VANET-Based Smart Navigation for Emergency Evacuation and Special Events

In this paper we propose, develop, and analyze the performance of a new system-optimum navigation model that utilizes Vehicular Ad-hoc Networks (VANETs), linear programming optimization, and stochastic routing to efficiently and smartly navigate vehicle crowds in case of an emergency evacuation or after special events. The objective of the proposed system is to clear the network in a shorter time by better utilizing the network resources while taking into consideration the road capacities. In this model, road links are weighted based on travel time. Road link capacities and current traffic conditions are used as constraints in the optimization problem. Vehicles are employed as sensors to compute travel times of the links and send this information to the Traffic Management Center (TMC) in real-time. The TMC periodically optimizes the traffic assignment. Subsequently, routes for vehicles are created/updated based on the latest optimized assignments. To test the model, a real network with calibrated traffic is used. The proposed model is compared to the Sub-population Feedback Dynamic Time-dependant Assignment (SFDTA) navigation. Moreover, we analyze its sensitivity to the re-optimization interval at different traffic demand levels. The results show that the proposed system decreases the network-wide travel time and is successful in clearing the network earlier especially in the case high vehicle traffic demands.