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Predictive Lane-Change and Routing Coordination in Bus-Priority Mixed Traffic Corridors

Tanlu Liang, Ting Bai, Andreas A. Malikopoulos

TL;DR

A segment-based network coordination framework that jointly optimizes lane-change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority is developed.

Abstract

In this paper, we investigate the coordination of vehicle maneuvers in mixed-traffic corridors where connected and automated vehicles, human-driven vehicles, and buses interact under dedicated bus lane operations. We develop a segment-based network coordination framework that jointly optimizes lane-change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority. The proposed framework incorporates a predictive bus-protection mechanism that restricts vehicle access to protected lane segments within a monitoring horizon, together with a utility-driven lane-change strategy that accounts for anticipated travel time gains, downstream routing feasibility, and lane-change stability. By explicitly coupling network-level routing decisions with lane-level interaction control, the method proactively mitigates conflicts on dedicated lanes before congestion effects materialize. The proposed approach is evaluated through microscopic traffic simulations in SUMO using a realistic urban corridor. Simulation results demonstrate that the framework enhances bus schedule adherence and reduces average travel times for both automated and human-driven vehicles, while maintaining stable lane-change behavior without increasing maneuver frequency.

Predictive Lane-Change and Routing Coordination in Bus-Priority Mixed Traffic Corridors

TL;DR

A segment-based network coordination framework that jointly optimizes lane-change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority is developed.

Abstract

In this paper, we investigate the coordination of vehicle maneuvers in mixed-traffic corridors where connected and automated vehicles, human-driven vehicles, and buses interact under dedicated bus lane operations. We develop a segment-based network coordination framework that jointly optimizes lane-change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority. The proposed framework incorporates a predictive bus-protection mechanism that restricts vehicle access to protected lane segments within a monitoring horizon, together with a utility-driven lane-change strategy that accounts for anticipated travel time gains, downstream routing feasibility, and lane-change stability. By explicitly coupling network-level routing decisions with lane-level interaction control, the method proactively mitigates conflicts on dedicated lanes before congestion effects materialize. The proposed approach is evaluated through microscopic traffic simulations in SUMO using a realistic urban corridor. Simulation results demonstrate that the framework enhances bus schedule adherence and reduces average travel times for both automated and human-driven vehicles, while maintaining stable lane-change behavior without increasing maneuver frequency.
Paper Structure (13 sections, 28 equations, 9 figures, 2 tables)

This paper contains 13 sections, 28 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Segment Based Modeling
  4. Segment Predictive Flow Modeling
  5. Bus Predictive Protection Modeling
  6. Dynamic Lane-change Approach
  7. Bus Protection Constraints
  8. Lane-Change Control
  9. Rerouting Control
  10. Simulation
  11. Simulation Setup
  12. Results and Analysis
  13. Conclusions

Figures (9)

  • Figure 1: Illustration of the joint DL and lane-change behavior.
  • Figure 2: Segment-based monitoring and lane-change candidate selection within the control zone.
  • Figure 3: Predictive coupling of lane-change and routing decisions in the proposed control framework.
  • Figure 4: Study corridor on Van Ness Avenue with DL and bus stops.
  • Figure 5: The cumulative bus travel time over the time horizon.
  • ...and 4 more figures