4 research outputs found
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
Estrategias multi-mapa para el enrutamiento dinámico de tráfico urbano
La Directiva «Clean Transport, Urban Transport» de la Unión Europea identifica que la congestión en áreas urbanas tiene un coste anual acumulado de 100 billones de euros. El 60% de la población europea se ubica en áreas urbanas de más de 10,000 habitantes. De igual manera, se estima que la movilidad urbana es causante del 40% de emisiones de CO2 y hasta el 70% de otros contaminantes. Pero el problema es global y generalizado.
La tesis aborda la problemática de optimizar tanto la planificación del tráfico urbano como su enrutamiento dinámico mediante una nueva técnica denominada Traffic Weighted Multi-Maps (TWM) con el fin de mitigar la congestión y sus efectos en los entornos urbanos. TWM propone la entrega selectiva de mapas de tráfico a los diferentes conjuntos de vehículos presentes en la red tenido en cuenta sus especificidades, el momento temporal, las situaciones de la via y el contexto. Para ello, recoge la colección de artículos científicos publicados en revistas indexadas respecto a TWM.
La tesis analiza el uso de TWM para diversos casos de uso: mejora de la congestión en redes urbanas complejas mediante mapas de red aleatorizados, el encaminamiento selectivo de flotas, la reducción de la congestión ante incidentes aleatorios o planificados, y se plantean otros muchos escenarios.
Asimismo, la tesis profundiza en cómo obtener distribuciones de mapas TWM óptimos para una cierta demanda de tráfico conocida por medio de datos históricos, proponiendo un conjunto de algoritmos de optimización basado en algoritmos evolutivos.
El éxito de la implantación de un sistema de gestión inteligente de tráfico (ITS) depende de la adherencia de los conductores al mismo, dependiendo ésta de la percepción de la utilidad por los conductores. La tesis propone un modelo de experiencia de usuario-conductor para analizar el caso complejo de una red de tráfico que emplee diversos ITS de forma simultánea y no coordinada, con el objetivo de analizar la evolución en el tiempo de la adherencia de los conductores a TWM y así validar las hipótesis de partida respecto a su efectividad.
La parte experimental de la tesis describe cómo se han empleado simulaciones de tráfico de diferente tipología: microscópicas y macroscópicas, desarrollando componentes de simulación específicos sobre plataformas abiertas de simulación de tráfico.
Los resultados obtenidos son muy prometedores, obteniendo mejoras en la congestión global que oscilan entre el 20% y el 45%, con impacto en el resto de indicadores de emisiones y movilidad. Los estudios de simulación del comportamiento de los conductores en base a la utilidad percibida de TWM, muestran cómo la adherencia al mismo crece y se estabiliza garantizando el comportamiento global.
Por último, se indican las posibles líneas futuras de investigación identificadas
An innovative approach to multi-method integrated assessment modelling of global climate change
© 2020, University of Surrey. All rights reserved. Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisation models. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, often used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybrid modelling approach; we do not claim that it has predictive powers
Supporting an integrated transportation infrastructure and public space design: A coupled simulation method for evaluating traffic pollution and microclimate
Traditional urban and transport infrastructure planning that emphasized motorized transport has fractured public space systems and worsened environmental quality, leading to a decrease in active travel. A novel multiscale simulation method for supporting an integrated transportation infrastructure and public space design is presented in this paper. This method couples a mesoscale agent-based traffic prediction model, traffic-related emission calculation, microclimate simulations, and human thermal comfort assessment. In addition, the effects of five urban design strategies on traffic pollution and pedestrian level microclimate are evaluated (i.e., a “two-fold” evaluation). A case study in Beijing, China, is presented utilizing the proposed urban modeling-design framework to support the assessment of a series of transport infrastructure and public space scenarios, including the Baseline scenario, a System-Internal Integration scenario, and two External Integration scenarios. The results indicate that the most effective way of achieving an environmentally- and pedestrian- friendly urban design is to concentrate on both the integration within the transport infrastructure and public space system and the mitigation of the system externalities (e.g., air pollution and heat exhaustion). It also demonstrates that the integrated blue-green approach is a promising way of improving local air quality, micro-climatic conditions, and human comfort