This work identifies factors that influence crash occurrence within a traffic analysis zone (TAZ) by accounting for location-specific effects and serial correlation in longitudinal crash data. This is accomplished by applying a random effect negative binomial (RENB) model. Unlike commonly used count models such as Poisson and negative binomial (NB), RENB accounts for heterogeneity and serial correlation in crash occurrence. An RENB was applied to 15 years of crash data in Arkansas with 1,817 TAZs. Four models were developed for total crashes and by severity (property damage only (PDO), injury, and fatal). RENB-estimated impacts were measured using the incidence rate ratio (IRR). The significant causal factors found to increase in observed crashes include: (i) average precipitation (a one-unit increase in average precipitation results in a 134% increase in total monthly crashes for a TAZ); (ii) average wind speed (16%); (iii) urban designation (7%); (iv) traffic volume (2%); and (v) total roadway mileage (1% for each functional class). Snow depth and days of sunshine were found to decrease the number of accidents by 15% and 2%, respectively. Employment and total population had no impact on crash occurrence. Goodness-of-fit comparisons show that RENB provides the best fit among Poisson and NB formulations. All four model diagnostics confirm the presence of over-dispersion and serial correlation indicating the necessity of RENB model estimation. The main contribution of this work is the identification of crash causal factors at the TAZ level for longitudinal data, which supports data-driven performance measurement requirements of recent federal legislation.

这项工作通过考虑纵向碰撞数据中的位置特定效应和序列相关性来确定影响交通分析区 (TAZ) 内碰撞发生的因素。这是通过应用随机效应负二项式 (RENB) 模型来实现的。与常用的计数模型（如泊松和负二项式 (NB)）不同，RENB 考虑了崩溃发生时的异质性和序列相关性。RENB 应用于阿肯色州 15 年的碰撞数据，其中包含 1,817 个 TAZ。针对总碰撞次数和严重程度（仅限财产损失 (PDO)、伤害和致命）开发了四种模型。RENB 估计的影响是使用发生率比 (IRR) 来衡量的。发现增加观察到的崩溃的重要原因包括：(i) 平均降水量（平均降水量增加一个单位会导致 TAZ 的月事故总数增加 134%）；(ii) 平均风速（16%）；(iii) 城市指定（7%）；(iv) 交通量（2%）；(v) 道路总里程（每个功能类别 1%）。发现雪深和日照天数分别使事故数量减少了 15% 和 2%。就业和总人口对事故发生没有影响。拟合优度比较表明 RENB 在 Poisson 和 NB 公式中提供了最佳拟合。所有四个模型诊断都证实了过度分散和序列相关的存在，表明 RENB 模型估计的必要性。这项工作的主要贡献是在 TAZ 级别识别纵向数据的碰撞因果因素，