Abstract With the emergence of the internet of things, pathfinding problems have recently received a significant amount of attention. Various commercial applications provide automated routing by considering travel time, travel distance, fuel consumption, complexity of the road, etc. However, many of these prospective applications do not consider route safety. Emergence of high-resolution big data generated by connected vehicles (CV) helps us to integrate safety into routing problem. The goal of this study is to address safety aspects in pathfinding problems by developing a methodological framework that simultaneously considers safety and mobility. To reach this goal, the concept of volatility is utilized as a surrogate safety performance measure to quantify route safety and driver behavior. The proposed framework uses CV big data and real-time traffic data to calculate safety indices and travel times. Measured safety indices include 5-year crash history, route speed and acceleration volatility, and driver volatility. Travel time and safety shape a cost function called “route impedance.” The algorithm has the flexibility for the user to predefine the weight for safety consideration. It also uses driver volatility to automatically increase safety weight for volatile drivers. To illustrate the algorithm, a numerical example is provided using an origin-destination pair in Ann Arbor, MI, and more than 42 million CV observations from around 2,500 CVs from the Safety Pilot Model Deployment (SPMD) were analyzed. The sensitivity analysis is performed to discuss the impact of penetration rate of CVs and time of the trip on the results. Finally, this paper shows suggested routes for multiple scenarios to demonstrate the outcome of the study. The results revealed that the algorithm might suggest different routes when considering safety indices and not just travel time.

中文翻译：

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使用联网车辆生成的大数据集成安全性和移动性以进行寻路

摘要 随着物联网的出现，寻路问题最近受到了广泛的关注。各种商业应用通过考虑旅行时间、旅行距离、燃料消耗、道路复杂性等来提供自动路由。然而，许多这些潜在应用没有考虑路线安全。由联网车辆 (CV) 生成的高分辨率大数据的出现帮助我们将安全性整合到路线问题中。本研究的目标是通过开发同时考虑安全性和机动性的方法框架来解决寻路问题中的安全问题。为了实现这一目标，波动性的概念被用作替代安全绩效衡量标准，以量化路线安全和驾驶员行为。所提出的框架使用 CV 大数据和实时交通数据来计算安全指数和旅行时间。测量的安全指数包括 5 年的碰撞历史、路线速度和加速度波动性以及驾驶员波动性。旅行时间和安全形成了一个称为“路线阻抗”的成本函数。出于安全考虑，该算法可以让用户灵活地预先定义重量。它还使用驱动程序的波动性来自动增加波动性驱动程序的安全权重。为了说明该算法，我们使用密歇根州安娜堡的起点-目的地对提供了一个数值示例，并分析了来自安全试点模型部署 (SPMD) 的大约 2,500 个 CV 的超过 4,200 万个 CV 观测值。进行敏感性分析以讨论 CV 的渗透率和行程时间对结果的影响。最后，本文展示了多种场景的建议路线，以展示研究结果。结果表明，在考虑安全指数而不仅仅是旅行时间时，该算法可能会建议不同的路线。