The real-time crash likelihood prediction model is an essential component of the proactive traffic safety management system. Over the years, numerous studies have attempted to construct a crash likelihood prediction model in order to enhance traffic safety, but mostly on freeways. In the majority of the existing studies, researchers have primarily employed a deep learning-based framework to identify crash potential. Lately, Transformer has emerged as a potential deep neural network that fundamentally operates through attention-based mechanisms. Transformer has several functional benefits over extant deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural Network (CNN), etc. Firstly, Transformer can readily handle long-term dependencies in a data sequence. Secondly, Transformers can parallelly process all elements in a data sequence during training. Finally, a Transformer does not have the vanishing gradient issue. Realizing the immense possibility of Transformers, this paper proposes inTersection-Transformer (inTformer), a time-embedded attention-based Transformer model that can effectively predict intersection crash likelihood in real-time. The proposed model was evaluated using connected vehicle data extracted from INRIX and Center for Advanced Transportation Technology (CATT) Lab's Signal Analytics Platform. The data was parallelly formatted and stacked at different timesteps to develop nine inTformer models. The best inTformer model achieved a sensitivity of 73%. This model was also compared to earlier studies on crash likelihood prediction at intersections and with several established deep learning models trained on the same connected vehicle dataset. In every scenario, this inTformer outperformed the benchmark models confirming the viability of the proposed inTformer architecture.

翻译：实时碰撞风险预测模型是主动式交通安全管理体系的核心组成部分。多年来，众多研究致力于构建碰撞风险预测模型以提升交通安全水平，但其研究场景主要集中于高速公路。现有研究中，学者们主要采用基于深度学习的框架来识别碰撞风险。近年来，Transformer作为一种基于注意力机制的深度神经网络崭露头角。与长短期记忆网络（LSTM）、卷积神经网络（CNN）等现有深度学习模型相比，Transformer具有多项功能优势：首先，它能有效处理数据序列中的长期依赖关系；其次，在训练过程中可并行处理序列中的所有元素；最后，它不存在梯度消失问题。基于Transformer的巨大潜力，本文提出inTersection-Transformer（inTformer）——一种基于时间嵌入注意力的Transformer模型，可实时有效预测交叉口碰撞风险。该模型使用从INRIX与先进交通技术实验室（CATT Lab）信号分析平台提取的网联车辆数据进行评估。通过并行格式化处理并按不同时间步长堆叠数据，本研究构建了九个inTformer模型。最优模型实现了73%的灵敏度。同时，将该模型与既有交叉口碰撞风险预测研究及基于相同网联车辆数据集训练的多个成熟深度学习模型进行对比，在所有测试场景中，inTformer均优于基准模型，验证了所提inTformer架构的可行性。