Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201

Scalable Population Synthesis with Deep Generative Modeling

Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for how to 'grow' pools of micro-agents is presented. The model framework adopts a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE). Compared to the previous population synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs sampling and traditional generative models such as Bayesian Networks or Hidden Markov Models, the proposed method allows fitting the full joint distribution for high dimensions. The proposed methodology is compared with a conventional Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary. It is shown that, while these two methods outperform the VAE in the low-dimensional case, they both suffer from scalability issues when the number of modeled attributes increases. It is also shown that the Gibbs sampler essentially replicates the agents from the original sample when the required conditional distributions are estimated as frequency tables. In contrast, the VAE allows addressing the problem of sampling zeros by generating agents that are virtually different from those in the original data but have similar statistical properties. The presented approach can support agent-based modeling at all levels by enabling richer synthetic populations with smaller zones and more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

Towards a Computational Case-Based Model for Creative Planning

This paper describes a computational case-based model for the creative planning process. Our approach is inspired in Wallas’ model for the creative process in that we consider that creativity involves a sequence of four stages: preparation, incubation, illumination and verification. Preparation includes problem acquisition and assimilation of background knowledge, which is represented by cases, i.e., documented past experiences. With the aim of achieving a flexible knowledge representation, as a means to potentiate specific creative abilities like Fluency, Synthesis and Analysis, we structure each case as a network of hierarchically and temporally related case pieces. These case pieces can be considered individually, providing better recombinations of them. These recombinations, rather than made by chance, are guided by those hierarchical and temporal case piece relations (or explanations). We explain the role of opportunistic knowledge acquisition at the incubation stage. We sustain that illumination may comprise recursive calls of the sequence of the first three stages. This computational model is implemented in the system INSPIRER (ImagiNation1 taking as Source Past and Imperfectly RElated Reasonings). An application in musical composition domain is presented. We also show how a musical composition task may be cognitively modelled and treated as a planning task. We also present a short example illustrating how INSPIRER generates music

The role of strain localization in magma injection into a transtensional shear zone (Variscan belt, SW Iberia).

This study deals with the interaction between deformation and magmatism in mid- to deep-crustal domains. The relation is analysed between migmatites and shear zones and the spatial distribution of leucogranitoid veins and dykes running through a footwall migmatite system, and reaching a transtensional shear zone operated under amphibolite- to greenschist-facies metamorphic conditions (Boa Fé shear zone, Variscan belt, SW Iberia). Statistical results show that the frequency of width and spacing of the leucogranitoid dykes conform to power-law distributions comparable with observations in volcanic systems. The fractal geometry of the distribution of leucogranitoid dykes highlights the development of a dense framework of thinner weakly or non-mineralized veins and dykes formed at higher nucleation/growth ratios in the footwall migmatite system that contrasts with the emplacement of thicker dykes associated with strongly mineralized thinner veins within the shear zone. The volume of injected leucogranitoid dykes in the shear zone is lower as compared with the footwall and is comparable with an expanding footwall shear zone with non-coaxial flow and volume increase. The Boa Fé shear zone seems to form a physical barrier to the transport of magma to the hanging wall

A structured framework for representing time in a generative composition system

The representation of music structures is, from Musicology to Artificial Intelligence, a widely known research focus. It entails several generic Knowledge Representation problems like structured knowledge representation, time representation and causality. In this paper, we focus the problem of representing and reasoning about time in the framework of a structured music representation approach, intended to support the development of a Case-Based generative composition system. The basic idea of this system is to use Music Analysis as foundation for a generative process of composition, providing a structured and constrained way of composing novel pieces, although keeping the essential traits of the composer’s style. We propose a solution that combines a tree-like representation with a pseudo-dating scheme to provide an efficient and expressive means to deal with the problem