Traffic collisions are considered a serious problem worldwide that cause severe losses. Gulf Cooperation Council (GCC) Countries have high rates of collisions, which requires urgent proactive strategies and attention to solve such problem. This study aimed to enhance traffic safety conditions and reduce collisions' severity levels at several locations in Kuwait City, Kuwait. Consequently, the study importance raised in four folds; reducing the total number of collisions on Kuwait highways, predicting the future numbers of collisions, identifying and managing risk factors contributing to collision's severity, and developing new strategies to enhance traffic safety condition in Kuwait. Three-year crash dataset from 2016 to 2018 including 4028 road collisions in Kuwait City were used to analyse the driver injury severity influence factors and to predict the future collision counts. Statistical indices were used to evaluate the mixed logit model performance, which are the MCFadden Pseudo R-Squared statistics and the two-log likelihood. Eight covariates were tested for significance in the mixed logit model estimation. Results showed that female drivers, driving during night-time, driving outside the city, and not using seatbelt produced the highest possibility of having incapacitating injuries and fatalities by 46.6% and 31.6%, respectively. Contrastingly, male drivers, driving during daytime, driving inside the city, and using seatbelt had resulted in the lowest probability of getting incapacitating injuries and fatalities by 4.7% and 0.2%, respectively. Furthermore, Bayesian hierarchical model was used to investigate the future road collision counts and to identify the collision blackspots (CBS) on Kuwait City highways. Six covariates were considered in the model. Log-linear regression model (CPM) was used to estimate the vector of means ${\mathrm{\mu }}_{\text{j}}\left(\text{t}\right)$. By applying regression to mean (RTM) and predicting the trend in the mean collision rate λj (t), results showed that the average model accuracy for year 2018 was 45.76% (for a 95% confidence interval estimation using the collision dataset of previous years). It was also found that the highest number of collisions had occurred on the second ring road. Basing the results on year 2018 helped in predicting more accurate future collision counts, and in justifying collision rates. Additionally, further covariates can be added to the models for any additional recent crash data to increase the model accuracy. This work is anticipated to help Kuwaiti decision makers in designing accurate countermeasures to improve traffic safety condition.

在世界范围内，交通冲突被认为是一个严重的问题，会造成严重的损失。海湾合作委员会（GCC）国家的撞车率很高，这需要紧急的积极策略并给予关注以解决这一问题。这项研究旨在改善交通安全状况，并降低科威特科威特市多个地点的碰撞严重程度。因此，研究的重要性提高了四倍。减少科威特高速公路上的碰撞总数，预测未来的碰撞次数，识别和管理导致碰撞严重性的风险因素，并制定新的策略来改善科威特的交通安全状况。使用2016年至2018年的三年碰撞数据集，包括科威特市的4028处道路碰撞，来分析驾驶员伤害严重性的影响因素并预测未来的碰撞次数。统计指标用于评估混合logit模型的性能，即MCFadden伪R平方统计量和两对数可能性。测试了八个协变量在混合logit模型估计中的显着性。结果表明，夜间驾驶，在城市外驾驶和不使用安全带的女性驾驶员，致残伤亡的可能性最高，分别为46.6％和31.6％。相比之下，白天白天开车，在城市里面开车以及使用安全带的男性驾驶员，致残伤亡的可能性最低，为4。分别为7％和0.2％。此外，使用贝叶斯分层模型来调查未来的道路碰撞计数并识别科威特市高速公路上的碰撞黑点（CBS）。在模型中考虑了六个协变量。使用对数线性回归模型（CPM）估计均值向量${\mathrm{\mu }}_{\text{Ĵ}}（\text{Ť}）$。通过应用均值回归（RTM）并预测平均碰撞率λj（t）的趋势，结果显示2018年的平均模型准确性为45.76％（对于使用前几年碰撞数据集的95％置信区间估计）。还发现在第二环路上发生的碰撞次数最多。基于2018年的结果有助于预测更准确的未来碰撞计数，并证明碰撞率合理。另外，可以为任何其他近期崩溃数据将更多协变量添加到模型中，以提高模型的准确性。预期这项工作将帮助科威特决策者设计准确的对策，以改善交通安全状况。