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An optimal control framework for reliable and sustainable bus operation

Research output: Contribution to journalArticlepeer-review

Abstract

Most existing studies on bus transport systems treat service reliability and operational sustainability as separate objectives. This paper presents an integrated simulation and optimal control framework that jointly addresses both. We develop a real-time, event-based simulation environment that captures human-driven bus behavior under realistic traffic conditions, passenger demand, and operational disturbances, calibrated using open-source data. Building on this platform, we design and implement two complementary control strategies within a Model Predictive Control (MPC) framework: holding control at stops and speed limit regulation. The controllers are jointly optimized to regulate time headways while minimizing energy consumption. The framework is evaluated on five high-frequency bus lines in the Tel Aviv metropolitan area. Simulation results show that the combined strategy reduces headway deviations by over 60% while achieving approximately 15% reductions in total energy consumption. These results demonstrate that jointly designed, minimally intrusive control strategies can simultaneously improve reliability and sustainability in urban bus operations.

Original languageEnglish
Article number105798
JournalTransportation Research Part C: Emerging Technologies
Volume192
DOIs
StatePublished - Nov 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  3. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • Energy efficiency
  • Model predictive control
  • Public transport operation
  • Service reliability

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Management Science and Operations Research

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