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TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate Traffic Congestion

Doris E. M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

TL;DR

This work tackles traffic congestion in networks where travelers probabilistically comply with SRP route recommendations, modeling the interaction as a probabilistic-compliance Stackelberg game. It proposes TASR, a greedy trust-aware routing algorithm, that first solves the optimal fully-compliant flow via a Beckmann convex program and then greedily assigns recommendations to partially compliant traveler groups in order of increasing path latency, weighted by trust. The latency on each edge follows the Bureau of Public Roads form $\tau(f_e)=t^{ff}_e\bigl(1+\lambda (f_e/c_e)^\beta\bigr)$, and trust dynamics are captured by an update rule that adjusts each group's trust $\alpha_i$ based on observed regret $B_i$. Empirical results on real road networks (Sioux Falls, Chicago Sketch, Sydney) show TASR reduces network congestion compared with LLF, Scale, Aloof, and ASCALE while maintaining favorable trust evolution, highlighting its practical impact for smart transportation systems in multi-commodity settings.

Abstract

Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic networks by proposing optimal route recommendations to selfish travelers. Traditionally, Stackelberg routing is cast as a partial control problem where a fraction of traveler flow complies with route recommendations, while the remaining respond as selfish travelers. In this paper, a novel Stackelberg routing framework is formulated where the agents exhibit \emph{probabilistic compliance} by accepting SRP's route recommendations with a \emph{trust} probability. A greedy \emph{\textbf{T}rust-\textbf{A}ware \textbf{S}tackelberg \textbf{R}outing} algorithm (in short, TASR) is proposed for SRP to compute unique path recommendations to each traveler flow with a unique demand. Simulation experiments are designed with random travel demands with diverse trust values on real road networks such as Sioux Falls, Chicago Sketch, and Sydney networks for both single-commodity and multi-commodity flows. The performance of TASR is compared with state-of-the-art Stackelberg routing methods in terms of traffic congestion and trust dynamics over repeated interaction between the SRP and the travelers. Results show that TASR improves network congestion without causing a significant reduction in trust towards the SRP, when compared to most well-known Stackelberg routing strategies.

TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate Traffic Congestion

TL;DR

This work tackles traffic congestion in networks where travelers probabilistically comply with SRP route recommendations, modeling the interaction as a probabilistic-compliance Stackelberg game. It proposes TASR, a greedy trust-aware routing algorithm, that first solves the optimal fully-compliant flow via a Beckmann convex program and then greedily assigns recommendations to partially compliant traveler groups in order of increasing path latency, weighted by trust. The latency on each edge follows the Bureau of Public Roads form , and trust dynamics are captured by an update rule that adjusts each group's trust based on observed regret . Empirical results on real road networks (Sioux Falls, Chicago Sketch, Sydney) show TASR reduces network congestion compared with LLF, Scale, Aloof, and ASCALE while maintaining favorable trust evolution, highlighting its practical impact for smart transportation systems in multi-commodity settings.

Abstract

Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic networks by proposing optimal route recommendations to selfish travelers. Traditionally, Stackelberg routing is cast as a partial control problem where a fraction of traveler flow complies with route recommendations, while the remaining respond as selfish travelers. In this paper, a novel Stackelberg routing framework is formulated where the agents exhibit \emph{probabilistic compliance} by accepting SRP's route recommendations with a \emph{trust} probability. A greedy \emph{\textbf{T}rust-\textbf{A}ware \textbf{S}tackelberg \textbf{R}outing} algorithm (in short, TASR) is proposed for SRP to compute unique path recommendations to each traveler flow with a unique demand. Simulation experiments are designed with random travel demands with diverse trust values on real road networks such as Sioux Falls, Chicago Sketch, and Sydney networks for both single-commodity and multi-commodity flows. The performance of TASR is compared with state-of-the-art Stackelberg routing methods in terms of traffic congestion and trust dynamics over repeated interaction between the SRP and the travelers. Results show that TASR improves network congestion without causing a significant reduction in trust towards the SRP, when compared to most well-known Stackelberg routing strategies.
Paper Structure (19 sections, 2 theorems, 9 equations, 4 figures, 4 tables, 2 algorithms)

This paper contains 19 sections, 2 theorems, 9 equations, 4 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work on Stackelberg Routing
  3. Novel Contributions
  4. System Model and Problem Formulation
  5. System Model
  6. Problem Formulation
  7. Proposed Algorithm
  8. Approximate Best-Response Path-Profile Computation
  9. TASR Properties
  10. Trust Dynamics
  11. Evaluation Methodology and Datasets
  12. Performance Metrics
  13. Experiments, Datasets, and Preprocessing
  14. Single-Commodity Networks
  15. Multi-Commodity Network
  16. ...and 4 more sections

Key Result

Theorem 1

The optimal path-profile recommendation of the system is a path-profile recommendation $\boldsymbol{P}_s^*$ such that

Figures (4)

  • Figure 1: Smart Transportation System Model.
  • Figure 2: Comparison of Starting Trust to Final Trust for each Algorithm on the Single-Commodity Sioux Falls Network, Averaged over 500 Independent Iterations with $\varepsilon = 0.25$.
  • Figure 3: Comparison of Trust Dynamics over 50 Repeated Interactions on the Single-Commodity Sioux Falls Network with $\Delta = 10$ (Top) and $\Delta = 20$ (Bottom), when Trust is Initialized at 0.5 for All Agents with $\varepsilon = 0.25$.
  • Figure 4: Comparison of Congestion for $\Delta = 5$ and $\Delta = 10$ on the Single-Commodity Sioux Falls and Chicago Sketch Networks.

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Corollary 1.1
  • proof