255 research outputs found
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and
developed separately. However, there is now cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In the
present paper, we introduce the importance of urban planning from the
sustainability, living, economic, disaster, and environmental perspectives. We
review the fundamental concepts of urban planning and relate these concepts to
crucial open problems of machine learning, including adversarial learning,
generative neural networks, deep encoder-decoder networks, conversational AI,
and geospatial and temporal machine learning, thereby assaying how AI can
contribute to modern urban planning. Thus, a central problem is automated
land-use configuration, which is formulated as the generation of land uses and
building configuration for a target area from surrounding geospatial, human
mobility, social media, environment, and economic activities. Finally, we
delineate some implications of AI for urban planning and propose key research
areas at the intersection of both topics.Comment: TSAS Submissio
Robustness Analysis of Deep Learning Models for Population Synthesis
Deep generative models have become useful for synthetic data generation,
particularly population synthesis. The models implicitly learn the probability
distribution of a dataset and can draw samples from a distribution. Several
models have been proposed, but their performance is only tested on a single
cross-sectional sample. The implementation of population synthesis on single
datasets is seen as a drawback that needs further studies to explore the
robustness of the models on multiple datasets. While comparing with the real
data can increase trust and interpretability of the models, techniques to
evaluate deep generative models' robustness for population synthesis remain
underexplored. In this study, we present bootstrap confidence interval for the
deep generative models, an approach that computes efficient confidence
intervals for mean errors predictions to evaluate the robustness of the models
to multiple datasets. Specifically, we adopt the tabular-based Composite Travel
Generative Adversarial Network (CTGAN) and Variational Autoencoder (VAE), to
estimate the distribution of the population, by generating agents that have
tabular data using several samples over time from the same study area. The
models are implemented on multiple travel diaries of Montreal Origin-
Destination Survey of 2008, 2013, and 2018 and compare the predictive
performance under varying sample sizes from multiple surveys. Results show that
the predictive errors of CTGAN have narrower confidence intervals indicating
its robustness to multiple datasets of the varying sample sizes when compared
to VAE. Again, the evaluation of model robustness against varying sample size
shows a minimal decrease in model performance with decrease in sample size.
This study directly supports agent-based modelling by enabling finer synthetic
generation of populations in a reliable environment.Comment: arXiv admin note: text overlap with arXiv:2203.03489,
arXiv:1909.07689 by other author
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Human mobility estimation is crucial during the COVID-19 pandemic due to its
significant guidance for policymakers to make non-pharmaceutical interventions.
While deep learning approaches outperform conventional estimation techniques on
tasks with abundant training data, the continuously evolving pandemic poses a
significant challenge to solving this problem due to data nonstationarity,
limited observations, and complex social contexts. Prior works on mobility
estimation either focus on a single city or lack the ability to model the
spatio-temporal dependencies across cities and time periods. To address these
issues, we make the first attempt to tackle the cross-city human mobility
estimation problem through a deep meta-generative framework. We propose a
Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that
estimates dynamic human mobility responses under a set of social and policy
conditions related to COVID-19. Facilitated by a novel spatio-temporal
task-based graph (STTG) embedding, STORM-GAN is capable of learning shared
knowledge from a spatio-temporal distribution of estimation tasks and quickly
adapting to new cities and time periods with limited training samples. The STTG
embedding component is designed to capture the similarities among cities to
mitigate cross-task heterogeneity. Experimental results on real-world data show
that the proposed approach can greatly improve estimation performance and
out-perform baselines.Comment: Accepted at the 22nd IEEE International Conference on Data Mining
(ICDM 2022) Full Pape
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation
Effective management of urban traffic is important for any smart city
initiative. Therefore, the quality of the sensory traffic data is of paramount
importance. However, like any sensory data, urban traffic data are prone to
imperfections leading to missing measurements. In this paper, we focus on
inter-region traffic data completion. We model the inter-region traffic as a
spatiotemporal tensor that suffers from missing measurements. To recover the
missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach
that considers the urban and temporal aspects of the traffic. To derive the
urban characteristics, we divide the area of study into regions. Then, for each
region, we compute urban feature vectors inspired from biodiversity which are
used to compute the urban similarity matrix. To mine the temporal aspect, we
first conduct an entropy analysis to determine the most regular time-series.
Then, we conduct a joint Fourier and correlation analysis to compute its
periodicity and construct the temporal matrix. Both urban and temporal matrices
are fed into a modified CP-completion objective function. To solve this
objective, we propose an alternating least square approach that operates on the
vectorized version of the inputs. We conduct comprehensive comparative study
with two evaluation scenarios. In the first one, we simulate random missing
values. In the second scenario, we simulate missing values at a given area and
time duration. Our results demonstrate that our approach provides effective
recovering performance reaching 26% improvement compared to state-of-art CP
approaches and 35% compared to state-of-art generative model-based approaches
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