Road Risk Modeling and Cloud-Aided Safety-Based Route Planning

作者：Li Zhaojian^*; Kolmanovsky Ilya; Atkins Ella; Lu Jianbo; Filev Dimitar P; Michelini John

来源：IEEE Transactions on Cybernetics, 2016, 46(11): 2473-2483.

DOI：10.1109/TCYB.2015.2478698

• Data mining
• mixed-integer programming (MIP)
• neural network modeling
• road risk index (RRI)
• route planning

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 "best" route can be adjusted to favor time versus safety metrics.

• 出版日期2016-11