27 research outputs found

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

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    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

    Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

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    Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.Comment: 21 pages, 10 figure

    Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

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    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

    Variational Optimisation for Non-conjugate Likelihood Gaussian Process Models

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    In this thesis we address the problems associated to non-conjugate likelihood Gaussian process models, i.e., probabilistic models where the likelihood function and the Gaussian process priors are non-conjugate. Such problems include intractability, scalability, and poor local optima solutions for the parameters and hyper-parameters of the models. Particularly, in this thesis we address the aforementioned issues in the context of probabilistic models, where the likelihood’s parameters are modelled as latent parameter functions drawn from correlated Gaussian processes. We study three ways to generate such latent parameter functions: 1. from a linear model of coregionalisation; 2. from convolution processes, i.e., a convolution integral between smoothing kernels and Gaussian process priors; and 3. using variational inducing kernels, an alternative form to generate the latent parameter functions through the convolution processes formalism, by using a double convolution integral. We borrow ideas from different variational optimisation mechanisms, that consist on introducing a variational (or exploratory) distribution over the model so as to build objective functions that: allow us to deal with intractability as well as enabling scalability when needing to hand massive amounts of data observations. Also, such variational optimisations mechanisms grant us to perform inference of the model hyper-parameters together with the posterior’s parameters through a fully natural gradient optimisation scheme; a useful scheme for tackling the problem of poor local optima solutions. Such variational optimisation mechanisms have been broadly studied in the context of reinforcement and Bayesian deep learning showing to be successful exploratory-learning tools; nonetheless, they have not been much studied in the context of Gaussian process models, so we provide a study of their performance in said context

    A Data Fusion CANDECOMP-PARAFAC Method for Interval-wise Missing Network Volume Imputation

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    Traffic missing data imputation is a fundamental demand and crucial application for real-world intelligent transportation systems. The wide imputation methods in different missing patterns have demonstrated the superiority of tensor learning by effectively characterizing complex spatiotemporal correlations. However, interval-wise missing volume scenarios remain a challenging topic, in particular for long-term continuous missing and high-dimensional data with complex missing mechanisms and patterns. In this paper, we propose a customized tensor decomposition framework, named the data fusion CANDECOMP/PARAFAC (DFCP) tensor decomposition, to combine vehicle license plate recognition (LPR) data and cellphone location (CL) data for the interval-wise missing volume imputation on urban networks. Benefiting from the unique advantages of CL data in the wide spatiotemporal coverage and correlates highly with real-world traffic states, it is fused into vehicle license plate recognition (LPR) data imputation. They are regarded as data types dimension, combined with other dimensions (different segments, time, days), we innovatively design a 4-way low-n-rank tensor decomposition for data reconstruction. Furthermore, to deal with the diverse disturbances in different data dimensions, we derive a regularization penalty coefficient in data imputation. Different from existing regularization schemes, we further introduce Bayesian optimization (BO) to enhance the performance in the non-convexity of the objective function in our regularized hyperparametric solutions during tensor decomposition. Numerical experiments highlight that our proposed method, combining CL and LPR data, significantly outperforms the imputation method using LPR data only. And a sensitivity analysis with varying missing length and rate scenarios demonstrates the robustness of model performance

    Learning Behavior Models for Interpreting and Predicting Traffic Situations

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    In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees

    A supervised learning framework in the context of multiple annotators

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    The increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, is changing how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), we have datasets labeled by multiple annotators with different and unknown expertise. Hence, we face a multi-labeler scenario, which typical supervised learning models cannot tackle. For such a reason, much attention has recently been given to the approaches that capture multiple annotators’ wisdom. However, such methods residing on two key assumptions: the labeler’s performance does not depend on the input space and independence among the annotators, which are hardly feasible in real-world settings..
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