Predict crime using urban metrics with machine learning.
problem Predict crime using urban metrics and handle multicollinearity.
method Random forest regressor for crime prediction and clustering of urban indicators.
result Urban indicators like unemployment and illiteracy are most important for predicting homicides.
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.
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.
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…
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.
Model estimates urban capabilities driving economic performance.
problem Estimating the drivers of urban economic complexity and their connection to performance.
method Derived a model to infer capabilities from employment data, statistically superior to alternatives.
result The derived model explains known urban scaling and economic complexity results and correlates with economic performance.
Graph kernels for metric graphs using tropical algebra.
problem Comparing graphs representing different metric spaces.
method Purely based on geometry and topology, invariant under edge subdivision.
result Capture complementary geometric and topological information.
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.
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.
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.
Modeling urban transformations, researchers identify phase transitions and thermodynamic efficiency.
problem Urban transformations and their effects on social interactions, transport, and income distribution.
method Developed a statistical-mechanical model of urban transformations, considering fast and slow dynamics.
result Identified phase transitions between dispersed and polycentric urban phases, quantified thermodynamic cost of transformation.
Improved pedestrian motion prediction in urban intersections.
problem Accurate prediction of pedestrian trajectories in crowded urban environments.
method Incorporates semantic features from the environment into a Gaussian Process (GP) model for pedestrian motion prediction.
result 12.5% improvement in prediction accuracy and 2.65 times reduction in AUC.
Developing a scientific understanding of cities in a fast urbanizing world is essential for planning sustainable urban systems. Recently, it was shown that income and wealth creation follow increasing returns, scaling superlinearly with city size. We study scaling of per capita incomes for separate census defined incom…
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.
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.
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.
The paper uses WeChat data to map urban cultural resource needs.
problem Optimizing cultural resource allocation in cities.
method Data-driven framework using WeChat user check-ins and temporal LDA model.
result Identifies urban regions lacking cultural resources.
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 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.
Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large data…
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.
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.
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.
Bounded total variation denoising improves traffic analysis accuracy.
problem Improving traffic prediction and clustering accuracy in urban areas.
method Applying bounded total variation denoising to GPS traffic data and clustering analysis.
result Significant improvement in predicting and clustering accuracy after denoising.
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.
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.
Proposes ANN for more accurate path loss prediction in urban environments.
problem Inaccurate path loss prediction in complex urban environments.
method Artificial Neural Network (ANN) for multi-dimensional regression modeling of path loss.
result The proposed ANN model is more accurate and flexible than conventional linear models.
NCPF model improves traffic data imputation with neural and tensor methods.
problem Pervasive missing data in traffic analysis due to sensor failures and gaps.
method Neural Canonical Polyadic Factorization (NCPF) integrating CP decomposition and deep learning.
result NCPF outperforms state-of-the-art baselines in urban traffic datasets.
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.
Paper presents a simple method for accurate user localization in urban areas.
problem Poor GPS performance in dense urban environments.
method Localization based on pathloss using base station signal strengths and RadioUNet approximations.
result Accurate user location extraction in urban environments.
Study uses U-Net for efficient urban planning map semantic segmentation.
problem Manual visual interpretation of urban planning maps is inefficient and time-consuming.
method Utilized U-shape fully convolutional architecture for end-to-end semantic segmentation.
result Achieved high Jaccard similarity coefficient of 93.63% and overall accuracy of 99.36%.
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.
Generative model learns vehicle trajectory distributions for better data generalization.
problem Data sparsity and privacy issues in urban vehicle trajectory analysis.
method Generative adversarial imitation learning framework for urban vehicle trajectory generation.
result TrajGAIL model produces synthetic trajectories similar to real ones, achieving significant performance gains.
Paper presents UrbanFM and UrbanPy models for inferring fine-grained urban flows.
problem Reduce cost of urban flow monitoring while maintaining data accuracy and granularity.
method Develops UrbanFM and UrbanPy models to infer fine-grained urban flows from coarse-grained observations.
result UrbanPy model demonstrates favorable performance for larger-scale inference tasks.
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
problem Lack of interpretability in digital mobility data for understanding urban lifestyles.
method Privacy-enhanced dataset of mobility visitation patterns, latent activity behavior decomposition.
result Detected 12 latent activity behaviors that describe urban lifestyles, not single lifestyles.
I propose and briefly define the concept of Urban Isobenefit Lines by using functions as easy as efficient, whose results can offer a rich tool to use into spatial equilibrium analysis involving cities. They are line joining urban points with equal level of positional advantage from city amenities. The results which on…
Deep learning models optimize urban transportation scheduling.
problem Optimizing transportation systems with complex dynamics and large data sets.
method Developed deep learning metamodels for simulators and reinforcement learning algorithms.
result Improved optimal scheduling of travelers on transportation networks.
Study identifies spatial inequalities in urban services access based on income.
problem Spatial inequalities in urban services and accessibility based on income.
method Multidimensional approach using land use and public transportation data.
result Low-income population has low access to hospitals and cultural centers, while public schools and sports centers have intermediate accessibility.
Because of the prominent position of urban rail in reducing urban transport-related problems, such as congestion and air pollution, insights into the costs of possible new urban rail projects is very relevant for those involved with cost estimations, policy makers, cost-benefit analysts, and other target groups. Knowle…
Model predicts urban population using mobile data traffic.
problem Estimating urban population dynamics from mobile data.
method Data-driven approach combining NetMob 2023 and ENACT datasets.
result NetMob 2023 data can estimate urban population with XGBoost models.
The paper proposes a model to learn street network representations directly from graphs.
problem Loss of detailed topological data in raster representations of street networks.
method Variational autoencoder with graph convolutional layers and a probabilistic fully-connected graph decoder.
result The model infers good representations directly from street networks, capturing both local structure and spatial distribution.
Paper proposes a method for predicting future passenger flow in urban transportation.
problem Predicting future passenger flow in urban transportation development.
method Multi-view localized correlation learning method with adaptive-weight.
result Our method achieves excellent performance compared with other baselines.