ARIS: A Real Time Edge Computed Accident Risk Inference System
ARIS: A Real Time Edge Computed Accident Risk Inference System
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DOI：
10.1109/smartcomp52413.2021.00027
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Pretom Roy Ovi;E. Dey;Nirmalya Roy;A. Gangopadhyay
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作者：
Pretom Roy Ovi;E. Dey;Nirmalya Roy;A. Gangopadhyay
为了在城市环境中部署智能交通系统，需要一种有效的、实时的事故风险预测方法，以帮助维护道路安全，提供足够的医疗救助和紧急情况下的交通。减少交通事故是提高公共安全的一个重要问题，因此事故分析和预测是近年来广泛研究的课题。即使发生交通危险，具有准确事故预测的可随时展开的结构也有助于更好地管理救援资源。但当前研究的显著缺点是使用范围最小的小规模数据集，基于广泛的数据集，并且不适用于实时目的。为了克服这些挑战，我们提出了ARIS：一个基于2016年2月至2020年6月收集的覆盖美国49个州的交通事故数据集的实时交通事故预测系统。我们的方法基于深度神经网络模型，该模型利用了各种数据特征，例如对时间敏感的天气数据、文本信息和识别因素。我们已经通过在美国几个主要城市的一系列全面的实验，针对多个基线测试了ARIS，我们注意到在推理过程中，特别是在检测事故类别方面，有了显著的改进。此外，为了使我们的模型边缘可实现，我们使用了基于幅度的权重剪枝和模型量化的联合技术来压缩我们的模型。我们还演示了在资源受限的环境中部署该模型后的推断结果以及功耗分析，该环境由英特尔神经计算棒2(NCS2)和Raspberry PI 4B(RPi4)组成。我们的调查和观察表明，即使在模型压缩和部署之后，预测不寻常的交通事故事件的能力也有了重大改进。我们成功地将模型规模和推理时间分别减少了≈6倍和≈70%，而性能下降不明显。此外，为了更好地理解在我们的分析中使用的每一种单独类型变量的重要性，我们展示了一项全面的消融研究。
To deploy an intelligent transport system in urban environment, an effective and real-time accident risk prediction method is required that can help maintain road safety, provide adequate level of medical assistance and transport in case of an emergency. Reducing traffic accidents is an important problem for increasing public safety, so accident analysis and prediction have been a subject of extensive research in recent time. Even if a traffic hazard occurs, a readily deployable structure with an accurate prediction of accident can contribute to better management of rescue resources. But the significant shortcomings of current studies are the use of small-scale datasets with minimal scope, being based on extensive data sets, and not being applicable for real-time purposes. To overcome these challenges, we propose ARIS: a system for real-time traffic accident prediction built on a traffic accident dataset named ‘US-Accidents’ which covers 49 states of United States, collected from February 2016 to June 2020. Our approach is based on a deep neural network model that utilizes a variety of data characteristics, such as time-sensitive weather data, textual information, and discerning factors. We have tested ARIS against multiple baselines through a comprehensive series of experiments across several major cities of USA, and we have noticed significant improvement during inference especially in detecting accident classes. Additionally, to make our model edge-implementable we have compressed our model using a joint technique of magnitude-based weight pruning and model quantization. We have also demonstrated the inference results along with power consumption profiling after deploying the model on a resource constrained environment that consists of Intel Neural Compute Stick 2 (NCS2) with Raspberry Pi 4B (RPi4). Our investigation and observations indicate major improvements to predict unusual traffic accident event even after model compression and deployment. We have managed to reduce the model size and inference time by ≈ 6x, and ≈ 70 % respectively with insignificant drop in performance. Furthermore, to better understand the importance of each individual type of variables used in our analysis, we have showcased a comprehensive ablation study.