海报:安全路线:评估道路安全的框架

Reuben Vince Rabsatt, H. Kalantarian, M. Gerla
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引用次数: 2

摘要

导航系统通常会根据天气状况、交通信息和道路危险等因素向用户提示方向。通过考虑这些因素,系统可以建议一条最短旅行时间的路线。然而,这些导航系统没有包括任何有关不同路线相对安全性的有意义的信息。例如,有些道路比其他道路更容易发生事故,但很容易避免。通过选择其他路径,个人可以降低风险,同时通常只增加最少的通勤时间。我们根据加州高速公路性能测量系统(PEMS)数据库的数据开发了一个道路安全模型。我们的道路安全模型使用基于诸如一天中的时间、一周中的哪一天、平均速度、流量和其他交通特征等因素的历史事故数据来考虑风险。我们使用RandomForest分类器的实验结果显示,在识别高风险路线方面,分类准确率达到80%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Poster: SafeRoute a framework for assessment of road safety
Navigation systems typically suggest directions to the user based on factors such as weather conditions, traffic information, and road hazards. By considering these factors, the system can suggest a route in which travel time is minimized. However, these navigation systems fail to include any meaningful information about the relative safety of different routes. For example, some roads are significantly more accident prone than others and can easily be avoided. By taking alternative paths, individuals can reduce risk, while often adding only minimal time to their commutes. We develop a model for road safety based on data from the California Freeway Performance Measurement System (PEMS) database. Our model for road safety considers risk using historical accident data based on factors such as the time of day, day of week, average speed, flow, and other traffic features. Our experimental results using the RandomForest classifier show a classification accuracy of 80% for identifying high-risk routes.
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