Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study
Clemson University（克莱姆森大学）；Northern Arizona University（北亚利桑那大学）；The University of Arizona（亚利桑那大学）；Arizona Department of Transportation（亚利桑那州交通局）；Exponent Co（Exponent公司）
Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, Alonso Granados Baca
Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safety features, autonomous driving systems, connected vehicles, high-throughput computing, and edge computing servers. Particularly, deep learning (DL) methods empowered volume video processing to extract safety-related features from massive videos captured by roadside units (RSU). Safety metrics are commonly used measures to investigate crashes and near-conflict events. However, these metrics provide limited insight into the overall network-level traffic management. On the other hand, some safety assessment efforts are devoted to processing crash reports and identifying spatial and temporal patterns of crashes that correlate with road geometry, traffic volume, and weather conditions. This approach relies merely on crash reports and ignores the rich information of traffic videos that can help identify the role of safety violations in crashes. To bridge these two perspectives, we define a new set of network-level safety metrics (NSM) to assess the overall safety profile of traffic flow by processing imagery taken by RSU cameras. Our analysis suggests that NSMs show significant statistical associations with crash rates. This approach is different than simply generalizing the results of individual crash analyses, since all vehicles contribute to calculating NSMs, not only the ones involved in crash incidents. This perspective considers the traffic flow as a complex dynamic system where actions of some nodes can propagate through the network and influence the crash risk for other nodes. We also provide a comprehensive review of surrogate safety metrics (SSM) in the Appendix A.
TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟（BRTA））；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；National Technical University of Athens (NTUA)（雅典国立技术大学（NTUA））
In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activity reported in this long-standing research field has been lately dominated by different Deep Learning approaches, yielding overly complex forecasting models that in general achieve accuracy gains of questionable practical utility. In this work we elaborate on the performance of Deep Echo State Networks for this particular task. The efficient learning algorithm and simpler parametric configuration of these alternative …
Clemson University（克莱姆森大学）；University of Arizona（亚利桑那大学）；Northern Arizona University（北亚利桑那大学）；Arizona State University（亚利桑那州立大学）
This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, …
University of Chicago（芝加哥大学）；Colorado State University（科罗拉多州立大学）；University of California, Berkeley（加州大学伯克利分校）；University of the Witwatersrand（威特沃特斯兰德大学）；Pulse Lab Jakarta（雅加达脉搏实验室）；Jakarta Smart City（雅加达智慧城市）
This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to be used for traffic analysis. By analyzing these patterns, the city of Jakarta will better understand how human behavior and built infrastructure contribute to traffic challenges and safety risks. The results of this work should also be broadly applicable to smart city initiative…
University of North Carolina at Greensboro（北卡罗来纳大学格林斯伯勒分校）
The variability of the clusters generated by clustering techniques in the domain of latitude and longitude variables of fatal crash data are significantly unpredictable. This unpredictability, caused by the randomness of fatal crash incidents, reduces the accuracy of crash frequency (i.e., counts of fatal crashes per cluster) which is used to measure traffic safety in practice. In this paper, a quantitative measure of traffic safety that is not significantly affected by the aforementioned variability is proposed. It introduces a fatal point -- a segment with the highest frequency of fatality -…
In the absence of pedestrian crossing lights, finding a safe moment to cross the road is often hazardous and challenging, especially for people with visual impairments. We present a reliable low-cost solution, an Android device attached to a traffic sign or lighting pole near the crossing, indicating whether it is safe to cross the road. The indication can be by sound, display, vibration, and various communication modalities provided by the Android device. The integral system camera is aimed at approaching traffic. Optical flow is computed from the incoming video stream, and projected onto an …
Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
Technical University of Munich（慕尼黑技术大学）；Fraunhofer Institute for Transportation and Infrastructure Systems（交通与基础设施系统弗劳恩霍夫研究所）；SETLabs Research GmbH（SETLabs研究公司）；University of California Merced（加州大学默塞德分校）
University of Kentucky（肯塔基大学）；Kentucky Transportation Center（肯塔基交通中心）
This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadway features in street-level panoramas, a slow and costly process. We propose to automate this process using a deep convolutional neural network that directly estimates the star rating from a street-level panorama, requiring milliseconds per image at test time. Our network also est…
Self-supervised large-scale kidney abnormality detection in drug safety assessment studies
Department of Pathology, Radboud University Medical Center（拉德堡德大学医学中心病理科部）；Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València（人类中心技术大学研究机构，巴塞罗那理工大学）
