Optimizing Maintenance Routes for Highway Infrastructure Using Leader–Follower Autonomous Vehicles

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is proposed to quantify the system cost incurred by ATMA on a given route. A queuing-based time-dependent (QBTD) travel time function is introduced to incorporate capacity drop into the static user equilibrium traffic assignment problem (TAP), thereby introducing dynamic characteristics. A modified path-based algorithm is developed to solve the resulting queuing-based TAP. The method is validated on both small and large networks and compared against two benchmark models to assess the benefits of capacity drop modeling and the QBTD travel time function. It is further applied to evaluate the impact of different routes on the network and to identify an optimal ATMA route during maintenance operations. Finally, the sensitivity analysis explores the effects of varying traffic demand and capacity reduction.