Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2309.02812

Agent-based simulation of pedestrians' earthquake evacuation; application to Beirut, Lebanon

Rouba Iskandar·Kamel Allaw·Julie Dugdale·Elise Beck·Jocelyne Adjizian-Gérard·Cécile Cornou·Jacques Harb·Pascal Lacroix·Nada Badaro-Saliba·Stéphane Cartier·Rita Zaarour
View original source ↗Read source document ↗Export metadata ↓

Abstract

Most seismic risk assessment methods focus on estimating the damages to the built environment and the consequent socioeconomic losses without fully taking into account the social aspect of risk. Yet, human behaviour is a key element in predicting the human impact of an earthquake, therefore, it is important to include it in quantitative risk assessment studies. In this study, an interdisciplinary approach simulating pedestrians' evacuation during earthquakes at the city scale is developed using an agent-based model. The model integrates the seismic hazard, the physical vulnerability as well as individuals' behaviours and mobility. The simulator is applied to the case of Beirut, Lebanon. Lebanon is at the heart of the Levant fault system that has generated several Mw>7 earthquakes, the latest being in 1759. It is one of the countries with the highest seismic risk in the Mediterranean region. This is due to the high seismic vulnerability of the buildings due to the absence of mandatory seismic regulation until 2012, the high level of urbanization, and the lack of adequate spatial planning and risk prevention policies. Beirut as the main residential, economic and institutional hub of Lebanon is densely populated. To accommodate the growing need for urban development, constructions have almost taken over all of the green areas of the city; squares and gardens are disappearing to give place to skyscrapers. However, open spaces are safe places to shelter, away from debris, and therefore play an essential role in earthquake evacuation. Despite the massive urbanization, there are a few open spaces but locked gates and other types of anthropogenic barriers often limit their access. To simulate this complex context, pedestrians' evacuation simulations are run in a highly realistic spatial environment implemented in GAMA [1]. Previous data concerning soil and buildings in Beirut [2, 3] are complemented by new geographic data extracted from high-resolution Pleiades satellite images. The seismic loading is defined as a peak ground acceleration of 0.3g, as stated in Lebanese seismic regulations. Building damages are estimated using an artificial neural network trained to predict the mean damage [4] based on the seismic loading as well as the soil and building vibrational properties [5]. Moreover, the quantity and the footprint of the generated debris around each building are also estimated and included in the model. We simulate how topography, buildings, debris, and access to open spaces, affect individuals' mobility. Two city configurations are implemented: 1. Open spaces are accessible without any barriers; 2. Access to some open spaces is blocked. The first simulation results show that while 52% of the population is able to arrive to an open space within 5 minutes after an earthquake, this number is reduced to 39% when one of the open spaces is locked. These results show that the presence of accessible open spaces in a city and their proximity to the residential buildings is a crucial factor for ensuring people's safety when an earthquake occurs.

Explore related subjects

cs.CL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rouba Iskandar, Kamel Allaw, Julie Dugdale, Elise Beck, Jocelyne Adjizian-Gérard, Cécile Cornou, Jacques Harb, Pascal Lacroix, Nada Badaro-Saliba, Stéphane Cartier, Rita Zaarour. 2023-09-06. Agent-based simulation of pedestrians' earthquake evacuation; application to Beirut, Lebanon. https://arxiv.org/abs/2309.02812

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Hierarchical attention interpretation: an interpretable speech-level transformer for bi-modal depression detection

Depression is a common mental disorder. Automatic depression detection tools using speech, enabled by machine learning, help early screening of depression. This paper addresses two limitations that may hinder the clinical implementations of such tools: noise resulting from segment-level labelling and a lack of model interpretability. We propose a bi-modal speech-level transformer to avoid segment-level labelling and introduce a hierarchical interpretation approach to provide both speech-level and sentence-level interpretations, based on gradient-weighted attention maps derived from all attention layers to track interactions between input features. We show that the proposed model outperforms a model that learns at a segment level ($p$=0.854, $r$=0.947, $F1$=0.897 compared to $p$=0.732, $r$=0.808, $F1$=0.768). For model interpretation, using one true positive sample, we show which sentences within a given speech are most relevant to depression detection; and which text tokens and Mel-spectrogram regions within these sentences are most relevant to depression detection. These interpretations allow clinicians to verify the validity of predictions made by depression detection tools, promoting their clinical implementations.

cs.CL↗
arXiv2026

Cultural Alignment in Large Language Models Using Soft Prompt Tuning

Large Language Model (LLM) alignment is commonly achieved through supervised fine-tuning or reinforcement learning, both of which require labeled or preference data and update model weights. Without targeted cultural adaptation, however, deployed LLMs often exhibit culturally homogeneous behavior that fails to reflect diverse local values. Aligning models to cultural value frameworks such as Hofstede's Value Survey Module (VSM13) presents a distinct challenge: alignment signals are available only as aggregated survey-level scores computed after generating responses to an entire survey, providing no per-token gradient and requiring no preference data by construction. This makes standard gradient-based alignment methods ill-suited to the task. We propose a deployment-friendly approach that encodes cultural behavior in short, tunable soft prompts optimized with Differential Evolution (DE), while keeping model weights frozen and requiring no preference data. At inference, the system inserts the appropriate cultural-specific prompt to adapt model responses for different cultures. Experiments across four countries and four instruction-tuned models show that DE-optimized prompts generally reduce discrepancy with VSM13 reference profiles, improve rank agreement with the World Values Survey (WVS), an independent framework not seen during optimization, and are preferred in blinded pairwise evaluations using majority voting across three LLM judges.

cs.CL↗
arXiv2026

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits

Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their extensive output vocabularies. The final linear projection layer, mapping hidden states to vocabulary-sized logits, often constitutes a substantial portion of the model's parameters and computational cost during inference. Existing methods like adaptive softmax or hierarchical softmax introduce structural complexities. In this paper, we propose VQ-Logits, a novel approach that leverages Vector Quantization (VQ) to drastically reduce the parameter count and computational load of the LLM output layer. VQ-Logits replaces the large V * dmodel output embedding matrix with a small, shared codebook of K embedding vectors (K << V ). Each token in the vocabulary is mapped to one of these K codebook vectors. The LLM predicts logits over this compact codebook, which are then efficiently "scattered" to the full vocabulary space using the learned or preassigned mapping. We demonstrate through extensive experiments on standard language modeling benchmarks (e.g., WikiText-103, C4) that VQ-Logits can achieve up to 99% parameter reduction in the output layer and 6x speedup in logit computation, with only a marginal 4% increase in perplexity compared to full softmax baselines. We further provide detailed ablation studies on codebook size, initialization, and learning strategies, showcasing the robustness and effectiveness of our approach.

cs.CL↗