Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels

Hybrid Time-Series Forecasting Models for Traffic Flow Prediction

Traffic flow forecast is critical in today’s transportation system since it is necessary to construct a traffic plan in order to determine a travel route. The goal of this research is to use time-series forecasting models to estimate future traffic in order to reduce traffic congestion on roadways. Minimising prediction error is the most difficult task in traffic prediction. In order to anticipate future traffic flow, the system also requires real-time data from vehicles and roadways. A hybrid autoregressive integrated moving av-erage with multilayer perceptron (ARIMA-MLP) model and a hybrid autoregressive integrated moving average with recurrent neural network (ARIMA-RNN) model are proposed in this paper to address these difficulties. The transportation data are used from the UK Highways data-set. The time-series data are preprocessed using a random walk model. The forecasting models autoregressive inte-grated moving average (ARIMA), recurrent neural net-work (RNN), and multilayer perceptron (MLP) are trained and tested. In the proposed hybrid ARIMA-MLP and ARI-MA-RNN models, the residuals from the ARIMA model are used to train the MLP and RNN models. Then the ef-ficacy of the hybrid system is assessed using the metrics MAE, MSE, RMSE and R2 (peak hour forecast-0.936763, non-peak hour forecast-0.87638 on ARIMA-MLP model and peak hour forecast-0.9416466, non-peak hour fore-cast-0.931917 on ARIMA-RNN model)

Predicting space occupancy for street paid parking

This dissertation discusses how to develop a prediction method for on-street parking space availability, using only historical occupancy data collected from on-street multi-space parking meters. It is analyzed how to transform the raw data into a dataset representing the occupancy and how can this information be used to detect when the parking spaces on a street are Vacant or Full. Attributes like weather conditions and holidays are added to the data, giving them more context and comprehension. After the data preparation and analysis, a prediction model is developed using machinelearning techniques that can forecast the availability of the parking spaces on a street at a specific day and on a given moment. For that, a classification method is implemented based on decision trees and neural networks, comparing both methods regarding results and development time. Particular attention is given to the algorithm parameters, to achieve the right balance between accuracy and computational time. The developed model proved effective, correctly capturing the different behavior of each street through the different weeks, and returning results useful to drivers searching for parking and to the business owners while monitoring their parking investments and returns.Esta dissertação apresenta como pode ser desenvolvido um método para previsão de disponibilidade de lugares de estacionamento em rua, utilizando dados históricos obtidos através de parquímetros de controlo a múltiplos lugares. É analisado como os dados em bruto dos parquímetros podem ser transformados num conjunto de dados que represente qual a ocupação dos lugares, e posteriormente como esta informação pode ser utilizada para detetar se o estacionamento em uma rua está livre ou ocupado. São adicionados também mais alguns atributos, como por exemplo informação sobre as condições meteorológicas ou que dias são feriados, dando mais algum contexto e compreensão à informação já existente. Após a preparação e análise dos dados, é desenvolvido um método de previsão utilizando técnicas de aprendizagem automática de modo a que seja possível saber qual a disponibilidade de estacionamento em uma rua, a um dia específico e a um determinado momento. Para isso, foi implementado um método de classificação baseado em árvores de decisão e redes neuronais, comparando ambos os métodos do ponto de vista dos resultados e do tempo de desenvolvimento. Foi dada especial atenção aos parâmetros utilizados em cada algoritmo, de modo a que haja um balanço entre a precisão e tempo de computação. O modelo desenvolvido mostrou ser eficaz, captando corretamente o comportamento de cada rua nas diferentes semanas, devolvendo resultados uteis aos condutores que procurem lugares de estacionamento e aos proprietários do negócio por lhes permitir monitorizar o desempenho dos seus investimentos em parques de estacionamento e qual o retorno

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

Microgrids/Nanogrids Implementation, Planning, and Operation

Today’s power system is facing the challenges of increasing global demand for electricity, high-reliability requirements, the need for clean energy and environmental protection, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed ones. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. The energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid. Microgrids and nanogrids can allow for better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore increasing its efficiency and resilience to natural and technical disruptive events. Microgrid networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings. In this challenging context, the objective of this book is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues

Pervasive Data Analytics for Sustainable Energy Systems

With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates efforts to balance supply and demand, since their generation is intermittent and unpredictable. Traditionally, it has always been the supply side that has adapted to follow energy demand, however, in order to have a sustainable energy system for the future, the demand side will have to be better managed to match the available energy supply. In the first part of this thesis, we focus on understanding customers' energy consumption behavior (demand analytics). While previously, information about customer's energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we then develop a versatile customer segmentation framework, track cluster changes over time, and identify key characteristics that define a cluster. Additionally, although household-level consumption is hard to predict, it can be used to improve aggregate-level forecasting by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the customer base. Furthermore, we develop an approach to model the uncertainty of future demand. In contrast to previous work that used computationally expensive methods, such as simulation, bootstrapping, or ensemble, we construct prediction intervals directly using the time-varying conditional mean and variance of future demand. While analytics on customer energy data are indeed essential to understanding customer behavior, they could also lead to breaches of privacy, with all the attendant risks. The first part of this thesis closes by exploring symbolic representations of smart meter data which still allow learning algorithms to be performed on top of them, thus providing a trade-off between accurate analytics and the protection of customer privacy. In the second part of this thesis, we focus on mechanisms for incentivizing changes in customers' energy usage in order to maintain (electricity) grid stability, i.e., Demand Response (DR). We complement previous work in this area (which typically targeted large, industrial customers) by studying the application of DR to residential customers. We first study the influence of DR baselines, i.e., estimates of what customers would have consumed in the absence of a DR event. While the literature to date has focused on baseline accuracy and bias, we go beyond these concepts by explaining how a baseline affects customer participation in a DR event, and how it affects both the customer and company profit. We then discuss a strategy for matching the demand side with the supply side by using a multiunit auction performed by intelligent agents on behalf of customers. The thesis closes by eliciting behavioral incentives from the crowd of customers for promoting and maintaining customer engagement in DR programs

Power Electronics Applications in Renewable Energy Systems

The renewable generation system is currently experiencing rapid growth in various power grids. The stability and dynamic response issues of power grids are receiving attention due to the increase in power electronics-based renewable energy. The main focus of this Special Issue is to provide solutions for power system planning and operation. Power electronics-based devices can offer new ancillary services to several industrial sectors. In order to fully include the capability of power conversion systems in the network integration of renewable generators, several studies should be carried out, including detailed studies of switching circuits, and comprehensive operating strategies for numerous devices, consisting of large-scale renewable generation clusters

Projects in Applied Data Science: Fall 2019

This document contains semester projects for students in CSCI 4381/7000 Data ScienceProjects. This course explores concepts and techniques for design, formulation and execution ofpractical, applied data science. Topics covered include experimental design, statistical analysisand predictive modeling, machine learning, data visualization, scientific writing and presentation.During the class, students selected a semester-long project to acquire, analyze, and understanddata in support of a research question. In addition to traditional lectures, students read anddiscussed published papers on data science topics, practiced skills in recitation sessions, andentertained guest lectures from expert data scientists in the field. Outside of these readings andrecitations, students were allowed to work on their projects exclusively and were supported withmeetings, peer-discussion and copyediting. In terms of the scope of the final product, undergraduate students were asked to perform aresearch or engineering task of some complexity while graduate students were additionallyrequired to perform a survey of related work, demonstrate some novelty in their approach, anddescribe the position of their contribution within the broader literature. All students whoperformed at or above these expectations were offered the opportunity to contribute their paperfor publication in this technical summary. The diversity of the papers herein is representative of the diversity of interests of the students inthe class. There is no common trend among the papers submitted and each takes a differenttopic to task. Students made use of open data or worked with organizations to acquire data.Several students pivoted their projects early on due to limitations and difficulties in data access--- a real-world challenge in practical data science. The projects herein range from analyzingtraffic in cities, restaurant trends and Facebook responses to smartphone accelerometer data,scaling laws in higher education, and bicycle trends in Boulder, Colorado. Analysis approachesare similarly varied: visualization, statistical analysis and modeling, machine learning,reinforcement learning, etc.. Most papers can be understood as exploratory data analysis,although some emphasize interactive visualization and others emphasize statistical modelingand prediction aimed at testing a well-defined research question. To inform the style of theirapproach, students read papers from a broad sampling of original research. They used thesereadings to build an understanding of approaches to presentation and analysis in the modernscientific literature. One paper was held out from this compendium so that it could be submittedfor publication to a peer-reviewed venue. Please direct questions/comments on individual papers to the student authors when contactinformation has been made available.</p