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

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

Modeling Censored Mobility Demand through Quantile Regression Neural Networks

Shared mobility services require accurate demand models for effective service planning. On one hand, modeling the full probability distribution of demand is advantageous, because the full uncertainty structure preserves valuable information for decision making. On the other hand, demand is often observed through usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have shown them to perform well under such conditions, and in the last two decades, several works have proposed to implement them flexibly through Neural Networks (CQRNN). However, apparently no works have yet applied CQRNN in the Transport domain. We address this gap by applying CQRNN to datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark, as well as common synthetic baseline datasets. The results show that CQRNN can estimate the intended distributions better than both censorship-unaware models and parametric censored models.Comment: 13 pages, 7 figures, 4 table

Lichen-Moss associations in plant communities of the Southwest Admiralty Bay, King George Island, Antarctica

The phytosociology of plant communities in the Admiralty Bay ice-free areas (King George Island, South Shetland Islands, Antarctica) was investigated during the 2003/04 summer seasons. In this study associations among lichens and mosses were found, where the lichen species are dominant in the samples. A total of 10 associations are identified. For each association found in this work, descriptions are given and comments about their ecology and distribution in the study area are made. Key words: Antartic plants, phytosociology, ecology.The phytosociology of plant communities in the Admiralty Bay ice-free areas (King George Island, South Shetland Islands, Antarctica) was investigated during the 2003/04 summer seasons. In this study associations among lichens and mosses were found, where the lichen species are dominant in the samples. A total of 10 associations are identified. For each association found in this work, descriptions are given and comments about their ecology and distribution in the study area are made. Key words: Antartic plants, phytosociology, ecology

Quality of board members’ training and bank financial performance: evidence from Portugal

This study examines the impact of the quality of board members’ training on the financial performance of Portuguese banks. The study employs a sample of 276 board members. Financial ratios such as return on average assets (ROAA) and return on average equity (ROAE) are used as measures for gauging banks’ financial performance. Three indexes are used as proxies for board members’ educational qualifications, specifically: Eduindex, for all academic qualifications obtained in areas such as business or economics; EduindexDP, for all qualifications obtained in prestigious domestic business schools; and EduindexFP, for all qualifications obtained in prestigious foreign business schools. The study findings have important policy implications, specifically a positive and significant impact on the bank’s financial performance from having board members holding degrees from prestigious foreign business schools. In particular, the findings suggest that the prudential supervision developed by Banco de Portugal in cooperation with the European Central Bank should include a more rigorous process in the selection of board members. The present study is one of the first attempts in the literature emphasizing all these aspects simultaneously, that is, the banking sector, quality of board members’ training, and Eduniversal Rankings, in the context in which all the banks of a specific country are analysed. © 2018 International Strategic Management Association.info:eu-repo/semantics/publishedVersio

Aplicação de um índice de desempenho de banco verde em Portugal

This paper reports the calculation of a green banking performance index that considers the importance that banks give to environmental issues. Research was conducted considering all banks authorized to operate in Portugal and similar relevant institutions. The calculated green banking performance index reveals a large difference between the 5 most representative banks and the others. The 5 most representative banks show that, on average, they are very concerned with environmental issues, and the others reveal, on average, a medium level of concern in terms of environmental issues.info:eu-repo/semantics/publishedVersio

Recommender system for drivers of electric vehicles

Being the next big step in automobile industry, electric vehicles continue to have limited autonomy which associated with the long charging times, limited charging stations and undeveloped smart grid infrastructure demands for a hard planning of the daily use of the vehicle. This paper presents an information system that will help the driver in the daily use of his electric vehicle, minimizing the problem of range anxiety thru the continuous control of the vehicle range and presenting in time relevant information about the charging stations within reach. Given the success of recommendation systems on automatically delivering the relevant information in numerous areas of usage, it can be applied in this scenario as well as with the objective of maximizing the relevance of the information presented to the driver, which should be the strictly needed for him to make his decisions filtering out the unnecessary one.Fundação para a Ciência e a Tecnologia (FCT

Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand

Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better optimisation of the power grid and future infrastructure expansions. In this paper, we propose to use publicly available data to forecast the electric vehicle charging demand. To model the complex spatial-temporal correlations between charging stations, we argue that Temporal Graph Convolution Models are the most suitable to capture the correlations. The proposed Temporal Graph Convolutional Networks provide the most accurate forecasts for short and long-term forecasting compared with other forecasting methods

Periodic vehicle routing problem in a health unit

In logistics of home health care services in the Health Units, the managers and nurses need to carry out the schedule and the vehicles routes for the provision of care at the patients' homes. Currently, in Portugal, these services are increasingly used but the problem is still, usually, solved manually and without computational resources. The increased demand for home health care due to the boost of the elderly people number entails a high associated cost which, sometimes, does not guarantee the quality of the service. In this sense, the periodic vehicle routing problem is a generalization of the classical vehicle routing problem in which routes are determined for a time horizon of several days. In this work, it is provided a periodic vehicle routing problem applied in the Health Unit in Bragança. An integer linear programming formulation for the real database, allowed to solve the problem in an efficient and optimized way using the CPLEXR software.Programa Operacional Temático Factores de Competitividade(POCI-01-0145-FEDER-007043