JOINT WORKSHOP PROCEEDINGS

Program Chairs

• Abdul Sattar (Griffith University, Australia) • David Hansen (CSIRO Australian e-Health Research Centre, Australia) Workshop Chair

• Sankalp Khanna (CSIRO Australian e-Health Research Centre, Australia) Senior Program Committee • Aditya Ghose (University of Newcastle, Australia) • Jim Warren (University of Auckland, New Zealand) • Wayne Wobcke (University of New South Wales, Australia) • Mehmet Orgun (Macquarie University, Australia) • Yogesan (Yogi) Kanagasingam (CSIRO Australian e-Health Research Centre, Australia) Program Committee • Simon McBride (CSIRO AEHRC) • • Adam Dunn (University of New South Wales) • • Stephen Anthony (University of New South •

Wales) • • Lawrence Cavedon (Royal Melbourne Institute •

of Technology / NICTA) • • Diego Mollá Aliod (Macquarie University) • • Michael Lawley (CSIRO AEHRC) • • Anthony Nguyen (CSIRO AEHRC) • • Amol Wagholikar (CSIRO AEHRC) • Bevan Koopman (CSIRO AEHRC) Key Sponsors • CSIRO Australian e-Health Research Centre • Institute for Integrated and Intelligent Systems, Griffith University Kewen Wang (Griffith University) Vladimir Estivill-Castro (Griffith University) John Thornton (Griffith University) Bela Stantic (Griffith University) Byeong-Ho Kang (University of Tasmania) Justin Boyle (CSIRO AEHRC) Guido Zuccon (CSIRO AEHRC) Hugo Leroux(CSIRO AEHRC)

Alejandro Metke (CSIRO AEHRC)

CARE 2013 - ACKNOWLEDGEMENTS

Supporting Organisations

• The Australasian College of Health Informatics • The Australasian Medical Journal • The Australasian Telehealth Society

Classification Models in Intensive Care Outcome Prediction-can we improve on current models?

Nicholas Barnes, Lynette Hunt, and Michael Mayo

Towards a visually enhanced medical search engine

Lavish Lalwani, Guido Zuccon, Mohamed Sharaf and Anthony Nguyen

Using Fuzzy Logic for Decision Support in Vital Signs Monitoring

Shohas Dutta, Anthony Maeder and Jim Basilakis

A Novel Approach for Improving Chronic Disease Outcomes using Intelligent

Personal Health Records in a Collaborative Care Framework

Amol Wagholikar

Partially automated literature screening for systematic reviews by modelling nonrelevant articles

Henry Petersen, Josiah Poon, Simon Poon, Clement Loy and Mariska Leeflang

Optimizing Shiftable Appliance Schedules across Residential Neighbourhoods for

Lower Energy Costs and Fair Billing

Salma Bakr and Stephen Cranefield

Proposal of information provision to probe vehicles based on distribution of link travel time that tends to have two peaks

Keita Mizuno, Ryo Kanamori, and Takayuki Ito

AIH 2013 SHORT PAPERS

CARE 2013 FULL PAPERS

3 5 22 29 34 43 45 Health Informatics and Artificial Intelligence solutions: Addressing the Challenges at the Frontiers of Modern Healthcare

Keynote Address Professor Michael Blumenstein

Griffith University, Australia [e-mail] Speaker Profile

Michael Blumenstein is a Professor and Head of the School of Information and Communication Technology at Griffith University, where he previously served as the Dean (Research) in the Science, Environment, Engineering and Technology Group. In addition, Michael currently serves as the Leader for the Health Informatics Flagship Program at the Institute for Integrated and Intelligent Systems.

Michael is a nationally and internationally recognised expert in the areas of automated Pattern Recognition and Artificial Intelligence, and his current research interests include Document Analysis, MultiScript Handwriting Recognition and Signature Verification. He has published over 132 papers in refereed books, conferences and journals. His research also spans various projects applying Artificial Intelligence to the fields of Engineering, Environmental Science, Neurobiology, Coastal Management and Health. Michael has secured internal/nationally competitive research grants to undertake these projects with funds exceeding AUD$4.3 Million. Components of his research into the predictive assessment of beach conditions have been commercialised for use by local government agencies, coastal management authorities and in commercial applications.

Following his achievements in applying Artificial Intelligence to the area of bridge engineering (where he has published widely and has been awarded federal funding), he was invited to serve on the International Association for Bridge and Structural Engineering’s Working Commission 6 to advise on matters pertaining to Information Technology. Michael is the first Australian to be elected onto this committee. In addition, he was previously the Chair of the Queensland Branch of the Institute for Electrical and Electronic Engineers (IEEE) Computational Intelligence Society. He is also the Gold Coast Chapter Convener and a Board Member of the Australian Computer Society's Queensland Branch Executive Committee as well as the Chairman of the IT Forum Gold Coast and a Board Member of IT Queensland. Michael currently serves on the Australian Research Council's (ARC) College of Experts on the Engineering, Mathematics and Informatics (EMI) panel. In addition, he has recently been elected onto the Executive of the Australian Council of Deans of Information and Communication Technology (ACDICT). Michael also serves on a number of Journal Editorial Boards and has been invited to act as General Chair, Organising Chair, Program Chair and/or Committee member for numerous national/international conferences in his areas of expertise.

In 2009 Michael was named as one of Australia’s Top 10 Emerging Leaders in Innovation in the Australian’s Top 100 Emerging Leaders Series supported by Microsoft. Michael is a Fellow of the Australian Computer Society and a Senior Member of the IEEE.

Numerous challenges currently exist in the Health Sector such as effective treatment of patients with chronic diseases, early diagnosis and prediction of health conditions, patient data administration and adoption of electronic health records, strategic planning for hospitals and engagement of health professionals in training. This presentation focuses on these challenges and examines some innovative Health Informatics solutions with prospective deployment of automated artificial intelligence tools to augment current practices.

Some challenges are examined at a brand new University Hospital in Queensland, whereby a number of automated solutions are investigated using technology and intelligent approaches such as mobile devices for understanding patient chronic health conditions over time, image analysis and pattern recognition for the early diagnosis and treatment of such brain disorders as Parkinson's disease, social media analytics for patient engagement in the adoption of electronic health records, online collaborative tools for strategic planning in the hospital and the use of 3D virtual worlds for realistic training and professional development for medical staff.

Finally, the presentation will conclude with a discussion about the emerging "Research Triangle" present at the Gold Coast, in Queensland, which includes the new Gold Coast University Hospital and is directly adjacent to Griffith University's Gold Coast campus with proximity to the emerging Health and Knowledge Precinct. This special zone presents a unique opportunity to nurture cutting edge healthrelated research intersecting information technology in collaboration with industry and government, which may have a profound impact on the future landscape of Health Informatics innovation in the region. Classification Models in Intensive Care Outcome Prediction-can we improve on current models? Nicholas A. Barnes,

Intensive Care Unit, Waikato Hospital, Hamilton, New Zealand.

Lynnette A. Hunt,

Department of Statistics, University of Waikato, Hamilton, New Zealand.

Michael M. Mayo,

Department of Computer Science, University of Waikato, Hamilton, New Zealand.

Corresponding Author: Nicholas A. Barnes.

Abstract Classification models (“machine learners” or “learners”) were developed using machine learning techniques to predict mortality at discharge from an intensive care unit (ICU) and evaluated based on a large training data set from a single ICU. The best models were tested on data on subsequent patient admissions. Excellent model performance (AUCROC (area under the receiver operating curve) =0.896 on a test set), possibly superior to a widely used existing model based on conventional logistic regression models was obtained, with fewer perpatient data than that model. 1

Introduction

Intensive care clinicians use explicit judgement and heuristics to formulate prognoses as soon as reasonable after patient referral and admission to an intensive care unit [ 1 ].

Models to predict outcome in such patients have been in use for over 30 years [ 2 ] but are considered to have insufficient discriminatory power for individual decision making in a situation where patient variables that are difficult or impossible to measure may be relevant. Indeed even variables that have little or nothing to do with the patient directly (such as bed availability or staffing levels [ 3 ]) may be important in determining outcome.

There are further challenges for model development. Any model used should be able to deal with the problem of class imbalance, which refers in this case to the fact that mortality should be much less common than survival. Many patient data are probably only loosely or indeed not related to outcome and many are highly correlated. For example, elevated measurements of serum urea, creatinine, urine output, diagnosis of renal failure and use of dialysis will all be closely correlated.

Nevertheless, models are used to risk adjust for comparison within an institution over time or between institutions, and model performance is obviously important if this is to be meaningful. It is also likely that a model with excellent performance could augment clinical assessment of prognosis. Furthermore, a model that performs well while requiring fewer data would be helpful as accurate data acquisition is an expensive task.

The APACHE III-J (Acute Physiology and Chronic Health Evaluation revision IIIJ [ 4 ]) model is used extensively within Australasia by the Centre for Outcomes Research of the Australian and New Zealand Intensive Care Society (ANZICS) and a good understanding of its local performance is available in the published literature [ 4 ]. It should be noted that death at hospital discharge is the outcome variable usually considered by these models. Unfortunately the coefficients for all variables for this model are no longer in the public domain so direct comparison with new models is difficult. The APACHE (Acute Physiology and Chronic Health Evaluation) models are based largely on baseline demographic and illness data and physiological measurements taken within the first day after ICU admission.

This study aims to explore machine learning methods that may outperform the logistic regression models that have previously been used.

The reader may like to consult a useful introduction to the concepts and practice of machine learning [ 5 ] if terms or concepts are unfamiliar. 2

Methods

The study is comprised of three parts: 1. An empirical exploration of raw and processed admission data with a variety of attribute selection methods, filters, base classifiers and metalearning techniques (which are overarching models that have other methods nested within them) that were felt to be suitable to develop the best classification models. Metamodels and base classifiers may be nested within other metamodels and learning schemes can be varied in very many ways .These experiments are represented below in Figure 1 where we used up to two metaclassifiers with up to two base classifiers nested within a metaclassifier. Choose Dataset

Metamodel 1

Metamodel 2 Base Classifier (s)

Evaluate Classifier Results 2. Further testing with the best performing data set (full unimputed training set) and learners with manual hyperparameter setting. A hyperparameter is a particular model configuration that is selected by the user, either manually or following an automatic tuning process. This is represented in a schematic below: 3. Testing of the best models from phase 2 above on a new set of test data to better understand generalizability of the models. This is depicted in Figure 3 below. Matching Test Set

Four Best Models based on 4 Evaluation

Measures

The training data for adult patients (8122 patients over 16 years of age) were obtained from the database of a multidisciplinary ICU in a tertiary referral centre from a period between July 2004 and July 2012.Data extracted were comprised of a demographic variable (age), diagnostic category (with diagnostic coefficient from the APACHE III-J scoring system, including ANZICS modifications), and an extensive list of numeric variables relating to patient physiology and composite scores based on these, along with the classification variable: either survival, or alternatively, death at ICU discharge (as opposed to death at hospital discharge as in the APACHE models). Much of the data collected is used in APACHE III-J model mentioned above, and represents a subset of the data used in that model. Training data, prior to the imputation process, but following discretization of selected variables are represented in Table 1. Test data for the identical variable set were obtained from the same database for the period July 2012 to March 2013.

Of particular interest is that the data is clearly class imbalanced with mortality during ICU stay of approximately 12%. This has important implications for modelling the data.

There were many strongly correlated attributes within the data sets. Many of the model variables are collected as highest and lowest measures within twenty four hours of admission to the ICU. Correlated variables may bring special problems with conventional modelling including logistic regression. The extent of correlation is demonstrated in Figure 4.

Fig. 5. Patterns of missing data in the raw training set. Missing data is represented by red colouration.

Missing numeric data in the training set was imputed using multiple imputation with the R program [ 6 ] and the R package Amelia [ 7 ], which utilises bootstrapping of non-missing data followed by imputation by expectation maximisation. We initially used the average of five multiple imputation runs.

Using the last imputed set was also trialled, as it may be expected to be the most accurate based on the iterative nature of the Amelia algorithm. No categorical data were missing. Date of admission was discretized to the year of admission, age was converted to months of age, and the diagnostic categories were converted to five to eight (depending on study phase) ordinal risk categories by using coefficients from the existing APACHE III-J risk model.

A summary of data is presented below in Table 1.

Phase 1 consisted of an exploration of machine learning techniques thought suitable to this classification problem, and in particular those thought to be appropriate to a class imbalanced data set. Attribute selection, examining the effect of using imputed and unimputed data sets and application of a variety of base learners and metaclassifiers without major hyperparameter variation occurred in this phase. The importance of attributes was examined in multiple ways including using random forest methodology for variable selection, using improvement in Gini index using particular attributes. This information is displayed in figure 6. riables used in the study are ranked by their contribution to Gini index.

A comprehensive evaluation of all techniques is nearly impossible given the enormous variety of techniques and the ability to combine up to several of these at a time in any particular model. Techniques were chosen based on the likely success of their application. WEKA [ 8 ] was used to apply learners and all models were evaluated with tenfold cross validation. WEKA default settings were commonly used in phase 1 and the details of these defaults are widely available [ 9 ]. Unless otherwise stated all settings in all study phases were the default settings of WEKA for each classifier or filter. Two results were used to judge overall model performance during phase 1. These were: 1. Area under the receiver operating curve (AUC ROC) 2. Area under the precision recall curve (AUC PRC) The results are presented in Table 3 in the results section.

Phase 2 of our study involved training and evaluation on the same data sets with learners that had performed well in phase 1. Hyperparameters were mostly selected manually, as automatic hyperparameter selection in any software is limited and hampered by a lack of explicitness. Class imbalance issues were addressed with appropriate WEKA filters (spread subsample and SMOTE, a filter which generates a synthetic data set to balance the classes [ 10 ]), or the use of cost sensitive learners [ 11 ]. Unless otherwise stated in Table 3, WEKA default settings were used for each filter or classifier. Evaluation of these models proceeded with tenfold cross-validation and the results were examined in light of four measures: 1. Area under the receiver operating curve with 95% confidence intervals by the method of Hanley and McNeill [ 12 ] 2. Area under the precision recall curve 3. Matthews correlation coefficient and, 4. F-measure Additionally, scaling the quantitative variables by standardizing or normalizing the data was explored as this is known to sometimes improve model performance [ 13 ]. The results of phase 2 are presented in Table 2 in the results section.

Phase 3 involved evaluating the accuracy of the best classification models from phase 2 on a new test set of 813 patient admissions. Missing data in the test set were not imputed. Results are shown in Table 3. 3

Results

Base classifier 2 NA NA

ROC

PRC Preprocess Spread subsample uniform NA Spread subsample uniform Spread subsample uniform Spread subsample uniform Spread subsample uniform Spread subsample uniform NA NA NA NA NA NA NA Spread subsample Spread subsample Spread subsample best of all models on any of the four classification methods is shaded in red to emphasise that no one performance measure dominates a classifier’s overall utility. J48 graft NA NA NA NA NA NA J48 NA NA NA

NA 0.901 (0.881,0.921) NA Spread subsample uniform Spread subsample Spread subsample Spread subsample Spread subsample uniform Spread subsample uniform 0.888 (0.864,0.912)

Normalizing or standardizing the data did not improve model performance and indeed tended to moderately worsen it.

Table 4 presents the results of applying four of the best models from phase 2 on a test data set of 813 patient admissions which should be from the same population distribution (if date of admission is not a relevant attribute). Evaluation is based on AUC ROC, AUC PRC, Matthews’s correlation coefficient and F-measure. These evaluations were obtained by WEKA’s knowledge flow interface. NA NA NA

ROC 0.896 0.893 ROC-area under receiver operating characteristic curve CI-confidence interval PRC-area under precision-recall curve MCC-Matthews correlation coefficient

F-meas-F-measure 4

Discussion

It is unrealistic to expect models to perfectly represent such a complex reality as that of survival from critical illness. Perfect classification is impossible because of the limitations of any combination of currently available measurements made on such patients to accurately reflect survival potential. Patient factors such as attitudes towards artificial support and presumably health practitioner and institution related factors are important. Additionally non-patient related factors which may be purely logistical will continue to thwart perfect prediction by any future model. For instance, a patient may die soon after discharge from the ICU if a ward bed is available and conversely will die within the ICU if a ward bed is not available and transfer cannot proceed. Models currently employed generally consider death at hospital discharge, but new factors that increase randomness can enter in the hospital stay following ICU discharge, so problems are not necessarily decreased with this approach.

The best models we have studied have excellent performance when evaluated following tenfold cross validation in the single ICU setting with use of fewer data points than the current gold standard model. Machine learning techniques usually make few distributional assumptions about the data when compared with the traditional logistic regression model. Missing data are often dealt with effectively with machine learning techniques while complete cases are generally used in traditional general linear modelling such as logistic regression. Clinical data will never be complete, as some data will not be required for a given patient, while some patients may die prior to collection of data which cannot subsequently be obtained. Imputation may be performed on data prior to modelling but has limitations. It is interesting that models trained on unimputed data tend to perform better than imputed data, both in phase 2 and with the test set in phase 3.

The best comparison we can make in the published literature is the work of Paul et al [ 4 ] which demonstrates that the AUC ROC of the APACHE-III-J model has varied between 0.879 and 0.890 when applied to over half a million adult admissions to Australasian ICUs between 2000 and 2009. Routine exclusions in this study included readmissions, transfers to other ICUs, and missing outcome and other data, and admission post coronary artery bypass grafting prior to introduction of the ANZICS modification to APACHE-III-J for this category. None of these were exclusions in our study. The Paul et al paper looks at outcome at hospital discharge, while ours examines outcome at ICU discharge. For these reasons the results are not directly comparable but our results for AUC ROC of up to 0.896 on a separate validation set clearly demonstrate excellent model performance.

The techniques associated with the best performance involve addressing class imbalance (i.e. pre-processing data to create a dataset with similar numbers of those who survive and those that die). This class imbalance is a well-known problem in classification. Mortality data from any healthcare setting tend to be class imbalanced. Our study shows that any approach to class imbalance in the data greatly enhance model performance. Cost sensitive metalearners [ 11 ], synthetic minority generation techniques (SMOTE [ 10 ]) and creating a uniform class distribution by subsampling across the data all improve model performance.

A cost sensitive learner indicates a technique that reweights cases according to a cost matrix that the user sets to reflect differing “cost” of misclassification of positive and negative cases. This intuitively lends itself to the intensive care treatment process where such a framework is likely implemented at least subconsciously by the intensive care clinician. For instance the cost of clinically “misclassifying” a patient may be substantial and clinicians would likely try hard to avoid this situation. In our study, the ensemble learner random forests [ 14 ] with or without a technique to address class imbalance tends to outperform many more complex metalearners, or enhancements of single base classifiers such as bagging [ 15 ] and boosting [ 16 ]. Random forests involve generation of many different tree models, each of which splits the cases based on different variables and a criterion to increase information gain. Voting then occurs across the “forest” to decide on the best way to split the cases and this produces the model. The term ensemble simply represents the fact that multiple learners are involved, rather than a single tree. As many as 500 or 1000 trees are commonly required before the error of the forest is at a minimum. The number of variables to be considered by each tree may also be set to try and improve performance. The other techniques that produced excellent results were rotation forests either alone, with a cost sensitive classifier, or in combination with a technique known as alternating decision tree. Alternating decision tree takes a “weak” classifier (such as a tree classifier) and uses a technique similar to boosting to improve performance. The reason extensive experimentation may be required to produce the best model is attributed to Wolpert [ 17 ] and described as the “no free lunch theorem”, meaning that there is no one single technique that will model the best in every given scenario. Of course the same is true of any conventional statistical technique applied to multidimensional problems. Data processing and model selection are crucial to performance although if prediction alone is important, a pragmatic approach can be taken to the usual statistical assumptions. Machine learning techniques are generally not a “black box” approach however and deserve the same credibility as any older method, if application is appropriate.

Similarly, no single evaluation measure can summarize a classifier’s performance and different model strengths and weaknesses may be more or less tolerable depending on the circumstances of model use and hence a range of measures are usually presented as we have done.

There are several weaknesses to our study. It is clearly from a single centre and may not generalize to other ICUs in other healthcare systems. Mortality remains a

Initial Screening on Title Alone Obtain Title and Abstract RSTceArivbteilseeetwnraasencordtn1 RSTceArivbteilseeetwnraasencordtn2 Reviewer 1 Screens on Ful Text

Reviewer 2 Screens on

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Include (a)

Exclude Exclude Exclude { 'neutropenia', but 'infection' or 'thorax'

not { 'skin' but not 'thorax' { 'immunoglobulin g' { 'animals' { 'drug therapy', but not 'risk' or 'infection' (b)

Exclude Obtain Title and Abstract

Initial Screening on Title Alone Obtain Title and Abstract Reviewer 1 Screens on Title and Abstract

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Text (c) are then represented as Boolean statements over these terms, and interpretable rules are then generated using Boolean minimisation (examples of rules are given in 1b Review authors can then re ne the classi er by selecting only those rules most likely to describe non-relevant studies, maximising overall precision.

Preliminary experiments simulating the process outlined in Figure 1c on a previously conducted systematic review indicate that as many as 25% of articles can be safely eliminated without the need for screening by a second reviewer. The evaluation does assume that all false positives (studies erroneously excluded by the generated rules) were included by the rst reviewer. Such an assumption is reasonable; the reason for multiple reviewers is that even human experts make mistakes. A study comparing the precision of our classi er to human reviewers is planned. In addition, future work will focus on improving the quality of the generated rules by trying to better capture reasons for excluding studies matching those used by human reviewers. Optimizing Shiftable Appliance Schedules across Residential Neighbourhoods for Lower Energy Costs and Fair Billing

Salma Bakr and Stephen Crane eld Department of Information Science, University of Otago, Dunedin, New Zealand [e-mail], [e-mail] Abstract. This early stage interdisciplinary research contributes to smart grid advancements by integrating information and communications technology and electric power systems. It aims at tackling the drawbacks of current demand-side energy management schemes by developing an agent-based energy management system that coordinates and optimizes neighbourhood-level aggregate power load. In this paper, we report on the implementation of an energy consumption scheduler for rescheduling \shiftable" household appliances at the household-level; the scheduler takes into account the consumer's time preferences, the total hourly power consumption across neighbouring households, and a fair electricity billing mechanism. This scheduler is to be deployed in an autonomous and distributed residential energy management system to avoid load synchronization, reduce utility energy costs, and improve the load factor of the aggregate power load. 1 Electric utilities tend to meet growing consumer energy demand by expanding their generation capacities, especially capital-intensive peak power plants (also known as \peakers"), which are much more costly to operate than base load power plants. As this strategy results in highly ine cient consumption behaviours and under-utilized power systems, demand-side energy management schemes aiming to optimally match power supply and demand have emerged.

Currently deployed demand-side energy management schemes are based on the interactions between the electric utility and a single household [ 18 ], as in Fig.1(a). As this approach lacks coordination among neighbouring households sharing the same low-voltage distribution network, it may cause load synchronization problems where new peaks arise in o -peak hours [ 15 ]. Thus, it is essential to develop exible and scalable energy management systems that coordinate energy usage between neighbouring households, as in Fig.1(b). 2

Background The smart grid, or the modernized electric grid, is a complex system comprising a number of heterogeneous control, communication, computation, and electric Fig. 1. The interactions between the utility and the consumers in demand-side energy management schemes are either: (a) individual interactions, or (b) neighbourhood-level interactions power components. It also integrates humans in decision making. To verify the states of smart grid components in a simultaneous manner and take human intervention into account, it is necessary to adopt autonomous distributed system architectures whose functionality can be modelled and veri ed using agent-based modelling and simulation.

Multi-agent systems (MAS) provide the properties required to coordinate the interactions between smart grid components and solve complex problems in a exible approach. In the context of a smart grid, agents represent producers, consumers, and aggregators at di erent scales of operation, e.g. wholesale and retail energy traders [ 7 ]. MAS have been deployed in a number of smart grid applications, with a more recent focus on micro-grid control [ 6, 17 ] and energy management [ 10, 12 ] especially due to the emerging trend of integrating distributed energy resources (DER), storage capacities, and plug-in hybrid electric vehicles (PHEV) into consumer premises.

In agent-based energy management systems, agents may aim at achieving a single objective or a multitude of objectives; typical objectives include: balancing energy supply and demand [ 4 ]; reducing peak power demand [ 13, 16 ]; reducing utility energy costs [ 8, 16 ] and consumer bills [ 16 ]; improving grid e ciency [ 4 ]; and increasing the share of renewable energy sources [ 1, 12 ] which consequently reduces the carbon footprint of the power grid. Agent objectives can be achieved using evolutionary algorithms [ 8 ] or a number of optimization techniques such as integer, quadratic [ 5, 13 ], stochastic [ 4 ] and dynamic programming [ 5 ]. As for the interactions among agents, game theory provides a conceptual and a formal analytical framework that enables the study of those complex interactions [ 19 ]. This research aims at optimizing the energy demand of a group of neighbouring households, to reduce utility costs by using energy at o -peak periods, avoid load synchronization that may occur due to rescheduling appliance usage, and improve the load factor (i.e. the ratio between average and peak power) of the aggregate load. A number of energy consumption schedulers have been proposed in the literature [ 14, 16, 21 ]; however, those schedulers do not leverage an accurately quanti ed and fair billing mechanism that charges consumers based on the shape of their power load pro les and their actual contribution in reducing energy generation costs for electric utilities [ 3 ]. In this paper, we implement and evaluate an energy consumption scheduler that optimizes the operation times of three wet home appliances and a PHEV at the household-level based on the total hourly power consumption across neighbouring households, consumer time preferences, and a fair electricity billing mechanism. 4

Methodology We use the ndings of Baharlouei et al. [ 3 ] to resolve a gap in the ndings of Mohsenian-Rad et al. [ 16 ]. Game-theoretic analysis is used by Mohsenian-Rad et al. [ 16 ] to propose an incentive-based energy consumption game that schedules \shiftable" home appliances (e.g. washing machine, tumble dryer, dish washer, and PHEV) for residential consumers (players) according to their daily time preferences (strategies); at the Nash equilibrium of the proposed non-cooperative game, it is shown that the energy costs of the system are minimized. However, this game charges consumers based on their total daily electric energy consumption rather than their hourly energy consumption. In other words, two consumers having the same total daily energy consumption are charged equally even if their hourly load pro les are di erent. This unfair billing mechanism may thus discourage consumer participation as it does not take consumer rescheduling exibility into consideration. With this in mind, we propose leveraging the fair billing mechanism recently proposed by Baharlouei et al. [ 3 ] to encourage consumer participation in the energy consumption game. 5

Energy Consumption Scheduler

Formulation Assuming a multi-agent system for managing electric energy consumption at the neighbourhood-level, where agents represent consumers, each agent locally and optimally schedules his \shiftable" home appliances to minimize his electricity bill taking into account the following inputs: appliance load pro les, consumer time preferences, grid limitations (if any), aggregate scheduled hourly energy consumption of all the other agents in the neighbourhood, and the deployed electricity billing scheme. If the energy cost function is non-linear, knowing the aggregate scheduled load is required for optimization.

After this optimization, each agent sends out his updated appliance schedule to an aggregator agent, which then forwards the aggregated load to the other agents to optimize their schedules accordingly. By starting with random initial schedules, convergence of the distributed algorithm is guaranteed if householdlevel energy schedule updates are asynchronous [ 16 ]. The electric utility may coordinate such updates according to any turn-taking scenario.

We assume electricity distributed to the neighbourhood is generated by a thermal power generator having a quadratic hourly cost function [ 23 ] given by (1); as this equation is convex, quadratic, and has linear constraints, it can be solved using mixed integer quadratic programming.

Ch (Lh) = ahL2h + bhLh + ch; where ah > 0, and bh, ch 0 at each hour h 2 H = [1; :::; 24]. In (2), Lh and xhm denote the total hourly load of N consumers and consumer m, respectively [ 16 ].

Lh =

N X xhm; m=1

To encourage participation in energy management programmes, it is essential to reward consumers with fair incentives. By rescheduling appliances to o -peak hours where electricity tari s are cheaper, we save on utility energy costs and consequently impose monetary incentives for consumers in the form of savings on electricity bills. The optimization problem therefore targets the appliance schedule xhn that results in the minimum bill Bn for consumer (agent) n. The billing equation proposed by Baharlouei et al. [ 3 ], which fairly maps a consumer's bill to energy costs (1), is given by (3).

H Bn = X h=1 xh

n PNm=1 xhm

Ch

N ! X xm ;

h m=1 (1) (2) (3) To model the optimization problem such that each agent n individually and iteratively minimizes (3), we use YALMIP | an open-source modelling language that integrates with MATLAB. We consider a system of three households (agents) and investigate the behaviour of one of those schedulers with respect to fair billing, lower energy costs, and improved load factor. To model consumer exibility in scheduling, we consider two scenarios for the same household where the consumer's acceptance of rescheduling exibility di er. We investigate the two scenarios for four days in December, March, June and September.

To test our energy consumption scheduler, we choose to schedule a PHEV and three wet appliances: a clothes washer, a tumble dryer, and a dish washer. Wet appliance power load pro les are based on survey EUP14-07b [ 22 ], which was conducted with around 2500 consumers from 10 European countries. For the PHEV load, we use the power load pro le of a mid-size sedan at 240V{30A [ 9 ].

We choose a budget-balanced billing system and calibrate the coe cients of the hourly energy cost function (1) against a three-level time-of-use pricing scheme used by London Hydro [ 11 ], where the kilowatt-hour is charged at 12.4, 10.4, and 6.7 cents for on-, mid-, and o -peak hours, respectively. Energy consumption of neighbouring households and non-shiftable loads of the household investigated are taken from a publicly available household electric power consumption data set [ 2 ], for the period from December 2006 to September 2007. 5.3

Scenario 1 In this scenario, we assume the consumer is not exible about appliance scheduling and use common startup times: clothes washing starts at 7 a.m., drying starts two hours directly after washing starts, dish washing starts at 6 p.m. [ 22 ], and PHEV recharging starts at 6 p.m. [ 20 ].

The consumer is assumed to be exible about appliance scheduling in Scenario 2; clothes washing starts any time between 6 a.m. and 9 a.m., drying any time after washing but before 11 p.m., washing dishes any time after 7 p.m, but before 11 p.m., and PHEV recharging any time after 1 a.m. but before 5 a.m. Results indicate that the consumer's electricity bill for operating household \shiftable" appliances in Scenario 2 is lower by 70%, 57%, 32%, and 65% compared to that in Scenario 1 for the days chosen in December, March, June, and September, respectively. This clearly indicates that exibility is awarded fairly through the deployed billing mechanism. Figures 2 and 3 depict the appliance schedules resulting in the minimum bill for the household under investigation and the aggregate non-shiftable load of neighbouring households, for Scenario 1 and 2 in December, respectively. As we chose a budget-balanced billing system and since appliances are rescheduled to cheaper o -peak hours, utility energy costs are lower in Scenario 2 by 70%, 57%, 32%, and 65% compared to that in Scenario 1, for the days chosen across the four seasons, respectively. Fig. 2. Scenario 1: the unscheduled \shiftable" appliance loads of the consumer under investigation and the aggregate \non-shiftable" neighbourhood-level loads (December) 1 Fig. 3. Scenario 2: the scheduled \shiftable" appliance loads of the consumer under investigation and the aggregate \non-shiftable" neighbourhood-level loads (December) 6.3

Improved Load Factor As the \shiftable" appliances of the household under investigation are rescheduled to operate during o -peak hours instead of peak hours, the load factor of the aggregate load in Scenario 2 is improved by 44%, 13%, 19%, and 28% compared to that in Scenario 1, for the days chosen across the four seasons, respectively. This indicates improved resource allocation in the power grid. 7

Conclusion In this paper, we leverage the fair billing mechanism proposed by Baharlouei et al. [ 3 ] to evaluate the energy consumption scheduling game proposed by Mohsenian-Rad et al. [ 16 ]. We have implemented and evaluated a scheduler that optimally allocates the operation of \shiftable" appliances for a consumer based on his time preferences, the aggregate hourly \non-shiftable" load at the neighbourhood-level, and a fair billing mechanism. As the deployed billing mechanism takes advantage of cheaper o -peak electricity prices, we show that it helps in lowering utility energy costs and electricity bills, and improving the load factor of the aggregate neighbourhood-level power load. We also conclude that consumer exibility in rescheduling appliances is rewarded fairly based on the shape of his power load pro le rather than his total energy consumption. 8

Future Work Eventually, we intend to investigate an appliance scheduler that coordinates electric energy consumption among a large number of households (agents).

Proposal of information provision to probe vehicles based on distribution of link travel time that tends to have two peaks Keita Mizuno, Ryo Kanamori, and Takayuki Ito

Nagoya Institute of Technology, Gokiso, Showa, Nagoya 466-8555, JAPAN [e-mail], [e-mail], [e-mail] http://www.itolab.nitech.ac.jp/itl2/page_en/ Abstract. In most cities, traffic congestion is a primary problem that must be tackled. Traffic control/operation systems based on information gathered from probe vehicles have attracted a lot of attention. In this paper, we examine provision of travel information to eliminate traffic jams. Although it is conventional to provide the mean of historical accumulated data, we introduce the 25th percentile and 75th percentile values because a distribution of link travel time tends to have two peaks. As a result, the proposed method reduced travel time of vehicles compared with the conventional method. 1

Introduction Automobile traffic jams have become a major problem in many cities of the world. In Japan, an increase in vehicle emissions and time loss due to traffic congestion have also become signi cant problems. As a solution to these problems, information collected from probe vehicles is attracting attention. In this research, we assume an environment in which information of the travel time of a vehicle in the past can be obtained, vehicles can communicate mutually, and vehicles can share traffic conditions to reduce the travel time of all vehicles. Thus, we propose a method of providing information to a probe vehicle for reducing travel time of regular vehicles, and show the effectiveness of the proposed method by simulation experiments.

In this research, we focus on how a distribution of link travel time tends to have two peaks for historical accumulated data of travel time of the vehicle. In addition to the mean of historical accumulated data of the link travel time, using the 25th percentile value and 75th percentile value of historical accumulated data, we perform path nding and give information to the probe vehicle. Furthermore, to demonstrate that the proposed method of this research is effective,

Keita Mizuno, Ryo Kanamori, and Takayuki Ito we implement traffic ow simulation based on the cell transmission model[ 1 ][ 2 ], and we perform vehicle movement simulation of the conventional method and proposed method. We use travel time of the vehicle, which has also been used in conventional research, for the effect analysis of information provided to the probe vehicle. In addition, we examine the difference between the time taken to move in the simulation and travel time to the destination that is expected from the historical accumulated data of the vehicle.

The remainder of this paper is organized as follows. Background and purpose of this research are presented in chapter 2, and the distribution of link travel time having two peaks is discussed in chapter 3. We describe the proposed information provision method in chapter 4, the vehicle simulation in chapter 5, and the effectiveness of the proposed method, along with future work in chapter 6. 2

Background and purpose In this chapter, we describe the background and purpose of this research. Personal vehicles have become an essential means of transportation for many people. However, there are many problems we must solve; for example, decline in economic efficiency due to traffic congestion, global environmental degradation such as global warming and air pollution, and many traffic accidents. Transportation and traffic account for about 20% of carbon dioxide emissions in Japan, and of that, vehicles account for about 90%[ 3 ]. Figure 1 is a diagram showing the relationship between carbon dioxide emissions and the running speed of a vehicle. Because we can see that the carbon dioxide emissions from the vehicle decrease when running speed of the vehicle increases, we must decrease carbon dioxide emissions by eliminating traffic congestion, and increasing the running speed of the vehicle. Also, there are approximately 5 billion hours per year in time lost to congestion in Japan, and the economic loss is 11 trillion yen. Problems caused by traffic congestion have clearly become serious in Japan, as in many other parts of the world, and it is necessary to resolve these issues.

In addition to the promotion of next-generation vehicles such as electric cars as a way to solve these problems, traffic operation and management measures by Intelligent Transport Systems (ITS), such as providing path information and road pricing, have attracted attention. The number of vehicles with vehicle perception and navigation systems (probe vehicles) is increasing, and technology of information collection and provision has also advanced in route search information. Further, from the historical accumulated data collected from the probe vehicle, it is observed that a distribution of link travel time tends to have two peaks.

About providing information to the probe vehicle, Kanamori et al.[ 4 ] simulated providing information to a probe vehicle using not only the historical accumulated data collected from the probe vehicle but also predicting the traffic situation. Morikawa et al.[ 5 ] simulated providing information to a probe vehicle using the number of right and left turns in the path to the destination, in addition to the historical accumulated data collected from the probe vehicle.

Proposal of information provision to probe vehicles In researches of Kanamori et al. and Morikawa et al., they simulated providing information that uses the mean of historical accumulated data collected from probe vehicles, and searches for a route to a destination.

The purpose of this research is to propose a method to use historical accumulated data focusing on the distribution of link travel time, which tends to have two peaks, and reducing travel link time of vehicles in the simulation. 3

Distribution of link travel time In this section, we discuss how a distribution of link travel time tends to have two peaks. Link travel time of the vehicle described in this research is the time to travel from one intersection to another.

Figure 2 shows example of distribution of link travel time. It is observed that a distribution of link travel time tends to have two peaks when the vehicles pass through the intersection, and simulations that reproduce a distribution of link travel time have been researched[ 6 ].

The cause of the link travel time of the vehicle having two peaks is, for example, a traffic signal. When the vehicle passes through an intersection, a considerable difference occurs because the vehicle stops at the signal or doesn't stop. In previous research, they didn't consider that a distribution of link travel time tends to have two peaks; instead, they used the mean value of the link travel time collected from the probe vehicle. 4

Information provision to probe vehicles In this chapter, we provide a detailed description of the method of information provision to the probe vehicle in this research. As usage of the historical accu

Keita Mizuno, Ryo Kanamori, and Takayuki Ito mulated data of link travel time for searching the route to the destination, in addition to a conventional method to provide the mean of historical accumulated data of the travel time, we introduce provisions of the 25th percentile value and 75th percentile value of historical accumulated data of the travel time in this research.

Probe vehicle assumed in this paper is sending information of link travel time and receiving information of path to destination with least travel time. Information of path to destination with least travel time is predicted by link travel time collected from probe vehicle.

In this experiment, we use the data of the 25th percentile and 75th percentile values of the historical accumulated data of link travel time. To decide which value we will use in this research, we conduct a preliminary experiment. First, we used only the 25th percentile value of the historical accumulated data in the information-providing simulation. Second, we used only the 75th percentile value of the historical accumulated data in the information-providing simulation. We compared the mean of historical accumulated data of the link travel time with 25th percentile and 75th percentile values regarding the travel time of the vehicle. In this research, assuming the differences of factors such as the number of intersections passed through depending on the travel distance of the vehicle,

Proposal of information provision to probe vehicles we compare the mean value, 25th percentile and 75th percentile values by travel distance of each vehicle.

We set the travel distance of vehicles using the 25th percentile or 75th percentile values in the simulation, and conduct information provision simulation using the 25th percentile and 75th percentile values for searching the route to the destination.

Simulation for evaluation 5.1

Settings of simulation We use the data of Kichijoji and Mitaka that are provided in the traffic simulation clearing house as a road network used for the evaluation experiment in this research. The traffic simulation clearing house[ 7 ] is an institution providing various data for validation. The network is composed of 57 nodes and 137 links. Vehicles in the simulation number about 17,000 units, and approximately 50% are probe vehicles in this experiment. Further, in order to accumulate link travel time for the vehicles to be used for route search, the simulation was repeated about 30 times. Figure 3 is a network diagram from Kichijoji and Mitaka that is used for the simulation in this research.

Keita Mizuno, Ryo Kanamori, and Takayuki Ito

Table 1 shows survey contents collected by Kichijoji and Mitaka. Investigation time is set to a high-traffic period.

Since the investigation data contain the times each vehicle entered and exited the network, we can obtain the travel time to the destination of each vehicle. In this research, we implemented a traffic ow simulation based on the cell transmission model, in which the repeatability of travel time is high and we can control the route choice of the vehicle in the simulation. The cell transmission model is a model that divides the network links into cells and controls the movement of vehicles by the density of vehicles in a cell.

yi(t) = minf ni 1(t); Qi(t); Ni(t) ni(t)g { yi(t): number of vehicles moving to the cell of index i at time t { Qi(t): maximum number of vehicles that can ow into the cell of index i at time t { Ni(t): maximum number of vehicles in the cell of index i at time t { ni(t): number of vehicles in the cell of index i at time t

Equation (1) represents the number of vehicles to move between cells on the cell transmission model. The number of vehicles that can move to the next cell is determined by the smallest number of the following: number of vehicles in the present cell, the amount of empty space in the next cell, or maximum number of vehicles that can ow into the next cell. Equation (2) represents traffic ow rate.

q = k v (2) { q: traffic ow rate in the cell. { k: vehicle density in the cell.

Proposal of information provision to probe vehicles { v: vehicle speed in the cell.

Table 2 shows the results of a comparison of the coefficient of simple linear regression and the root mean square regarding the simulation based on the cellular automata model and the cell transmission model. Table 2 shows that the reproducibility of the travel time in the simulation based on the cell transmission model is greater than that of the cellular automata model from the values of both the coefficient of simple linear regression and the root mean square.

Traffic ow simulation that reproduces a distribution of link travel time tending to have two peaks is required for information provision and shows the effectiveness of proposed method.

Figure 4 shows that the passage number and travel times of the vehicles on one link in the network when we simulated movement of the vehicles using the Kichijoji and Mitaka data set on traffic ow simulation. As Figure 4 shows, it was con rmed that it is possible to reproduce a distribution of link travel time tending to have two peaks in the traffic ow simulation implemented in this research. Difference of the travel time for each distance of vehicles We show the comparison results regarding the travel time of vehicles between using the

Keita Mizuno, Ryo Kanamori, and Takayuki Ito mean value, 25th percentile value and 75th percentile value of the historical accumulated data of the link travel time.

Figures 5 and 6 show difference of travel time between using the mean, 25th percentile value, and 75th percentile value for route search by travel distance of vehicle. The value of the graph subtracts the travel time when using 75th percentile and 25th percentile values from the travel time in case of using the mean value. As the value of the graph is large, it represents that the travel time

Proposal of information provision to probe vehicles of vehicles using the mean value is more than the travel time of vehicles using the 25th percentile value and 75th percentile value. In Figure 5, the travel time of vehicles using the 75th percentile value is less than that using the mean value regarding vehicles that travel distances of 1,000 meters or more. On the other hand, in Figure 6, the travel time of vehicles using the 25th percentile value is less than that using the mean value regarding vehicles that travel distances of 1,000 meters or less.

Proposed method and evaluation In this research, we proposed that vehicles whose travel distance is 1,000 meters or less perform a route search using the 25th percentile value of historical accumulated data, and vehicles whose travel distance is 1,000 meters or more perform a route search using the 75th percentile value of historical accumulated data. The effect analysis is the total travel time of all vehicles in the simulation.

Figure 7 shows the result of the simulation experiment in each case. Values in the graph of Figure 7 show the total travel time of all vehicles in each case. We describe setting of each case. There is no probe vehicle in case 1; that is, vehicles do not change their routes in repetition. The probe vehicles search for the route using mean value in case 2, 25th percentile value in case 3, and 75th percentile value in case 4 as link cost. We use the proposed method in case 5.

As shown in the graph of Figure 7, using both 25th percentile value and 75th percentile value of historical accumulated data reduced the travel time of all vehicles most.

Keita Mizuno, Ryo Kanamori, and Takayuki Ito

Conclusion and future work In this research, we presented background information about the problems caused by the increasing number of vehicles on the road, such as economic losses and environmental degradation. Also, the number of probe vehicles has increased in recent years, and the distribution of link travel time tends to have two peaks. Next, we proposed information provision based on a distribution of link travel time tending to have two peaks. In the experimental simulation, as the information provision to the probe vehicle, we proposed using both the 25th percentile and 75th percentile values as a function of travel distance of a vehicle. We demonstrated that the proposed method reduced the travel time of all vehicles compared with the conventional method.

In future work, we will simulate a large network. In this experiment, since we used a small network data set, it is necessary to test a larger network to con rm that the proposed method is effective.

The information method proposed in this research used travel distance of the vehicles; it is also necessary to use such factors as the departure time of the vehicles in future research.

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