arXiv subjects

Shangjia Dong

Publications and source records attributed to Shangjia Dong.

12 recordsLinked to original sources

arXiv2026

A Joint Synthetic Housing-Household Inventory

Accurately understanding the interactions between humans and the built environment requires integrated representations of both the buildings and the populations that occupy them. However, high-fidelity datasets that jointly capture detailed housing structures and demographic characteristics at the household level do not currently exist. This paper presents a framework for constructing a joint housing-household inventory that explicitly links individuals and households to compatible housing units from the National Structure Inventory (NSI), while preserving realistic population densities and demographic distributions. The framework integrates three components: (i) synthetic population generation from American Community Survey (ACS) Public Use Microdata Sample (PUMS) records that preserve complex intra-household relationships; (ii) a deep contrastive learning model that quantifies housing-household compatibility; and (iii) a hierarchical optimization-based allocation procedure that enforces building-level capacity and block-group-level demographic constraints. The generated synthetic population attains high statistical realism relative to the census microdata, and the contrastive learning model identifies compatible housing-household pairs with high predictive accuracy. Applied to coastal North Carolina, evaluations at building, neighborhood, and regional scales show that the joint inventory matches block-group-level demographic distributions, reproduces observed spatial population patterns without systematic bias, and maintains consistent allocation quality across urban, suburban, and rural contexts. By enabling coupled household- and building-level analyses, the resulting inventory supports a broad range of applications, including disaster resilience planning, housing and affordability analysis, energy-use assessment, and public health research.

cs.CY↗

arXiv2026

PASM: Population Adaptive Symbolic Mixture-of-Experts Model for Cross-location Hurricane Evacuation Decision Prediction

Accurate prediction of evacuation behavior is critical for disaster preparedness, yet models trained in one region often fail elsewhere. Using a multi-state hurricane evacuation survey, we show this failure goes beyond feature distribution shift: households with similar characteristics follow systematically different decision patterns across states. As a result, single global models overfit dominant responses, misrepresent vulnerable subpopulations, and generalize poorly across locations. We propose Population-Adaptive Symbolic Mixture-of-Experts (PASM), which pairs large language model guided symbolic regression with a mixture-of-experts architecture. PASM discovers human-readable closed-form decision rules, specializes them to data-driven subpopulations, and routes each input to the appropriate expert at inference time. On Hurricanes Harvey and Irma data, transferring from Florida and Texas to Georgia with 100 calibration samples, PASM achieves a Matthews correlation coefficient of 0.607, compared to XGBoost (0.404), TabPFN (0.333), GPT-5-mini (0.434), and meta-learning baselines MAML and Prototypical Networks (MCC $\leq$ 0.346). The routing mechanism assigns distinct formula archetypes to subpopulations, so the resulting behavioral profiles are directly interpretable. A fairness audit across four demographic axes finds no statistically significant disparities after Bonferroni correction. PASM closes more than half the cross-location generalization gap while keeping decision rules transparent enough for real-world emergency planning.

cs.LG↗

arXiv2025

Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference

Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.

cs.LG↗

arXiv2024

A Deep Generative Framework for Joint Households and Individuals Population Synthesis

Household and individual-level sociodemographic data are essential for understanding human-infrastructure interaction and policymaking. However, the Public Use Microdata Sample (PUMS) offers only a sample at the state level, while census tract data only provides the marginal distributions of variables without correlations. Therefore, we need an accurate synthetic population dataset that maintains consistent variable correlations observed in microdata, preserves household-individual and individual-individual relationships, adheres to state-level statistics, and accurately represents the geographic distribution of the population. We propose a deep generative framework leveraging the variational autoencoder (VAE) to generate a synthetic population with the aforementioned features. The methodological contributions include (1) a new data structure for capturing household-individual and individual-individual relationships, (2) a transfer learning process with pre-training and fine-tuning steps to generate households and individuals whose aggregated distributions align with the census tract marginal distribution, and (3) decoupled binary cross-entropy (D-BCE) loss function enabling distribution shift and out-of-sample records generation. Model results for an application in Delaware, USA demonstrate the ability to ensure the realism of generated household-individual records and accurately describe population statistics at the census tract level compared to existing methods. Furthermore, testing in North Carolina, USA yielded promising results, supporting the transferability of our method.

cs.LG↗

arXiv2022

Anatomy of Perturbed Traffic Networks during Urban Flooding

Urban flooding disrupts traffic networks, affecting the mobility and disrupting access of residents. Since flooding events are predicted to increase due to climate change, and given the criticality of traffic networks, understanding the flood-caused disruption of traffic networks is critical to improve emergency planning and city resilience. Leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis, this study reveals characteristics of the anatomy of perturbed traffic networks. First, the findings show network-wide persistent increased travel times could last for weeks after flood water has receded, even after modest flood failure. During disaster event period, modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show distant trips would experience a greater percentage increase in travel time. Also, the extent of increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. Departing from the existing literature, which primarily informs about physical vulnerability in road networks to floods, the findings of this study provide important novel understanding of floods impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. The persistent travel time increase in the entire network can translate to significant social and economic impacts in terms of user costs, additional CO2 emissions, and lost productivity. Due to the importance of traffic networks functioning for the operation of cities, the results have significance implications for city managers, transportation planners, and emergency managers to deal with the impacts of urban flooding.

physics.soc-ph↗

arXiv2022

Quantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas

The objective of this study is to propose a system-level framework with quantitative measures to assess the resilience of road networks. The framework proposed in this paper can help transportation agencies incorporate resilience considerations into project development proactively and to understand the resilience performance of current road networks effectively. This study identified and implemented four quantitative metrics to classify the criticality of road segments based on critical dimensions of road network resilience, and two integrated metrics were proposed to combine all metrics to show the overall resilience performance of road segments. A case study was conducted on the Texas road networks to demonstrate the effectiveness of implementing this framework in a practical scenario. Since the data used in this study is available to other states and countries, the framework presented in this study can be adopted by other transportation agencies across the globe for regional transportation resilience assessments.

physics.soc-ph↗

arXiv2021

Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness

Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we demonstrate the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we illustrate the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals. Fourth, we leverage location-based and credit card transaction data in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.

physics.soc-ph↗

arXiv2021

Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data

The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood monitoring of road network flooding status plays an essential role in community hazard mitigation, preparedness, and response activities. Existing studies related to the estimation of road inundations either lack observed road inundation data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more essential than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. For example, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.

physics.soc-ph↗

arXiv2021

Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning

Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep Deterministic Policy Gradient) to achieve optimal control by enhancing the cooperation between traffic signals. By introducing the knowledge-sharing enabled communication protocol, each agent can access to the collective representation of the traffic environment collected by all agents. The proposed method is evaluated through two experiments respectively using synthetic and real-world datasets. The comparison with state-of-the-art reinforcement learning-based and conventional transportation methods demonstrate the proposed KS-DDPG has significant efficiency in controlling large-scale transportation networks and coping with fluctuations in traffic flow. In addition, the introduced communication mechanism has also been proven to speed up the convergence of the model without significantly increasing the computational burden.

cs.AI↗

arXiv2021

Empirical Optimization on Post-Disaster Communication Restoration for Social Equality

Disasters are constant threats to humankind, and beyond losses in lives, they cause many implicit yet profound societal issues such as wealth disparity and digital divide. Among those recovery measures in the aftermath of disasters, restoring and improving communication services is of vital importance. Although existing works have proposed many architectural and protocol designs, none of them have taken human factors and social equality into consideration. Recent sociological studies have shown that people from marginalized groups (e.g., minority, low income, and poor education) are more vulnerable to communication outages. In this work, we take pioneering efforts in integrating human factors into an empirical optimization model to determine strategies for post-disaster communication restoration. We cast the design into a mix-integer non-linear programming problem, which is proven too complex to be solved. Through a suite of convex relaxations, we then develop heuristic algorithms to efficiently solve the transformed optimization problem. Based on a collected dataset, we further evaluate and demonstrate how our design will prioritize communication services for vulnerable people and promote social equality compared with an existing modeling benchmark.

cs.NI↗

arXiv2020

A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data

The objective of this study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid deep learning model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision-recall curve and F-measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through one hundred iterations based on examining different weights and thresholds. The test accuracy and F-measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial-temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.

eess.SP↗

arXiv2020

Macroscopic and Microscopic Characteristics of Networks with Time-variant Functionality for Evaluating Resilience to External Perturbations

Knowledge of time-variant functionality of real-world physical, social, and engineered networks is critical to the understanding of the resilience of networks facing external perturbations. The majority of existing studies, however, focus only on the topological properties of networks for resilience assessment, which does not fully capture their dynamical resilience. In this study, we evaluate and quantify network resilience based both on the functionality states of links and on topology. We propose three independent measures---the failure scaling index (FSI), the weighted degree scaling index (WDSI), and the link functionality irregularity index (LFII)---that capture macroscopic, microscopic, and temporal performance characteristics of networks. Accordingly, an integrated general resilience (GR) metric is used to assess performance loss and recovery speed in networks with time-variant functionality. We test the proposed methods in the study of traffic networks under urban flooding impacts in the context of Harris County, Texas, during Hurricane Harvey using a high-resolution dataset, which contains temporal speed of 20,000 roads every 5 minutes for 5 months. Our results show that link weights and node weighted degrees with perturbed functionality in the traffic network during flooding follow a scale-free distribution. Hence, three proposed measures capture clear resilience curves of the network as well as identify the irregularity of links. Accordingly, network performance measures and the methodology for resilience quantification reveal insights into the extent of network performance loss and recovery speed, suggesting possible improvements in network resilience in the face of external perturbations such as urban flooding.

physics.soc-ph↗