Vassilis Christophides
email: vassilis.christophides@technicolor.com

Themis Palpanas
email: themis@mi.parisdescartes.fr

Report on the First International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT 2014)

INTRODUCTION

In the last few years, we are witnessing a wave of connected things (i.e., devices) that are flooding our everyday living spaces. These networks of physical objects with embedded sensors and actuators that communicate with other objects, databases, and in some cases with people, are often described under the umbrella term Internet of Things (IoT). Recent studies 1 estimate that by 2020 the IoT will scale up to 50 billion daily-use objects, while its economic value will reach $14.4 trillion dollars, spread across all sectors (both consumer 2 and industrial 3 markets). According to the IoT vision, "smart things" will "disappear" in our living environments (aka Pervasive Computing), or be embodied in humans (aka Wearable Computing, with the goal of building disruptive services for humanbeings, offering a seamless and implicit interaction between the real and the virtual worlds.

In this changing world, every human action and living context will create "information shadows" on the IoT via active, or passive monitoring. For example, Quantified-self 4 ) devices enable self-tracking of any kind of biological, physical, or behavioral information, smart home sensors 5 capture accurate indoor environmental information and energy consumption habits, residential gateways record in real-time usage information of communication and entertainment devices, while smart cars and phones trace the places and trajectories of our everyday life. Fine-grained personal data are already being massively created, permeating almost every facet of human interaction to objects (user awareness), to their physical environment (ambient awareness) and to other humans (social awareness). Such personal data are mostly analyzed today by application silos tightly coupled with the employed "smart things" (in health and well-being, home automation and entertainment, etc.). Individuals are striving today for tools that can help them to gather, process and make sense of all the data they produce in private, as well as public spaces.

The 1st International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT), held in conjunction with VLDB 2014, aims at sparking research on data analytics, shifting the focus from business to consumers services. While much of the public and academic discourse about personal data has been dominated by a focus on the privacy concerns and the risks they raise to the individual, especially when they are seen as the new oil of the global economy6 , PDA@IOT focus on how persons could effectively exploit the data they massively create in Cyber-Physical worlds. It relies on the following assumptions7 :

x My personal data is most valuable to me! x People want to benefit from their data-not hide it away -but they need control, trust & transparency x Personal data economy is primarily a human experience economy x Personal data intend to empower people

We believe that the full potential of the IoT goes far beyond connecting "things" to the Internet: it is about using data to create new value for people. In a Peoplecentric computing paradigm8 , both small scale personal data and large scale aggregated data should be exploited to identify unmet needs and proactively offer them to users. PDA@IOT seeks to address current technology barriers that impede existing personal data processing and analytics solutions to empower people in personal decision making9 .

The PDA@IOT ambition is to provide a unique forum for researchers and practitioners that approach personal data from different angles, ranging from data management and processing, to data mining and human-data interaction, as well as to nourish the interdisciplinary synergies required to tackle the challenges and problems emerging in People-centric Computing. A list of core research issues considered by the first edition of the workshop includes:

x Trusted architectures for personal data collection balancing data privacy & utility x Interpretation of raw personal data streams originating from heterogeneous sensors x Descriptive, predictive & cognizant data personal analytics x Offline/online personal data processing & curation.

The workshop program included 2 keynote talks, 7 research papers, and a panel discussion. In this report, we summarize the ideas that were presented and discussed in the workshop. The detailed program and the slides for the talks are available at the PDA@IOT web page: http://www2.thlab.net/pda-iot2014/.

Keynote Talks

The first keynote talk, "Objective Self", was given by Prof. Ramesh Jain from the University of California at Irvine (USA). Prof. Jain presented how it is now possible to analyze and understand a person's life style in order to build a model for an individual, called the "Objective Self". Most people use phones with a multitude of sensors that continuously generate data streams related to several different aspects of their lives. Other powerful sources of information for a person are social networks, e-mail, calendar systems, and lately wearable and home sensors. By integrating these multi-sensory data streams, it is possible to create an accurate chronicle of a person's life. In this talk, Prof. Jain focused on the context of a person's health: by correlating life events to health related events obtained using wearable sensors and other sources of information, one can build the health persona of a person, which is a long-term objective characterization of a person's health. Several data streams can be used in this respect, such as motion tracking, location tracking, activity level, wearable health sensors, and personal calendar data. Prof. Jain illustrated how recognition algorithms can be applied to the Life Event detection problem and then build an objective personal chronicle, called personicle. By building a personicle over a long period and applying data mining, it is possible to create a model of the person that could result in actionable insights and alerts in everyday life.

The second invited speaker was Prof. Dimitrios Georgakopoulos from the RMIT University (Australia) and his talk was entitled "Distilling High Value Personalized Information from the Internet of Things and Social Networks". He highlighted that Cyber-Physical-Social (CPS) Computing encompasses realtime extraction of high value and personalized information from social networks and millions of cyber-physical systems in the IoT, as well as the development of specialized cloud-based services. Despite the expanding array of applications that require distilling knowledge from big CPS data, there is currently no easy way to manage and exploit such big data, personalize the results, or develop cloud services that make this possible anywhere via mobile devices. Therefore, CPS requires the development of novel solutions for discovering on-line cyber-physical and social media sources, dynamically integrating such sources and their data, and analyzing or personalizing billions of data streams and tens of years of historical data form on-line sources anywhere and in real time. Prof. Georgakopoulos was particularly interested in context-aware techniques proposed by research and commercial systems that can help individuals understand IoT data. Finally, he outlined a unified approach for CPS data management and analysis involving real-time summarization and personalization as the main way of extracting high value information from big data in various domains: digital agriculture, smart energy grids, and disaster management.

Research Papers 3.1 Healthcare and Wellbeing

The first session of the workshop was devoted to research papers that addressed personal decision making in healthcare and wellbeing.

In the paper entitled "kHealth: Proactive Personalized Actionable Information for Better Healthcare", Amit Sheth, Pramod Anantharam, and Krishnaprasad Thirunarayan focused on health aficionados and patients with chronic conditions that increasingly rely on sensors and mobile devices to track sleep, food, physical activity, and other physiological observations (e.g., weight, heart rate, blood pressure). They highlighted a paradigm shift in healthcare from reactive medicine to predictive, preventative, personalized, and participatory medicine, in which individuals are empowered to fully participate in health related decision making. To facilitate this transformation, there is a need to understand the richness and nuances of fine-grained healthcare data produced by a variety of mobile devices and wearable sensors, related to a person and the population in general. The majority of existing data analytics techniques focuses on finding discrepancies in a single stream of observations without much insight into the problem and actionable knowledge to the individuals. The authors presented the kHealth system for analyzing observations from passive (no human involvement in data collection) and active (human input involved in data collection) sensors in order to provide explanations that are intelligible to individuals and their clinicians for well-informed decision making.

In the same direction, Emmanouil G. Spanakis, Feng Dong, Manolis Tsiknakis, Vangelis Sakkalis, Kostas Marias and Dimitris Kafetzopoulos, in their work entitled "MyHealthAvatar: The digital patient perspective using Internet of Things technologies", focused on how health-related information in the IoT can be locally aggregated and transmitted for remote monitoring and response. They stressed that the interrelation of such heterogeneous data sources opens new directions for providing a comprehensive picture related to health, thus triggering interventions by medical staff when necessary, and realizing preventive care. They described MyHealthAvatar (MHA), a personal digital health related collection bag, carried by individual citizens throughout their lifetime to sustain in a meaningful manner all information related to their health, empowering users through a number of patient-centred healthcare services. They outlined the MHA challenges for accessing, collecting and sharing long term multilevel personal health data through an integrated environment including: clinical data, genetic data, medical sensor data and devices, human behavior data and activity data for clinical data analysis, prediction and prevention for the individual citizen.

Finally, Xinpeng Zhang, Hiroki Kitabayashi, Yasuhito Asano and Masatoshi Yoshikawa, presented "A Health-aware Pedestrian Navigation System by Analysis of Spatiotemporal Vital Signs Data", discussing how health-related data can empower individuals in their outdoor social activities. The authors focused on a health-aware pedestrian navigation system to assist the outdoor activities for vulnerable pedestrians, such as senior people, or people with disabilities. Examples of health related profiling of jogging itineraries include the recognition of when pedestrians get tired while climbing a slope, or if they feel stressed when walking some road. The system offers essentially two services. First, it monitors the personal health condition from individual vital signs data. Since a pedestrian's health condition varies by location and time due to fatigue and stress, it is spatio-temporally contextualized. Second, the system recommends jogging itineraries according to the results of the previous analysis. It can therefore guide multiple pedestrians to walk together, promoting conversation and strengthening mental health.

Life Logging and Citizen Services

The second session of the workshop focused on extracting individual and collective behaviors for life logging and citizen services.

Laleh Jalali, Da Huo, Hyungik Oh, Mingfan Tang, Siripen Pongpaichet and Ramesh Jain, in their work entitled "Personicle: Personal Chronicle of Life Events", presented an overview and early results of their project aiming at integrating, aggregating, and analyzing heterogeneous personal data streams to build a persona, and use it to recognize evolving personal situations. In particular, the authors are interested in recognizing movements and personal situations in order to empower individuals with actionable information and insights. They demonstrated how high-level life events can be extracted through simultaneous use of asynchronous observations consisting of continuous GPS and accelerometer measurements. To create a chronicle of life events, called Personicle, they detect low level physical activities using data produced by various unobtrusive sensors embedded in a mobile phone, and use hierarchical classification techniques for identifying high level life events using location context and physical activity. The authors plan to collect, store, and analyze data from a large number of heterogeneous sensors, and make publicly available the test data sets.

Yuzuru Tanaka, Hajime Imura and Jonas Sjabergh, in their work entitled "Exploratory Visual Analytics for Winter Road Management using Statistically Preprocessed ProbeCar Data", show how aggregated probe car data can be statistically preprocessed over road links for an urban-scale area, in order to visualize the dynamic change of traffic flow in terms of the divergence and the flow vector field. This analysis provides insights regarding the dynamic change of traffic hotspots, main traffic streams, and route selection preference. This work is motivated by new citizen services, in particular improving winter road management in Sapporo. They extend well-known exploratory visual analytics techniques to multiple coordinated views by integrating different analysis tools with their result visualization views into the same operational framework. These newly added views may coordinate with others, and allow users to directly select patterns calculated at runtime to further quantify the underlying database view. Exploratory visual analytics with such an environment enables us to detect road links for effective, pinpoint snow-removal.

Wearable Devices

The third session of the workshop program comprised papers analyzing data from wearable devices to recognize individuals' emotional arousal, or train their brain's fast and slow thinking abilities.

Julien Fleureau, Philippe Guillotel and Izabela Orlac in their work entitled "Affective Profiles of Movies and Opera Based on the Physiological Responses of the Audience" proposed an objective study of the emotional impact on real audience of various audiovisual shows and live events. An affective benchmarking solution was presented, making use of a low-intrusive measurement of the ElectroDermal Activity (EDA), which is known to be linked to the unconscious reactions of the nervous system, and thus can be used as a measure of emotional and sympathetic arousal responses. A dedicated processing of this biosignal produces a time-variant and normalized affective profile related to the significant excitation variations of the audience. Besides the methodology, the originality of this work stems from the evaluation of the proposed framework on 62 real audience members during special screenings of a film festival and one opera. The authors show that the resulting "Affective Profile" is strongly correlated to the events occurring during the shows, that some shows have a higher intensity than others, and that the extracted affective profile is consistent with the artistic rules from the creative intent.

Panel Discussion

Given that personal data processing and analytics is still in its infancy, the panel attempted to sketch a first roadmap of the emerging core research issues. The moderator, Vassilis Christophides (Technicolor R&I, France), challenged three panelists on the building blocks required to understand and make sense of Personal Data harvested in the IoT for enabling smarter individual choices and behavioral changes, as well as resource-conscious automation and optimization of everyday life: (a) Prof. Ramesh Jain (Univ. of California at Irvine, USA) (b) Prof. Yuzuru Tanaka (Hokkaido Univ., Japan), and (c) Prof. Themis Palpanas (Paris Descartes Univ., France).

Prof. Ramesh Jain emphasized the need for collecting data from various wearable devices, mobile phones, and social networks, in a way that users do not feel threatened for their privacy. Current data sources are very limited, because people do not see a good reason to take the risk and share their data. He emphasized the need for developing simple applications that clearly show that data sharing results in tangible benefits, in order to encourage people to share their data for the long-term benefits to individuals, as well as to the society. Another issue that Prof. Jain raised was a sustainable business model to be used for personal data. Some people have talked about trading personal data for value depending on who wants access to it. This is a fundamental issue that assumes that personal data is a new currency of some kind that could be traded for the appropriate value.

Prof. Yuzuru Tanaka pointed out that urban-scale monitoring of collective human activities is nowadays conducted through private sector sensing (smartphone, probe-car and card usage data), public sector sensing (traffic jam/accident, public transportation and surveillance-camera image data), and crowd-source sensing (smart-phone application usage and Social Network data). Unfortunately, these data reside in different silos, mainly due to data privacy concerns. Statistical data processing may be a solution. However, what kind of statistical processing may allow what kind of analyses to discover what kind of knowledge about collective human activities is still an open issue. More generally, he argued that there exists a big gap between the state-of-the-art analytical methodologies and the complex big data application requirements, such as optimization of urban-scale infrastructures. In these settings, we need to analyze systems of heterogeneous systems, where each component can be mathematically modeled, but the whole system cannot. To find an appropriate analysis scenario is itself a research task. In this respect, he advocated the need for new machine learning algorithms capable to deal with these challenges, and that, for the moment, a pragmatic alternative seems to be exploratory visual analytics.

Prof. Palpanas stressed the need for non-technical users to be able to easily manage their personal data, as well as perform a range of analysis tasks on them. This would give them insights with respect to their habits and personal choices, and enable them to make informed decisions about their lifestyles. He argued that this goal could be achieved by the development of a new generation of systems and applications having usability at the core of their design. He offered as an example the domain of data series: even though most of the popular personal monitoring devices produce data in the form of time series, there are no suitable systems that can empower non-technical users (or developers) to take full advantage of the wealth of information hidden in these data.

  Finally, Melanie Swan, Takashi Kido and Minna Ruckenstein in their work entitled "BRAINY: Multimodal Brain Training App for Google Glass" presented a new Google Glass application for brain training. This work is motivated by the observation that there are over 50 brain fitness training companies, but there is not yet a brain fitness training app for Glass. BRAINY initially targets the improvement of memory function, and additional cognitive training modules can be added later for attention, processing speed, flexibility, problem solving, and other areas. BRAINY takes advantage of the audio-visual and voice command functionality of the Glass platform to create multi-modal memory games that can be played easily by users during waiting time, or other down time. The more advanced memory modules of BRAINY target neuroplasticity and memory updating, and train the brain's fast and slow thinking systems.

A. Pentland et al. "Personal Data: The Emergence of a New Asset Class", World Economic Forum. January 2011.

A. Margolis "A people-centred approach to Data and the Internet of Things", Claro Partners, 2014.

Similar paradigm shifts have been recently proposed in other domains such as Vendor Relationship Management (VRM) in contrast to Customer Relationship Management (CRM) or the Intention in contrast to the Attention Economy.

J. Duggan. "The Case for Personal Data-Driven Decision Making". InPVLDB, vol. 7, 2014.

ACKNOWLEDGMENTS

Special thanks go to Technicolor R&I Center (France) for their support in the organization of this workshop.


the definition of a measurable environmentally sustainable transport"

Keywords: measurement, tool, environment, impact, indicator, COST action

After a presentation of the objective of the COST action EST "Towards the definition of a measurable environmentally sustainable transport", the literature has been reviewed to identify some potentially relevant definitions of the term 'indicator', to help identify the key functions that indicators can play, and revel the extent to which context factors should be allowed to influence the definition of indicators. The review of indicator definitions considers general or generic indicator definitions, the definitions of 'environmental' indicators, the indicator definitions that take into account the context of sustainability, and the indicator definitions that have been proposed within the specific field of sustainable transport. A definition is proposed in conclusion.

The COST action 356

COST 356 aims at contributing to a systemic approach in the assessment of the environmental sustainability of transportation issues by integrating and communicating existing European knowledge (see http://cost356.inrets.fr). The primary target audience is forecasting (or backcasting) analysts involved in the impact assessment of the transport system, and transport planners.

The action is concerned with how environmental impacts of transport can be measured, how measurements can be transformed to operational indicators and indices, and how indicators are used in planning and decision making. The focus of the action is on the environmental dimension of sustainability. The main objective of the action is to identify harmonised, scientifically sound methods to build environmental indicators or indices for the assessment of transportation projects, plans and policies, and to integrate these indicators into decision-making processes by indicator selection or aggregation (e.g. multi-criteria analysis).

COST is an intergovernmental framework for European Co-operation in the field of Scientific and Technical Research, allowing the co-ordination and exchange of nationally funded research initiatives. It is open also to non-European countries and enables scientists from any country to collaborate in a wide spectrum of activities. COST is based on so-called actions. These are networks of co-ordinated national research projects in a given field. Each action is built by scientists from a bottom-up approach, and facilitates meetings and technical exchanges, usually reimbursed from the COST Action resources and encompass Management Committee meetings, working group meetings, short term scientific missions, workshops and seminars, dissemination and possible national working groups.

Scientists from 20 countries are currently participating to COST 356, which commenced on October 2005 and is scheduled to be completed by January 2010.

COST 356 is organised in four working groups, the core of the scientific work being done in WG 2 and 3. Whereas WG 2 basically adopts the environmental or natural science perspective and analyses which impacts are relevant, and how they could and should be described and measured, WG 3 identifies requirements for environmental sustainability indicators from the perspective of decision makers, and identifies methods to integrate them into decision making. An important, continuous part of the work will consist in discussing and integrating the results obtained from the application of each of these two perspectives in-between the working groups.

The action hosts a seminar that takes place at the Institute of Transport Economics (TØI) in Oslo, Norway, on February 20 th 2008. There are two main objectives of the seminar:

-to present to a larger audience the work carried out so far within the COST action 356 on environmental indicators as measurement tools or decision making tools for environmentally sustainable transport -to present significant research by other scholars in the same field, allowing the COST action to discuss and take into account the best available current thinking and results .

Defining indicators

The literature has been reviewed to identify some official and other potentially relevant definitions of the term 'indicator'. The detailed review is by no means complete. The role of this review of indicator definitions is not to locate one 'correct' definition, but to help identify the key functions that indicators can play, and revel the extent to which context factors should be allowed to influence the definition of indicators. The review of indicator definitions considers i) general, generic or global indicator definitions from dictionaries, encyclopaedia and some significant academic contributions, ii) the definitions of 'environmental' indicators, iii) the indicator definitions that take into account the context of sustainability, and iv) the indicator definitions that have been proposed within the specific field of sustainable transport. A definition is proposed in conclusion.

General definitions

A. A substance (as litmus) used to show visually (as by change of colour) the condition of a solution with respect to the presence of a particular material (as a free acid or alkali) (Websters).

B. An organism or ecological community so strictly associated with particular environmental conditions that its presence is indicative of the existence of these conditions (Websters).

C.

[ecology]: indicator species -a species whose presence is directly related to a particular quality in its environment at a given location (McGraw-Hill Encyclopaedia of Science & Technology).

D.

[economics] Any of a group of statistical values (as level of employment) that taken together give an indication of the health of the economy (Websters). G. Common term to refer to the variables that we use to detect (…) concepts empirically [START_REF] Bollen | Indicators: Methodology. International Encyclopedia of the Social & Behavioural Sciences[END_REF].

H. A variable that is directly associated with a latent variable such that differences in the values of the latent variable mirror differences in the values of the indicator [START_REF] Bollen | Indicators: Methodology. International Encyclopedia of the Social & Behavioural Sciences[END_REF].

I. At a more concrete level, …indicators are variables (not 'values', as they are sometimes called).

A variable is an operational representation of an attribute (quality, characteristic, property) of a system [START_REF] Gallopin | Environmental and sustainability indicators and the concept of situational indicators. A systems approach[END_REF][START_REF] Gallopin | Indicators and Their Use: Information for Decision Making[END_REF].

J. The reasoning is a multi-step one. Given a concept X. We begin by building a representation of this concept full of imagery: Here come into play knowledge, sensibility and creativity. The next step specifies the concept, giving its dimensions. During the third step, indicators of these dimensions are chosen, i.e. some observable characteristics, which show these dimensions. At the end, the weighted synthesis of these dimensions is made, giving a unique measurement, which is the index [START_REF] Bourdon | Le vocabulaire des sciences sociales (Lexicon of social sciences)[END_REF].

These general definitions of an indicator share many common elements. An indicator is generally understood as a tool or a method to measure something in a way that adequately represents what is measured. Even the general definitions are often defined with respect to different measurement functions in different scientific domains (chemistry, biology, social science). In some, mostly natural science definitions, the indicator linkage can be strong (e.g. used to determine something).

In other cases (social science, ecology) the linkage may be weaker, the indicator 'indicating' or suggesting something. In no cases an indicator is understood as a full description of something.

Environmental indicators

K. A parameter, or a value derived from parameters, which points to, provides information about, describes the state of a phenomenon/environment/area, with a significance extending beyond that directly associated with a parameter value (OECD, 2003).

L. A parameter or a value derived from parameters that describe the state of the environment and its impact on human beings, ecosystems and materials, the pressures on the environment, the driving forces and the responses steering that system. An indicator has gone through a selection and/or aggregation process to enable it to steer action (EEA, 2007).

M. A numerical value derived from actual measurements of a pressure, ambient condition, exposure, or human health or ecological condition over a specified geographic domain, whose trends over time represent or draw attention to underlying trends in the condition of the environment (USEPA, 2006).

The definitions of 'environmental indicators' are similar and all concern measurement of aspects of the environment itself or interactions between humans and the environment. The definitions do not deviate fundamentally from the above general definition of indicators, but provide some guidance about the content of environmental indicators. EEA mention 'environmental impact' as one aspect.

The basic notion of representation is clearly present. According to the OECD definition the representation should go 'beyond' what is directly measured. This is identical with the general indicator function. But the linkage between subject and indicator can be accepted as relatively weak for environmental indicator ('provides information about', 'describe', 'derived from', 'draw attention to'). Moreover the measurement aspect is slightly de-emphasised, since environmental indicators may be derived from 'parameters' or derived from 'actual measurement'. EEA highlights the context of steering. USEPA highlights context as a physical time-space domain.

Sustainability indicators

N. Quantitative measures of human wellbeing, economic activity, and natural processes and conditions; they are needed to sense the degree to which human activity may be continued or expanded in the future [START_REF] Lee | International Encyclopaedia of the Social & Behavioural Sciences[END_REF].

O. Sustainable development indicator: A statistical measure that gives an indication on the sustainability of social, environmental and economic development (OECD, 2005).

P. "Sustainability indicators reflect the reproducibility of the way a given society utilizes its environment'' (Opschoor & Reinders, 1991, p. 7).

All of these definitions of sustainability indicators, selected form a large literature on sustainability indicators, highlight the measurement aspect, again in overall correspondence with the general definition and its idea of representation. In this case the representation is of a complex notion namely 'sustainability' or 'reproducibility or 'the degree to which human activity may be continued or expanded.' Hence the linkage is accepted as potentially very weak ('reflect', 'give an indication', 'sense').

Large parts of the same literature deals with another aspect namely the role of sustainability indicators for decision making This literature adds several other elements to what it requires for an indicator to be a adequate sustainability indicator, including being 'meaningful' and 'resonant' (motivating) for decision makers and stakeholders [START_REF] Gray | Risk management and sustainable development: mutual lessons from approaches to the use of indicators[END_REF][START_REF] Scope | Indicators if sustainability. Reliable tools for decision making[END_REF][START_REF] Meadows | Indicators and Information Systems for Sustainable Development[END_REF][START_REF] Bossel | Deriving indicators of sustainable development[END_REF].

Sustainable transport indicators

Q. Selected, targeted, and compressed variables that reflect public concerns and are of use to decision-makers [START_REF] Gilbert | Sustainable Transportation. Performance Indicators (STPI). Project Report On Phase 3. The Centre for Sustainable Transportation[END_REF].

R. Sustainable transportation indicators (STIs) are defined as regularly updated performance measures that help transportation planners and managers take into account the full range of economic, social and environmental impacts of their decisions" [START_REF] Richard | Toward Sustainable Transportation Indicators for California[END_REF].

S. Forecastable quantifiable variable, usually with target value representing an objective, which symbolises environmental or other impacts of transport infrastructure plans (including ordinal scales: e.g low, medium, high): Output Indicator: an indicator that measures the direct output of the plan or programme. These indicators measure progress in achieving plan or programme objectives, targets and policies. Significant Effect Indicator: An indicator that measures the significant effects of the plan or programme. Contextual Indicator: An indicator that measures changes in the context within which a plan or programme is being prepared or implemented (COST 350, 2006).

T. Indicators are ways of quantifying objectives. For example, accident numbers would measure the overall safety objective. This type of indicator is often called an outcome indicator, in that it measures part of the outcome of a strategy. It is also possible to define input indicators, which measure what has been done (e.g. length of bus lanes implemented) and process indicators, which describe how the transport system is responding (e.g. number of bus users) (KonSULT).

U. General principles regarding indicators in any Urban Mobility system: Indicators should support decision-making capacity in particular enabling proactive action to correct the performance path of a specific element or agent whenever signs of potential underperformance are identified… [START_REF] Macario | Quality management in urban mobility systems: an integrated approach[END_REF] The definitions proposed in the context of sustainable transport are, even if mixed, based on the same idea of representation as the general definition. However, the 'something' to be indicated and represented is much more focused on objectives, plans, policies, measures, etc to achieve sustainable transport, than on simply representing items within systems. The definitions draw the emphasis on the context of decision making from the general literature about sustainability indicators, of which it is a subdivision. It does not seem that an EST indicator is acceptable (fulfil criteria) if it does not represent information that is relevant for the performance of policies. The COST 350 is the most detailed, concise and elaborate of the definitions, but very restrictive in the sense that only 'quantifiable, forecastable' variable are accepted. This seems not fully justified in the COST 356 context where indicators may be equally relevant in the retrospective, as in ex post measurement. Also it is restricted to transport infrastructure, which is too narrow for COST 356.

Conclusion and proposed definition

Above are listed three types of definitions:

-a sentry, sentinel, revelation, indicating the presence or absence of something: definitions C to F -a measurement tool: definitions F to Q -a definition by its using: definitions Q to U

The sentinel definition (absence or presence) is also a measurement tool, but a simplified one. Most of the definitions consider an indicator as a measurement tool, but some definitions add considerations about the use of such measurement tool: draw attention, quantify objectives, use by decision makers, help managers, measure progress.

The key notion is representation. An indicator has to represent something in an adequate way. At the same time it has to allow simplification compared to a full representation, other ways there is no point to an indicator. Representation necessarily involves three elements; the thing being represented; the thing representing it (the indicator) and the usage domain (the entity for whom the representation has to be valid; acceptable)

We propose to distinguish clearly the two fields of thought: the characteristics of the measurement tools, and the characteristics of the uses. Both have to be present however, in order the indicators can be fully accepted. The measurement aspect is fundamental to any indicator, and can be one starting point. The decision context and use is essential for indicators to be used for achieving sustainability and sustainable transport. This context is a 'filter' for purely measurement based indicators.

In summary the following simple definitions can be proposed:

An indicator is a variable, based on measurements, representing as accurately as possible and necessary a phenomenon of interest to human beings.

An environmental impact indicator is a variable based on measurements, representing an impact of human activity on the environment, as accurately as possible and necessary.

An indicator of environmentally sustainable transport is a variable, based on measurements, representing potential or actual impacts on the environment, or factors that may cause such impacts, due to transport systems, flows or policies, as accurately as possible and necessary.

  E. [biology]: An organism that can be used to determine the concentration of a chemical in the environment. (McGraw-Hill Encyclopaedia of Science & Technology) F. [analytical chemistry]: A substance whose physical appearance is altered at or near the end point of a chemical titration (McGraw-Hill Encyclopaedia of Science & Technology).