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Routing in Mixed Transportation Systems for Mobility Equity

Heeseung Bang, Aditya Dave, Andreas A. Malikopoulos

TL;DR

The paper tackles mobility equity in mixed-traffic networks by introducing a routing framework that handles compliant CPVs, NPVs modeled via cognitive hierarchy, and a mobility equity metric (MEM) to guide decisions. It combines a system-centric routing problem with a hierarchical psychology model for NPVs and integrates MEM into the objective through mode-weight optimization, addressing convergence challenges with a convergence-inducing constraint. Key contributions include the MEM definition, its integration into a MEM-driven routing optimization, and demonstrations on a small network showing MEM benefits under higher compliance and equity-focused weighting. The work offers a principled approach to balance efficiency and equity in emerging mobility systems and informs policy design through mode-weight selection and potential incentives to improve MEM.

Abstract

This letter proposes a routing framework in mixed transportation systems for improving mobility equity. We present a strategic routing game that governs interactions between compliant and noncompliant vehicles, where noncompliant vehicles are modeled with cognitive hierarchy theory. Then, we introduce a mobility equity metric (MEM) to quantify the accessibility and fairness in the transportation network. We integrate the MEM into the routing framework to optimize it with adjustable weights for different transportation modes. The proposed approach bridges the gap between technological advancements and societal goals in mixed transportation systems to enhance efficiency and equity. We provide numerical examples and analysis of the results.

Routing in Mixed Transportation Systems for Mobility Equity

TL;DR

The paper tackles mobility equity in mixed-traffic networks by introducing a routing framework that handles compliant CPVs, NPVs modeled via cognitive hierarchy, and a mobility equity metric (MEM) to guide decisions. It combines a system-centric routing problem with a hierarchical psychology model for NPVs and integrates MEM into the objective through mode-weight optimization, addressing convergence challenges with a convergence-inducing constraint. Key contributions include the MEM definition, its integration into a MEM-driven routing optimization, and demonstrations on a small network showing MEM benefits under higher compliance and equity-focused weighting. The work offers a principled approach to balance efficiency and equity in emerging mobility systems and informs policy design through mode-weight selection and potential incentives to improve MEM.

Abstract

This letter proposes a routing framework in mixed transportation systems for improving mobility equity. We present a strategic routing game that governs interactions between compliant and noncompliant vehicles, where noncompliant vehicles are modeled with cognitive hierarchy theory. Then, we introduce a mobility equity metric (MEM) to quantify the accessibility and fairness in the transportation network. We integrate the MEM into the routing framework to optimize it with adjustable weights for different transportation modes. The proposed approach bridges the gap between technological advancements and societal goals in mixed transportation systems to enhance efficiency and equity. We provide numerical examples and analysis of the results.
Paper Structure (9 sections, 8 equations, 5 figures)

This paper contains 9 sections, 8 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Routing in Mixed Transportation
  3. Mobility Equity Metric
  4. Mobility Equity Optimization
  5. Inducing Convergence
  6. Numerical Implementation
  7. Continuous Approximation of Mobility Equity Metric
  8. Numerical Simulation Results
  9. Concluding Remarks

Figures (5)

  • Figure 1: Conceptual diagram of cognitive hierarchy model.
  • Figure 2: Structure of the socially-optimal routing problem.
  • Figure 3: Small network for simulation with two origins and five destinations.
  • Figure 4: Travel time of each demand and transportation mode for different variables.
  • Figure 5: Mobility equity metric and time difference for different noncompliance rate.

Theorems & Definitions (6)

  • Remark 1
  • Remark 2
  • Definition 1
  • Remark 3
  • Remark 4
  • Remark 5