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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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%.
RadioUNet uses deep learning to estimate pathloss in urban environments.
problem Accurate pathloss estimation for urban environments.
method Deep neural networks trained on physical simulation data.
result RadioUNet generates pathloss estimations faster and more accurately than existing methods.
MIDAS learns to adaptively control other cars in urban driving scenarios.
problem Autonomous vehicles need to interact with other agents on the road.
method Reinforcement learning with attention mechanism to handle multiple agents.
result MIDAS policies are adaptive and robust to external changes.
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.
Deep-MAPS uses machine learning for mobile air pollution sensing in Beijing.
problem Ubiquitous sensing of urban air quality.
method Machine learning framework (Deep-MAPS) based on mobile and fixed sensors.
result Deep-MAPS achieves high spatial-temporal resolution (1km-by-1km and 1 hour) with over 85% accuracy.
Study proposes new methods to calculate probabilistic benchmarks in noisy data.
problem Identifying opportunities for improvement in comparable units with noisy data.
method 2-step methodology involving undersampling and relevance vector machine.
result Higher discrimination power achieved with macro-economic environment variables.
New method interprets complex models for music and urban simulations.
problem Difficulties in understanding deep neural network predictions.
method Uses generative models to improve explanation clarity.
result Flexibility demonstrated across diverse modalities (music, urban simulations).
This study maps cycling risks and discomfort in Zurich, offering personalized route recommendations.
problem High cycling accidents and discomfort in Smart Cities.
method Geolocated bike accidents data, kernel density contours, weather, time, accident type and severity analysis.
result Empirical continuous spatial risk estimations and personalized route recommendations.
PhysVarMix predicts diverse urban trajectories with physics constraints.
problem Predicting complex urban agent trajectories with multiple plausible scenarios.
method Physics-informed variational mixture model combining learning and physics constraints.
result Superior performance compared to existing methods on benchmark datasets.
Study improves self-driving safety in dynamic environments.
problem Safe self-driving in non-stationary urban settings.
method Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA).
result Self-driving agents can adapt safely in real-time.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
problem Nonlinear, nonstationary, spatiotemporally dependent urban air pollution forecasting challenges.
method Combines graph convolutional learning and support vector regression.
result GraphSVR improves predictive accuracy and maintains stable performance across seasons and outlier-prone episodes.
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-bas…
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.
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.
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.
CARL safely adapts RL agents for safety-critical tasks.
problem Safety hazards in RL for safety-critical tasks.
method CARL combines model-based RL and cautious adaptation.
result CARL achieves higher rewards with fewer failures in safety-critical tasks.
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…
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…
The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and t…
Paper develops a framework to test deep RL traffic controllers under various uncertainties.
problem Developing robust deep RL traffic controllers for dynamic urban areas.
method Open-source callback-based framework for evaluating deep RL configurations in a traffic simulation.
result Deep RL controllers perform well under demand surges, incidents, and sensor failures.
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.
We consider the exploration problem: an agent equipped with a depth sensor must map out a previously unknown environment using as few sensor measurements as possible. We propose an approach based on supervised learning of a greedy algorithm. We provide a bound on the optimality of the greedy algorithm using submodulari…
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.
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.
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.
SafeCritic predicts safe trajectories for pedestrians and cyclists avoiding collisions.
problem Predicting safe trajectories for pedestrians and cyclists in urban environments.
method Generative adversarial networks and reinforcement learning to generate safe trajectories, evaluated by a Discriminator.
result Significant improvement over state-of-the-art models in safety classification.
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.
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…
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…
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.
Deep RL for multi-agent autonomous driving in dynamic environments.
problem Adapting to dynamic, multi-agent driving environments.
method Formulated Partially Observable Markov Games (POSG) for multi-agent learning.
result Demonstrated successful training of multi-agent control policies.