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

TL;DR

This paper presents a queuing-based traffic assignment method for optimizing the routing of autonomous leader-follower vehicles in work zones, aiming to minimize system costs caused by capacity drops and delays.

Contribution

It introduces a novel queuing-based time-dependent travel time function and a modified path algorithm for better traffic management of ATMA vehicles.

Findings

The method effectively models capacity drops and dynamic travel times.

Optimized routing reduces system costs and delays.

Sensitivity analysis shows impact varies with demand and capacity changes.

Abstract

The emerging technology of the Autonomous Truck Mounted Attenuator (ATMA), a leader-follower style vehicle system, utilizes connected and automated vehicle capabilities to enhance safety during transportation infrastructure maintenance in work zones. However, the speed difference between ATMA vehicles and general vehicles creates a moving bottleneck that reduces capacity and increases queue length, resulting in additional delays. The different routes taken by ATMA cause diverse patterns of time-varying capacity drops, which may affect the user equilibrium traffic assignment and lead to different system costs. This manuscript focuses on optimizing the routing for ATMA vehicles in a network to minimize the system cost associated with the slow-moving operation. To achieve this, a queuing-based traffic assignment approach is proposed to identify the system cost caused by the ATMA system. A queuing-based time-dependent (QBTD) travel time function, considering capacity drop, is introduced and applied in the static user equilibrium traffic assignment problem, with a result of adding dynamic characteristics. Subsequently, we formulate the queuing-based traffic assignment problem and solve it using a modified path-based algorithm. The methodology is validated using a small-size and a large-size network and compared with two benchmark models to analyze the benefit of capacity drop modeling and QBTD travel time function. Furthermore, the approach is applied to quantify the impact of different routes on the traffic system and identify an optimal route for ATMA vehicles performing maintenance work. Finally, sensitivity analysis is conducted to explore how the impact changes with variations in traffic demand and capacity reduction.