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The emerging technology of the Autonomous Truck Mounted Attenuator (ATMA), aleader-follower style vehicle system, utilizes connected and automated vehiclecapabilities to enhance safety during transportation infrastructure maintenancein work zones. However, the speed difference between ATMA vehicles and generalvehicles creates a moving bottleneck that reduces capacity and increases queuelength, resulting in additional delays. The different routes taken by ATMAcause diverse patterns of time-varying capacity drops, which may affect theuser equilibrium traffic assignment and lead to different system costs. Thismanuscript focuses on optimizing the routing for ATMA vehicles in a network tominimize the system cost associated with the slow-moving operation.  To achieve this, a queuing-based traffic assignment approach is proposed toidentify the system cost caused by the ATMA system. A queuing-basedtime-dependent (QBTD) travel time function, considering capacity drop, isintroduced and applied in the static user equilibrium traffic assignmentproblem, with a result of adding dynamic characteristics. Subsequently, weformulate the queuing-based traffic assignment problem and solve it using amodified path-based algorithm. The methodology is validated using a small-sizeand a large-size network and compared with two benchmark models to analyze thebenefit of capacity drop modeling and QBTD travel time function. Furthermore,the approach is applied to quantify the impact of different routes on thetraffic system and identify an optimal route for ATMA vehicles performingmaintenance work. Finally, sensitivity analysis is conducted to explore how theimpact changes with variations in traffic demand and capacity reduction.

Deployment of Leader-Follower Automated Vehicle Systems for Smart Work Zone Applications with a Queuing-based Traffic Assignment Approach