a nonlinear mathematical model for autonomous vehicle routing problem by considering traffic congestion for each route

Emerging technologies, such as vehicle-to-road infrastructure connectivity via wireless telecommunications systems, in addition to reducing the role of humans in driving activities, can meaningfully improve road performance compared to traditional traffic control systems. today, automated vehicles (avs), as an emerging player in modern urban transportation, would significantly influence customer satisfaction. for avs, the optimal routes must be found by a decision support system. this problem becomes more challenging when a suitable route is concurrently chosen by the majority of vehicles and network congestion occurs. in this research, a mathematical model for seeking the optimal route and scheduling of avs by considering road traffic is presented. gams software is used for solving and analyzing the mathematical model. the results for a sample example show that the optimal routes are successfully obtained for the avs. sensitivity analysis reveals that as traffic time increases, so do the cost and service time. this model calls for government agencies to allocate portions of road networks to avs to regulate vehicle movement and thus increase the output and performance of the network.