@article{ref1,
title="Road Risk Modeling and Cloud-Aided Safety-Based Route Planning",
journal="IEEE transactions on cybernetics",
year="2015",
author="Li, Zhaojian and Kolmanovsky, Ilya and Atkins, Ella and Lu, Jianbo and Filev, Dimitar P. and Michelini, John",
volume="46",
number="11",
pages="2473-2483",
abstract="This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the &quot;best&quot; route can be adjusted to favor time versus safety metrics.<p /> <p>Language: en</p>",
language="en",
issn="2168-2267",
doi="10.1109/TCYB.2015.2478698",
url="http://dx.doi.org/10.1109/TCYB.2015.2478698"
}