TY  - JOUR
PY  - 2015//
TI  - Road Risk Modeling and Cloud-Aided Safety-Based Route Planning
JO  - IEEE transactions on cybernetics
A1  - Li, Zhaojian
A1  - Kolmanovsky, Ilya
A1  - Atkins, Ella
A1  - Lu, Jianbo
A1  - Filev, Dimitar P.
A1  - Michelini, John
SP  - 2473
EP  - 2483
VL  - 46
IS  - 11
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2168-2267
UR  - http://dx.doi.org/10.1109/TCYB.2015.2478698
ID  - ref1
ER  -