Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations

Khaled Hamad , Emran Alotaibi , Waleed Zeiada , Ghazi Al-Khateeb , Saleh Abu Dabous , Maher Omar , Bharadwaj R.K. Mantha , Mohamed G. Arab , Tarek Merabtene
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Abstract

Efficient management of traffic incidents is a focal point in traffic management, with direct implications for road safety, congestion, and the environment. Traditional models have grappled with the unpredictability inherent in traffic incidents, often failing to capture the multifaceted influences on incident durations. This study introduces an application of Explainable Artificial Intelligence (XAI) using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to analyze the complexities of traffic incident duration prediction. Utilizing a substantial dataset of over 366,000 records from the Houston traffic management center, the study innovates in the domain of traffic analytics by predicting incident durations and revealing the contribution of each predictive variable. The XGBoost algorithm's ability to handle multi-dimensional datasets was employed to identify crucial variables affecting incident durations. Meanwhile, SHAP values offered transparency into the model's decision-making process, clarifying the roles of over fifty parameters. The study's results demonstrate that variables such as the involvement of heavy trucks and blockage of main lanes are essential in influencing incident durations, aligning with findings from previous literature. The SHAP analysis further revealed time-sensitive patterns, with time of day and day of the week exhibiting considerable effects on predictions. The beeswarm plots of SHAP provided a detailed visualization of these effects, differentiating between high and low values effects for each variable. The model's high accuracy, with a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 21.2 min, indicates the potential of XAI in enhancing traffic management systems.
有效管理交通事故是交通管理的重点,对道路安全、交通拥堵和环境都有直接影响。传统模型一直在努力解决交通事故固有的不可预测性问题,但往往无法捕捉到事故持续时间的多方面影响因素。本研究介绍了一种可解释人工智能(XAI)的应用,即使用极梯度提升(XGBoost)和SHAPLEY Additive exPlanations(SHAP)来分析交通事故持续时间预测的复杂性。该研究利用休斯顿交通管理中心超过 366,000 条记录的大量数据集,通过预测事故持续时间和揭示每个预测变量的贡献,在交通分析领域进行了创新。XGBoost 算法具有处理多维数据集的能力,可用于识别影响事故持续时间的关键变量。同时,SHAP 值为模型的决策过程提供了透明度,明确了 50 多个参数的作用。研究结果表明,重型卡车的参与和主要车道的堵塞等变量在影响事故持续时间方面至关重要,这与以往文献的研究结果一致。SHAP 分析进一步揭示了对时间敏感的模式,一天中的时间和一周中的某一天对预测有相当大的影响。SHAP 的蜂群图提供了这些影响的详细直观图,区分了每个变量的高值和低值影响。该模型的准确度很高,决定系数 (R2) 为 0.72,均方根误差 (RMSE) 为 21.2 分钟,这表明 XAI 在增强交通管理系统方面具有潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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