title: Road Risk Modeling and Cloud-Aided Safety-Based Route Planning.
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 "best" route can be adjusted to favor time versus safety metrics.
authors.forename: Zhaojian
authors.initials: Z
authors.lastname: Li
authors.forename: Ilya
authors.initials: I
authors.lastname: Kolmanovsky
authors.forename: Ella
authors.initials: E
authors.lastname: Atkins
authors.forename: Jianbo
authors.initials: J
authors.lastname: Lu
authors.forename: Dimitar P
authors.initials: DP
authors.lastname: Filev
authors.forename: John
authors.initials: J
authors.lastname: Michelini