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Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban Networks

Panagiotis Typaldos, Andreas A. Malikopoulos

TL;DR

The paper tackles urban congestion by coupling upper-level predictive routing with lower-level robust trajectory planning for CAVs. It introduces a predictive routing layer that estimates the time to reach critical density on each edge, $t_c$, from real-time density and fundamental diagrams, and uses this to dynamically adjust edge weights for proactive rerouting. At the lower level, it extends robust intersection coordination to guarantee feasible, energy-efficient trajectories under uncertainty, ensuring safe crossing of signal-free intersections. Validation on SUMO using the Sioux Falls network demonstrates substantial improvements in total travel time, total delay, fuel consumption, and emissions, highlighting the practical impact of integrating predictive routing with robust trajectory planning for urban CAV deployment.

Abstract

In this paper, we present a hierarchical framework that integrates upper-level routing with low-level optimal trajectory planning for connected and automated vehicles (CAVs) traveling in an urban network. The upper-level controller efficiently distributes traffic flows by utilizing a dynamic re-routing algorithm that leverages real-time density information and the fundamental diagrams of each network edge. This re-routing approach predicts when each edge will reach critical density and proactively adjusts the routing algorithm's weights to prevent congestion before it occurs. The low-level controller coordinates CAVs as they cross signal-free intersections, generating optimal, fuel-efficient trajectories while ensuring safe passage by satisfying all relevant constraints. We formulate the problem as an optimal control problem and derive an analytical solution. Using the SUMO micro-simulation platform, we conduct simulation experiments on a realistic network. The results show that our hierarchical framework significantly enhances network performance compared to a baseline static routing approach. By dynamically re-routing vehicles, our approach successfully reduces total travel time and mitigates congestion before it develops.

Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban Networks

TL;DR

The paper tackles urban congestion by coupling upper-level predictive routing with lower-level robust trajectory planning for CAVs. It introduces a predictive routing layer that estimates the time to reach critical density on each edge, , from real-time density and fundamental diagrams, and uses this to dynamically adjust edge weights for proactive rerouting. At the lower level, it extends robust intersection coordination to guarantee feasible, energy-efficient trajectories under uncertainty, ensuring safe crossing of signal-free intersections. Validation on SUMO using the Sioux Falls network demonstrates substantial improvements in total travel time, total delay, fuel consumption, and emissions, highlighting the practical impact of integrating predictive routing with robust trajectory planning for urban CAV deployment.

Abstract

In this paper, we present a hierarchical framework that integrates upper-level routing with low-level optimal trajectory planning for connected and automated vehicles (CAVs) traveling in an urban network. The upper-level controller efficiently distributes traffic flows by utilizing a dynamic re-routing algorithm that leverages real-time density information and the fundamental diagrams of each network edge. This re-routing approach predicts when each edge will reach critical density and proactively adjusts the routing algorithm's weights to prevent congestion before it occurs. The low-level controller coordinates CAVs as they cross signal-free intersections, generating optimal, fuel-efficient trajectories while ensuring safe passage by satisfying all relevant constraints. We formulate the problem as an optimal control problem and derive an analytical solution. Using the SUMO micro-simulation platform, we conduct simulation experiments on a realistic network. The results show that our hierarchical framework significantly enhances network performance compared to a baseline static routing approach. By dynamically re-routing vehicles, our approach successfully reduces total travel time and mitigates congestion before it develops.

Paper Structure

This paper contains 14 sections, 3 theorems, 31 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Intersection Coordination
  4. Routing Planning
  5. Contributions
  6. Routing Problem
  7. Network Information and Fundamental Diagrams
  8. Re-routing
  9. Intersection Coordination Problem
  10. Deterministic Coordination Problem
  11. Robust Coordination Problem
  12. Limitations
  13. Simulation Results
  14. Concluding Remarks

Key Result

Proposition 1

At each edge of a network with density $k(t)$ and critical density $k_c$, there exists a time threshold $T_{thres}$ such that if $t_c-t_0 < T_{thres}$, the edge should be flagged for re-routing to avoid congestion.

Figures (6)

  • Figure 1: Example of triangular fundamental diagram.
  • Figure 2: Demonstration of the dynamic weight function for different values of parameter $\gamma$.
  • Figure 3: Coordination of connected and automated vehicles (CAVs) at an intersection. CAV $i$ enters at time $t_i^0$, passes the confict point at $t_i^c$, and exits at $t_i^f$.
  • Figure 4: Sioux Falls Network.
  • Figure 5: Comparison of density profiles evolution over time between baseline (blue lines) and proposed re-routing approach (red lines).
  • ...and 1 more figures

Theorems & Definitions (3)

  • Proposition 1
  • Proposition 2
  • Theorem 1