Browsing by Author "Goodyer, E."

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    Neighbouring Link Travel Time Inference Method Using Artificial Neural Network
    (2017-01-01) Luong H. Vu; Passow, Benjamin N.; Paluszczyszyn, D.; Deka, Lipika; Goodyer, E.
    This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods.
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    Range extended engine management system for electric vehicles: Control design process
    (2014) Paluszczyszyn, D.; Al-Doori, M.; Manning, W.; Elizondo, David; Goodyer, E.
    In this work a research is presented aimed to improve the mechanical performance models used to establish a range-extension methodology, and to introduce the use of computational intelligence to operate a real-time range extension engine management system to replace the current algorithmic approach. This paper describes the initial stage in design of the control strategy, taking into account a number of environmental factors in order to increase the range of series hybrid electric vehicles.
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    Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas
    (Elsevier, 2018-05-26) Orun, A.; Elizondo, David; Goodyer, E.; Paluszczyszyn, D.
    Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system
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    Water advisory demand evaluation and resource toolkit
    (CCWI2016, Amsterdam, 2016-11-07) Paluszczyszyn, D.; Illya, S.; Goodyer, E.; Kubrycht, T.; Ambler, M.
    Cities are living organisms, 24h / 7day, with demands on resources and outputs. Water is a key resource whose management has not kept pace with modern urban life. Demand for clean water and loads on waste water no longer fit diurnal patterns; and they are impacted by events that are outside the normal range of parameters that are taken account of in water management. This feasibility study will determine how the application of computational intelligence can be used to analyse a mix of data inputs to produce credible predictions for clean water demand and foul water outputs in urban areas. The data inputs will be social-media and gas and electricity usage, combined with meteorological and traffic movement data. These will deliver predictions of population density and activity over a subsequent 8 hours period, thus providing inputs to the water supply services on the future demand of fresh water supplies, and the subsequent load on waste water and sewerage systems. The innovation of this concept is the aggregation of social-media data with transport related data to deliver a toolkit that predicts population density in an urban area over the next 8 hours. The toolkit will output the predictions in an open-source manner to support interoperability; thus enabling the development of new applications. For the sake of feasibility study the obtained data sets are localised to Leicester city in United Kingdom. The created online database contains mix of historic and real-time data. Data sources which are monitored and collected in real-time are localised Twitter feeds, current gas and electricity usage on regional level, traffic information from in-situ sensors and from traffic monitoring institutions, weather forecast and rainfall data. To ease the work with such large dataset a graphical user interface was developed in Matlab software and employed capabilities its specialised toolboxes. The online database is based on the Microsoft Azure solution. The computational intelligence model currently developed consist of various topologies of artificial neural networks and support vector machine regression. Note that the final model will comprise at least two models with weighted outputs as initial studies suggested that one model may not capture all the possible trends that characterises the training data for artificial neural network. The created toolkit includes a sensitivity test unit to evaluate the importance or contribution of each of the input variable on the prediction accuracy of the model, and also as a means of comparing our approach with traditional methods of population and water prediction. The toolkit aims to provide predictions for different time intervals, e.g. hourly, daily, monthly and yearly. Embedded within the tool are variants of differential evolutionary and swarm intelligence optimisation algorithms for optimising the meta-parameters of the computational intelligence models and the weights of the combined model. To test the functionality of the developed tool along with appropriateness of the proposed approach for the water demand prediction, data obtained from the SmartSpaces website (http://smartspaces.dmu.ac.uk) were utilised. This website shows the energy performance of a selection of public buildings in Leicester such as De Montfort University campus buildings, Leicester City Council buildings, schools, libraries, leisure centres and others buildings in Leicester. The SmartSpaces website monitors at 30 min intervals temperature, usage of electricity, gas and water within the buildings on the list. While the number of monitored buildings on the SmartSpaces website is limited, it provided a convenient access to the data and thereby enabled development of initial models. For testing the functionality of the toolkit using historic data from the SmartSpace project, the inputs of the artificial neural network and support vector machine models include electricity, gas, temperature and two recent past water demand. The output is the predicted current water demand. The outcomes from the initial study seem promising as the water usage was predicted with an average mean square error of 0.119 in terms of cubic meters.
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    Water Advisory Demand Evaluation and Resource Toolkit
    (2017-09-04) Iliya, S.; Paluszczyszyn, D.; Goodyer, E.; Kubrycht, T.
    The purpose of this feasibility study is to determine if the application of computational intelligence can be used to analyse the apparently unrelated data sources (social media, grid usage, traffic/transportation and weather) to produce credible predictions for water demand. For this purpose the artificial neural networks were employed to demonstrate on datasets localised to Leicester city in United Kingdom that viable predictions can be obtained with use of data derived from the expanding Internet-of-Things ecosystem. The outcomes from the initial study are promising as the water demand can be predicted with accuracy of 0.346 m3 in terms of root mean square error.