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Harsh brakes at potholes in Nairobi: Context-based driver behavior in developing cities

机译:内罗毕的坑洼严峻:发展中城市基于情境的驾驶员行为

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Driving in developing cities presents numerous challenges. Traffic congestion and traffic accidents are the most visible challenges which are caused by different underlying factors. Two chief factors are poorly planned and maintained roadway infrastructure and the decisions made by the drivers. Drivers are constantly forced to negotiate road hazards, like potholes, unlabeled speed bumps as well as moving obstacles, like pushcarts, motorcycles, and animals. Current Usage Based Insurance (UBI) models do not include the context which in many cities may be paramount to understanding driver behavior. This article presents the Context-based Driver Score (CDS) model as a unified model for scoring a driver based on a unique formulation of context that includes road quality. We demonstrate the CDS model on a real-world use case in Nairobi, Kenya, where waste-collection trucks were instrumented with smartphones in order to collect inertial and telematic data. We present an analysis of the CDS model and driver behaviors in contexts that include weather, time-of-day, and road quality. Our results show that the distribution of driving behaviors, like harsh braking and swerving, vary greatly based on the context and the definition of the CDS model. Ultimately, this work aims to extend the utility and scalability of UBI models in order to make them more suitable for deployment in developing cities.
机译:在发展中城市的驾驶面临着众多挑战。交通拥堵和交通事故是由不同的潜在因素引起的最明显的挑战。两个主要因素是规划和维护不好的道路基础设施以及驾驶员做出的决定。驾驶员不断被迫要面对道路危险,例如坑洼,未贴标签的减速带以及移动障碍物,例如手推车,摩托车和动物。基于当前使用情况的保险(UBI)模型不包括在许多城市中可能对于理解驾驶员行为至关重要的背景。本文介绍了基于上下文的驾驶员评分(CDS)模型,该模型是基于包括道路质量在内的独特上下文公式对驾驶员进行评分的统一模型。我们在肯尼亚内罗毕的一个实际案例中演示了CDS模型,该案例中,废物收集卡车装有智能手机,以收集惯性和远程信息处理数据。我们将在包括天气,时间和道路质量在内的环境中对CDS模型和驾驶员行为进行分析。我们的结果表明,根据CDS模型的上下文和定义,驾驶行为的分布(如严厉的制动和转弯)会发生很大变化。最终,这项工作旨在扩展UBI模型的实用性和可扩展性,以使其更适合在发展中城市中部署。

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