Model-based predictive traffic control for intelligent vehicles: Dynamic speed limits and dynamic lane allocation

Baskar, Lakshmi Dhevi; De Schutter, Bart; Hellendoorn, Hans · 2008 · OpenAlex-citations

DOI: 10.1109/ivs.2008.4621307

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Summary

This paper addresses the challenge of traffic congestion in automated highway systems by proposing a Model-Based Predictive Control (MPC) framework for intelligent vehicles. The authors aim to improve traffic flow performance by coordinating automated vehicles organized into platoons with roadside infrastructure controllers. The motivation stems from the potential of next-generation Intelligent Transportation Systems (ITS) to shift driving tasks to automated controllers, thereby enabling tighter vehicle spacing and higher network throughput. Specifically, the study focuses on optimizing dynamic speed limits and lane allocations to minimize the total time vehicles spend in the network. The methodology employs a hierarchical control framework that distributes intelligence between roadside infrastructure and vehicles. At the roadside controller level, an MPC strategy determines optimal control actions—specifically dynamic speed limits and lane change commands for platoon leaders—by solving a mixed-integer optimization problem over a receding horizon. The prediction model uses aggregate macroscopic representations where platoons are treated as single entities. The optimization objective minimizes the total time spent in the network, subject to constraints such as minimum separation distances, maximum speeds, and penalties for frequent control signal changes. To solve the resulting nonlinear, nonconvex optimization problems online, the authors utilize Sequential Quadratic Programming for continuous variables and an extended DIRECT algorithm for mixed-integer variables. The proposed approach was evaluated through a simulation case study involving a 10 km, two-lane highway segment with a temporary incident causing a capacity drop. The study compared three scenarios: uncontrolled human-driven traffic, controlled human-driven traffic with Intelligent Speed Adaptation (ISA), and fully automated platoon-based traffic using the proposed MPC. The simulation ran for 30 minutes with a control sampling time of one minute. Results indicated that the uncontrolled case yielded a total time spent (TTS) of 1465 vehicle-minutes. The ISA-controlled human driving scenario reduced TTS to 1367 vehicle-minutes, representing a 6.69% improvement. The MPC-controlled platoon scenario achieved the best performance, reducing TTS to 1320 vehicle-minutes, which corresponds to a 9.90% improvement over the uncontrolled baseline. The significance of this work lies in demonstrating the effectiveness of integrating MPC with platoon-based automated driving to mitigate traffic congestion and capacity drops. The findings suggest that coordinating dynamic speed limits and lane allocations at the roadside controller level can significantly enhance network efficiency compared to conventional control measures or uncontrolled traffic. The study highlights the potential of hierarchical control architectures to manage large-scale traffic networks by leveraging the precision and coordination capabilities of automated vehicles, offering a scalable solution for future intelligent transportation systems.

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• speed management