Paper predicts urban dispersal events using deep survival analysis on mobility data.
problem Predicting abnormal dispersal events in urban areas to mitigate congestion and safety risks.
method Formulated as a survival analysis problem, developed a two-stage deep learning framework (DILSA).
result DILSA predicts dispersal events with F1-score of 0.7 and average time error of 18 minutes.
Paper explores multimodal methods for detecting urban micro-events.
problem Detecting urban micro-events in cities with limited geographical coverage.
method Exploring multimodal fusion methods including early, late, hybrid fusion and representation learning.
result Multimodal approach yields higher performance than unimodal alternatives.
The study examines Hawkes processes and their long-term behavior.
problem Understanding the long-term behavior of Hawkes processes.
method Proving functional limit theorems under various conditions on the dispersion of child events.
result Functional limit theorems hold for Hawkes processes with different levels of child event dispersion.
Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical-mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic descriptio…
Survey of urban flows prediction methods using various datasets.
problem Predicting urban flows influenced by human activities, weather, events, and holidays.
method Analysis of four main factors, preparation of multi-sources spatial-temporal data, detailed comparison of five categories of prediction methods.
result Facilitates researchers to choose suitable methods and datasets for urban flows prediction.
Framework classifies urban congestion into recurrent and non-recurrent types.
problem Classifying urban congestion into recurrent and non-recurrent types.
method Real-time distributed classification using VANET on a heterogeneous urban road network.
result Predictive accuracy of 89.17% for the boosting technique.
DMPP predicts events in cities using rich contextual data.
problem Predicting events in cities with rich contextual factors.
method Deep Mixture Point Processes model with mixture of kernels and deep neural network for context.
result DMPP outperforms existing methods in event prediction.
Reduced-order model improves LES for atmospheric pollutant dispersion.
problem Accurate near-field pollutant concentration tracking in urban areas.
method Combining POD and GPR for non-intrusive reduced-order modeling.
result Component-by-component optimization captures spatial scales in high-order modes.
New approach predicts crime and terrorism events with high accuracy and transparency.
problem Predicting crime and terrorism events with high accuracy and interpretability.
method Granger Network inference for local transport rules learning.
result Achieved AUC of ~90% for crime predictions and ~80% for terrorism predictions.
Proposes a dynamic model for urban traffic volume prediction.
problem Urban traffic volume prediction for better traffic management and driver planning.
method Combines bidirectional LSTM, attention mechanism, and external features.
result Improves prediction precision by 3-7 percent on NYC-Taxi and NYC-Bike datasets.
MIP framework improves urban flow prediction by adapting to distribution shifts.
problem Distribution shifts in urban flow data make prediction models unreliable.
method Memory-enhanced Invariant Prompt learning with learnable memory bank.
result MIP ensures robust predictions by focusing on invariant features.
Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.
problem Forecasting crash rates in areas with irregular traffic patterns and exogenous events.
method Adopted a stochastic volatility model to capture heterogeneity and temporal instability.
result The stochastic volatility model outperforms conventional models in forecasting crash rates in Washington, D.C.
We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual d…
AI classifies tourist events for better service.
problem Classifying tourist events efficiently across diverse sources.
method CRISP-DM, supervised machine learning, NLP.
result Automatic classification tool for consistent event catalogs.
Study uses ML to predict currency and bond returns from news sentiment.
problem Predicting financial returns from news sentiment.
method Pretrained FinBERT model on finance-specific language, XGBoost classifier, SHAP for interpretability.
result XGBoost strategy outperforms benchmarks with Sharpe ratios > 5.
VAE models generate urban street networks from data.
problem Quantitative description of urban patterns.
method Variational Autoencoders (VAEs) for urban street network data.
result VAEs can capture urban network metrics and generate new urban forms.
UrbanRhythm reveals urban dynamics from mobility data.
problem Understanding changing urban activities over time.
method Extracting staying, leaving, arriving attributes; using Saak transform; clustering for city states; motif analysis for short-term regularity.
result Characterized urban dynamics as city state transformations over time.
Graph embeddings from commute networks identify socioeconomic disparities in urban areas.
problem Urban delineation and socioeconomic group identification.
method Graph Neural Network (GNN) for modeling commute networks and deriving node embeddings.
result GNNs effectively capture socioeconomic disparities between urban communities.
Study proposes CNN for reconstructing high-res urban DEMs.
problem Lack of high-res urban DEM datasets for flood modeling.
method Multi-scale CNN model trained on urban DEMs of varying resolutions.
result CNN-based method produces superior high-res urban DEMs.
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.
problem Urban traffic congestion in expanding cities.
method Behavioral system theory, data-driven control, DeePC algorithm.
result DeePC outperforms existing traffic control methods in travel time and CO2 emissions.
Proposes a model to understand urban dynamics from mega-metropolises.
problem Understanding residents mobility patterns in mega-metropolises.
method Neighbor-Regularized and context-aware Non-negative Tensor Factorization (NR-cNTF).
result NR-cNTF accurately captures city rhythms and spatial communities.
Barcelona evaluates major events for economic and social impact.
problem Evaluating the economic and social impact of major events in Barcelona.
method Analyzes the economic and social dimensions of Barcelona's major events from 1888 to 2004.
result Develops a rational argument for communicating the economic benefits of major events.
Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multic…
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.
Model predicts traffic speed using urban incidents.
problem Accurately predicting traffic speed in urban areas.
method Deep Incident-Aware Graph Convolutional Network (DIGC-Net).
result Model outperforms competing benchmarks in traffic speed prediction.
Hybrid deep learning model predicts urban floods with high accuracy.
problem Urban flood prediction and situation awareness using channel network sensors data.
method FastGRNN-FCN hybrid deep learning model trained on Harris County, Texas flood data.
result Test accuracy and F-measure reach 97.8% and 0.792, respectively.
Novel graph model forecasts urban traffic with reduced spatial complexity.
problem Challenges in traffic forecasting due to spatio-temporal complexity, especially in urban environments.
method MW-TGC network model that combines spatial and temporal dependencies using multi-weighted adjacency matrices and graph convolution operations.
result MW-TGC network outperforms other models in urban-core and urban-mix sites, reducing variance in heterogeneous environments.
New dispersion indices based on inaccuracy and divergence introduced for information measures.
problem Measuring variability in uncertainty measures.
method Introducing new dispersion indices based on Kerridge inaccuracy and Kullback-Leibler divergence.
result Properties, bounds, and examples of new dispersion indices presented.
hood2vec identifies urban area similarity via mobility networks.
problem Identifying similar urban areas using mobility networks.
method Learning node embeddings of the mobility network from Foursquare check-ins.
result Mobility dynamics capture different aspects of urban area similarity than venue types.
The paper uses GIS data to predict urban sprawl.
problem Overgrowth and expansion of low-density areas with car dependency and segregation.
method Data mining algorithms (Apriori, J4.8) adapted for geospatial analysis using ArcGIS.
result Prototype spatial decision support system (SDSS) predicts urban sprawl and estimates impact variables.
Paper optimizes urban navigation with deep learning models.
problem Real-time urban pathfinding challenges.
method Enhanced A* algorithm and neural network model.
result Neural network model outperforms traditional methods, reducing travel times by up to 40%.
Deep learning agent improves pedestrian navigation in urban environments.
problem Autonomous driving among pedestrians in urban areas.
method Multi-objective deep reinforcement learning using a deep Q-learning variant.
result The multi-objective DQN agent outperforms single-objective DQN in various environments.
Novel RL method handles urban driving tasks including traffic lights.
problem Difficulties in reinforcement learning for complex urban driving tasks.
method Implicit affordances for model-free RL.
result Successfully handles urban driving tasks including traffic lights.
Bayesian network framework assesses urban risks across multiple domains.
problem Complex interdependencies in urban systems.
method Bayesian Belief Networks (BBNs) with DAGs, Hill-Climbing search, BIC, K2 scoring, synthetic data, SMOTE.
result Identifies key risk factors and quantifies likelihood of cascading failures.
New heat dispersion laws established for smooth compact manifolds.
problem Understanding heat dispersion in smooth compact manifolds.
method Established new heat dispersion laws through Theorem 1.1 and explored them further with Propositions 3.1 and 3.2.
result New heat dispersion laws for smooth compact manifolds.
AirRL uses RL to infer urban air quality from selected stations.
problem Inferring fine-grained urban air quality from limited monitoring stations.
method Reinforcement learning model with a dynamic station selector and air quality regressor.
result AirRL achieves highest performance in air quality inference experiments.
Quantile regression improves urban water demand forecasting.
problem Improving probabilistic urban water demand forecasting.
method Comparing five quantile regression algorithms and their combinations for one-day ahead forecasting.
result Linear boosting algorithm performs best for probabilistic urban water demand forecasting.
Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to offer generous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as…
MallowsPO enhances LLM fine-tuning with a dispersion index of human preferences.
problem Lack of diversity in human preferences in DPO.
method Developed a dispersion index based on Mallows' theory to characterize preference diversity.
result Demonstrated improved performance in various tasks using the dispersion index.
New model predicts urban sprawl sensitivity from remote sensing data.
problem Forecasting urban sprawl sensitivity to economic factors.
method Physics-constrained conditional GANs for image-to-image translation.
result Model accurately predicts urban sprawl sensitivity without detailed data.
Deep Recurrent Q-Network improves autonomous driving in urban areas with pedestrians.
problem Challenges in urban autonomous driving due to complex road structures and unpredictable pedestrian behavior.
method Combines Deep Q-Network with LSTM for long-term memory, designed a 3-D state representation, and uses a reward function.
result The proposed DRQN-based approach outperforms rule-based methods in dense urban scenarios.
Geometric focusing affects dispersive estimates for Schrödinger and wave equations.
problem Long-time decay rate in dispersive estimates for Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones.
method Classifying the long-time decay rate in dispersive estimates for the Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones in terms of the intensity of geometric focusing.
result Each multiplicity of conjugate points within distance π on Y = ∂X0 leads to a |t|1/2-loss in the long-time decay order and a half-order shift in the regularity index in the dispersive estimate for the Schrödinger equation.
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
While it is believed that denoising is not always necessary in many big data applications, we show in this paper that denoising is helpful in urban traffic analysis by applying the method of bounded total variation denoising to the urban road traffic prediction and clustering problem. We propose two easy-to-implement m…
Intensive development of urban systems creates a number of challenges for urban planners and policy makers in order to maintain sustainable growth. Running efficient urban policies requires meaningful urban metrics, which could quantify important urban characteristics including various aspects of an actual human behavi…
In the recent years, banks have sold structured products such as worst-of options, Everest and Himalayas, resulting in a short correlation exposure. They have hence become interested in offsetting part of this exposure, namely buying back correlation. Two ways have been proposed for such a strategy : either pure correl…
Study shows income inequality increases with city size, affecting only the wealthiest deciles.
problem Understanding income inequality in urban areas.
method Urban scaling analysis of total income scaling in population percentiles.
result Income in the poorest decile does not increase with city size, while the wealthiest deciles show superlinear scaling.