Managing connected and automated vehicles with flexible routing at "lane-allocation-free'' intersections

Abstract: Trajectory planning and coordination for connected and automated vehicles (CAVs) have been studied at isolated signal-free'' intersections and insignal-free'' corridors under the fully CAV environment in the literature. Most of the existing studies are based on the definition of approaching and exit lanes. The route a vehicle takes to pass through an intersection is determined from its movement. That is, only the origin and destination arms are included. This study proposes a mixed-integer linear programming (MILP) model to optimize vehicle trajectories at an isolated signal-free'' intersection without lane allocation, which is denoted aslane-allocation-free'' (LAF) control. Each lane can be used as both approaching and exit lanes for all vehicle movements including left-turn, through, and right-turn. A vehicle can take a flexible route by way of multiple arms to pass through the intersection. In this way, the spatial-temporal resources are expected to be fully utilized. The interactions between vehicle trajectories are modeled explicitly at the microscopic level. Vehicle routes and trajectories (i.e., car-following and lane-changing behaviors) at the intersection are optimized in one unified framework for system optimality in terms of total vehicle delay. Considering varying traffic conditions, the planning horizon is adaptively adjusted in the implementation procedure of the proposed model to make a balance between solution feasibility and computational burden. Numerical studies validate the advantages of the proposed LAF control in terms of both vehicle delay and throughput with different demand structures and temporal safety gaps.

• The paper introduces a MILP model for optimizing vehicle trajectories in signal-free intersections with lane-allocation-free control.
• Numerical studies show a reduction in vehicle delay by over 90% and significant improvements in traffic throughput compared to traditional methods.
• The approach adapts to varying traffic conditions using dynamic planning horizons, paving the way for future studies on multi-intersection coordination.

Managing Connected and Automated Vehicles with Flexible Routing at Intersections

This paper presents a novel approach for managing connected and automated vehicles (CAVs) at isolated intersections. The authors propose a mixed-integer linear programming (MILP) model that optimizes vehicle trajectories in a signal-free environment without traditional lane allocation constraints—termed "lane-allocation-free" (LAF) control.

Introduction and Background

Traditionally, intersections serve as bottlenecks in urban traffic networks, necessitating effective management to mitigate congestion, ensure safety, and minimize environmental impacts. Conventional methods, such as fixed-time and adaptive traffic signals, are continually being augmented by advancements in CAV technology, enabling a shift toward optimized vehicle trajectory control without reliance on physical traffic signals.

The concept of signal-free intersections leverages CAV capabilities, allowing vehicles to communicate with infrastructure and one another (V2V and V2I communication), thereby optimizing trajectory planning and movement coordination in both spatial and temporal dimensions.

MILP Model and Intersection Control

The paper introduces an MILP framework that optimizes the routes and trajectories of CAVs at isolated intersections marked by LAF control. This control scheme does not designate specific lanes as approaching or exit lanes, permitting flexible routing options where vehicles may pass through multiple arms to better utilize spatial-temporal resources. Vehicle interactions are explicitly modeled at the microscopic level, incorporating car-following and lane-changing behavior into a unified optimization framework.

The MILP model adapts to varying traffic conditions by adjusting the planning horizon, balancing between solution feasibility and computational efficiency. Numerical studies indicate significant reductions in vehicle delay and improvements in throughput compared to traditional control methods.

Numerical Results and Analysis

Simulations demonstrate that LAF control reduces average vehicle delay by over 90% compared to conventional vehicle-actuated control and outperforms previous ALAF control implementations. Through dynamic vehicle routing and optimized lane usage, the LAF model achieves superior capacity utilization and minimizes congestion.

The sensitivity analysis further explores the model’s robustness across different demand structures and confirms the LAF control's efficacy in environments characterized by imbalanced and high-demand traffic conditions.

Theoretical and Practical Implications

The introduction of lane-allocation-free control for CAVs revolutionizes intersection management by challenging traditional lane divisions and promoting the optimization of urban transport networks. The flexible routing allowed by LAF control has the potential to significantly enhance traffic throughput and vehicle operating efficiency, thus contributing substantially to urban traffic planning solutions.

This research lays groundwork for further exploration into more complex network scenarios, potentially incorporating mixed traffic environments with varying levels of vehicle connectivity. It paves the way for subsequent studies on multi-intersection coordination and the application of more sophisticated vehicle dynamics models.

Conclusion

The paper ultimately demonstrates that lane-allocation-free control facilitated by flexible routing possesses inherent benefits for optimizing CAV trajectories at intersections, significantly enhancing both delay performance and throughput. This approach presents a viable alternative to conventional intersection control methods, promising advancements in traffic management and urban planning in a fully CAV-integrated future. Future research should explore extensions to corridors and networks and consider the implications of mixed vehicle environments to further enhance urban mobility solutions.