Congestion-aware Evacuation Routing using Augmented Reality Devices

2020-04-25 · Zeyu Zhang, Hangxin Liu, Ziyuan Jiao, Yixin Zhu, Song-Chun Zhu

We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations. 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. To efficiently search the evacuation route among all destinations, a variant of A* algorithm is devised to obtain the optimal solution in a single pass. In a series of simulated studies, we show that the proposed algorithm is more computationally optimized compared to classic path planning algorithms; it generates a more time-efficient evacuation route for each individual that minimizes the overall congestion. A complete system using AR devices is implemented for a pilot study in real-world environments, demonstrating the efficacy of the proposed approach.

📄 PDF Abstract BibTeX arXiv:2004.12246

Code (0)

등록된 구현이 없습니다.

Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays

2022-09-04 · Kazi Ashik Islam, Da Qi Chen, Madhav Marathe, Henning Mortveit 외

Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation …

Heuristic Search Management Scheduling

Routing algorithms as tools for integrating social distancing with emergency evacuation

2021-03-05 · Yi-Lin Tsai, Chetanya Rastogi, Peter K. Kitanidis, Christopher B. Field

One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach

2023-11-16 · Md Mobasshir Rashid, Rezaur Rahman, Samiul Hasan

Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. It can reduce evacuation time by providing information on future congestion in advance. However, evacu…

Prediction Traffic Prediction Transfer Learning

Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations

2024-06-17 · Qinhua Jiang, Brian Yueshuai He, Changju Lee, Jiaqi Ma

Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This paper proposes a predictive modeling system that integrates Multilayer Perceptron (MLP) and Long-Short Term Memory (…

Management Prediction Traffic Prediction

Congestion-aware Ride-pooling in Mixed Traffic for Autonomous Mobility-on-Demand Systems

2023-11-06 · Fabio Paparella, Leonardo Pedroso, Theo Hofman, Mauro Salazar

This paper presents a modeling and optimization framework to study congestion-aware ride-pooling Autonomous Mobility-on-Demand (AMoD) systems, whereby self-driving robotaxis are providing on-demand mobility, and users he…