Strategizing Equitable Transit Evacuations: A Data-Driven Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2412.05777v1
• Date: Sun, 08 Dec 2024 02:17:38 GMT
• Title: Strategizing Equitable Transit Evacuations: A Data-Driven Reinforcement Learning Approach
• Authors: Fang Tang, Han Wang, Maria Laura Delle Monache,
• Abstract summary: This paper proposes a data-driven, reinforcement learning-based framework to optimize bus-based evacuations. We model the evacuation problem as a Markov Decision Process solved by reinforcement learning. We show that the proposed framework achieves significant improvements in both evacuation efficiency and equitable service distribution.
• Score: 5.962540020947193
• License:
• Abstract: As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning-based framework to optimize bus-based evacuations with an emphasis on improving both efficiency and equity. We model the evacuation problem as a Markov Decision Process solved by reinforcement learning, using real-time transit data from General Transit Feed Specification and transportation networks extracted from OpenStreetMap. The reinforcement learning agent dynamically reroutes buses from their scheduled location to minimize total passengers' evacuation time while prioritizing equity-priority communities. Simulations on the San Francisco Bay Area transportation network indicate that the proposed framework achieves significant improvements in both evacuation efficiency and equitable service distribution compared to traditional rule-based and random strategies. These results highlight the potential of reinforcement learning to enhance system performance and urban resilience during emergency evacuations, offering a scalable solution for real-world applications in intelligent transportation systems.

Related papers

• Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport [45.793758222754036]
  Diffusion policies have shown promise in learning complex behaviors from demonstrations. This paper explores improving diffusion-based imitation learning models through online interactions with the environment. We propose OTPR, a novel method that integrates diffusion policies with RL using optimal transport theory.
  arXiv  Detail & Related papers  (2025-02-18T08:22:20Z)
• 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)
• Forecasting and Mitigating Disruptions in Public Bus Transit Services [7.948662269574215]
  Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the randomness of disruptions and due to the nature of selecting locations across a city.
  arXiv  Detail & Related papers  (2024-03-06T22:06:21Z)
• A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System [11.237099288412558]
  Bike-Sharing Systems provide eco-friendly urban mobility, contributing to the alleviation of traffic congestion and healthier lifestyles. Devising effective rebalancing strategies using vehicles to redistribute bikes among stations is therefore of uttermost importance for operators. This paper introduces atemporal reinforcement learning algorithm for the dynamic rebalancing problem with multiple vehicles.
  arXiv  Detail & Related papers  (2024-02-05T23:46:42Z)
• Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph [0.0]
  We propose a novel deep learning-based methodology for a decision support system that enables public transport planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching.
  arXiv  Detail & Related papers  (2023-08-24T14:37:55Z)
• Building Transportation Foundation Model via Generative Graph Transformer [12.660129805049664]
  We propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy.
  arXiv  Detail & Related papers  (2023-05-24T07:34:15Z)
• A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications [43.356096302298056]
  Transportation is the backbone of the economy and urban development. Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications.
  arXiv  Detail & Related papers  (2022-10-26T07:34:51Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems [42.08603087208381]
  We argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. We propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks. We show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks.
  arXiv  Detail & Related papers  (2021-04-23T06:42:38Z)
• Congestion-aware Evacuation Routing using Augmented Reality Devices [96.68280427555808]
  We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model the congestion distribution inside a building.
  arXiv  Detail & Related papers  (2020-04-25T22:54:35Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

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.