估框架的有效性。使用两个真实世界的数据集和独立团队在文献中发布的相应模型进行比较分析。所提出的框架显示了其查明有效模型规格的能力,产生准确的估计,并为研究人员和从业人员提供有价值的见解。所提出的方法可以发现许多见解,同时最大程度地减少模型开发所花费的时间。通过考虑更广泛的因素,可以生成具有不同质量的模型。例如,当应用于昆士兰州的崩溃数据时,拟议的方法表明,在急剧弯曲的道路上加入中间分隔可以有效减少撞车的发生,当应用于华盛顿的崩溃数据时,同时考虑交通量和道路曲率,导致碰撞差异显着减少,但碰撞手段增加。
Analyzing crash data is a complex and labor-intensive process that requires careful consideration of multiple interdependent modeling aspects, such as functional forms, transformations, likely contributing factors, correlations, and unobserved heterogeneity. Limited time, knowledge, and experience may lead to over-simplified, over-fitted, or misspecified models overlooking important insights. This paper proposes an extensive hypothesis testing framework including a multi-objective mathematical programming formulation and solution algorithms to estimate crash frequency models considering simultaneously likely contributing factors, transformations, non-linearities, and correlated random parameters. The mathematical programming formulation minimizes both in-sample fit and out-of-sample prediction. To address the complexity and non-convexity of the mathematical program, the proposed solution framework utilizes a variety of metaheuristic solution algorithms. Specifically, Harmony Search demonstrated minimal sensitivity to hyperparameters, enabling an efficient search for solutions without being influenced by the choice of hyperparameters. The effectiveness of the framework was evaluated using two real-world datasets and one synthetic dataset. Comparative analyses were performed using the two real-world datasets and the corresponding models published in literature by independent teams. The proposed framework showed its capability to pinpoint efficient model specifications, produce accurate estimates, and provide valuable insights for both researchers and practitioners. The proposed approach allows for the discovery of numerous insights while minimizing the time spent on model development. By considering a broader set of contributing factors, models with varied qualities can be generated. For instance, when applied to crash data from Queensland, the proposed approach revealed that the inclusion of medians on sharp curved roads can effectively reduce the occurrence of crashes, when applied to crash data from Washington, the simultaneous consideration of traffic volume and road curvature resulted in a notable reduction in crash variances but an increase in crash means.