A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems
- URL: http://arxiv.org/abs/2606.08849v1
- Date: Sun, 07 Jun 2026 21:42:05 GMT
- Title: A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems
- Abstract summary: This paper proposes a time-indexed framework to assess the resilience of disruption response solutions in urban transit systems.<n>The framework is implemented for the RER B transit line in the Ile-de-France (Paris) network.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Urban public transport disruptions require rapid response strategies, yet existing studies rarely provide a decision support framework to compare alternative disruption response solutions using a common set of dynamic, passenger, operator, and environment oriented indicators. This paper proposes a KPI-driven, time-indexed framework to assess the resilience of disruption response solutions in urban transit systems. The framework combines an optimization model with a behavioral evaluation in agent-based simulation. It also underlays the secondary service degradation induced on helper lines when in-service vehicles are withdrawn to support the disrupted corridor. Rather than treating resilience as a single score, it evaluates complementary dimensions including vulnerability, adaptability, robustness, resilience loss, responsiveness, cost-based performance, emissions, and equity. The framework is implemented for the RER B transit line in the Ile-de-France (Paris) network. Results show that the coordinated strategy provides the most balanced resilience profile, combining high service continuity with lower total disruption cost than single mode alternatives, while also improving equity and maintaining competitive environmental performance. Sensitivity analysis further identifies the disruption conditions under which coordinated multimodal response is most valuable.
Related papers
- Resilience Matters for Embodied Agents System: New Metrics, Systematic Evaluation, and Optimization [30.854408807200972]
Embodied Agents System (EAS) are increasingly deployed in open-world physical domains.<n>Existing evaluations rely on outcome-centric metrics as success rate or safety scores.<n>We propose a novel resilience evaluation framework that can be flexibly applied to any EAS.
arXiv Detail & Related papers (2026-08-24T21:26:11Z) - Optimal Reward Shaping: Autonomous Car Parking Case Study [1.1458853556386799]
We present a parameterized reward shaping framework featuring alignment feedback, drive-direction switch regularization, and an aligned episode termination mechanism evaluated on an autonomous parallel parking task.<n>By employing surrogate-based Bayesian optimization, our co-optimized Deep Q-Network (DQN) agent resolves characteristic control failure modes, significantly outperforming uncalibrated baselines across both success rate and trajectory smoothness.
arXiv Detail & Related papers (2026-07-26T11:49:23Z) - AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities [55.23913617185508]
Agent is an open-source, lightweight, and infrastructure for evaluating Large Language Models (LLMs)-based agents.<n>It organizes the evaluation process around three independent components, namely Benchmark, Harness and Environment.<n>It features a fault-tolerant runtime and comprehensive trajectory analysis tools to transparently diagnose failure modes like reward-hacking.
arXiv Detail & Related papers (2026-07-15T11:11:17Z) - ALSO: Adversarial Online Strategy Optimization for Social Agents [63.84788516416339]
We propose textbfALSO (textbfAdrial ontextbfLine textbfStrategy textbfOptimization) as the first framework for online strategy optimization in social simulation.<n> ALSO formulates multi-turn interaction as an adversarial bandit problem, where combinations of static personas and dynamic strategy instructions are treated as arms.<n>Also introduces a lightweight neural surrogate to predict rewards from interaction histories, enabling sample-efficient exploration and continuous online adaptation.
arXiv Detail & Related papers (2026-05-15T09:25:15Z) - DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
arXiv Detail & Related papers (2026-03-16T02:02:38Z) - Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance [86.46794021499511]
We show a previously underexplored gap between strategy usage and strategy executability.<n>We propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability.<n> SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance.
arXiv Detail & Related papers (2026-02-26T03:34:23Z) - Exploring Dissatisfaction in Bus Route Reduction through LLM-Calibrated Agent-Based Modeling [0.0]
This study employs an agent-based modelling (ABM) approach calibrated through a large language model (LLM)<n>Using IC-card data from Beijing's Huairou District, the LLM-calibrated ABM estimated passenger sensitivity parameters related to travel time, waiting, transfers, and crowding.<n>Results show that the structural configuration of the bus network exerts a stronger influence on system stability than capacity or operational factors.
arXiv Detail & Related papers (2025-10-30T05:59:48Z) - Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study [4.731967623788092]
Reinforcement learning-based traffic signal control (RL-TSC) has emerged as a promising approach for improving urban mobility.<n>In this study, we introduce T-REX, an open-source, SUMO-based simulation framework for training and evaluating RL-TSC methods under dynamic, incident scenarios.
arXiv Detail & Related papers (2025-06-16T08:15:29Z) - Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
arXiv Detail & Related papers (2025-04-21T04:56:47Z) - Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification [80.83325513157637]
Few-Shot Remote Sensing Scene Classification (FS-RSSC) presents the challenge of classifying remote sensing images with limited labeled samples.<n>We propose a novel Optimal Transport Adapter Tuning (OTAT) framework aimed at constructing an ideal Platonic representational space.
arXiv Detail & Related papers (2025-03-19T07:04:24Z) - A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks [0.0]
This study aims to explore the management of public transport disruptions through resilience as a service strategies.
It develops an optimization model to effectively allocate resources and minimize the cost for operators and passengers.
The proposed model is applied to a case study in the Ile de France region, Paris and suburbs.
arXiv Detail & Related papers (2024-08-30T12:22:34Z) - Towards More Efficient Shared Autonomous Mobility: A Learning-Based
Fleet Repositioning Approach [0.0]
This paper formulates SAMS fleet as a Markov Decision Process and presents a reinforcement learning-based repositioning (RLR) approach called integrated system-agent repositioning (ISR)
The ISR learns to respond to evolving demand patterns without explicit demand forecasting and to cooperate with optimization-based passenger-to-vehicle assignment.
Results demonstrate the RLR approaches' substantial reductions in passenger wait times, over 50%, relative to the JO approach.
arXiv Detail & Related papers (2022-10-16T23:30:46Z) - Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement
Learning [11.168121941015013]
Bus bunching is a common phenomenon that undermines the reliability and efficiency of bus services.
We formulate route-level bus fleet control as an asynchronous multi-agent reinforcement learning problem.
We extend the classical actor-critic architecture to handle the asynchronous issue.
arXiv Detail & Related papers (2021-05-02T02:08:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.