Community Research and Development Information Service - CORDIS

H2020

OPTIMUM Report Summary

Project ID: 636160
Funded under: H2020-EU.3.4.

Periodic Reporting for period 1 - OPTIMUM (Multi-source Big Data Fusion Driven Proactivity for Intelligent Mobility)

Reporting period: 2015-05-01 to 2016-10-31

Summary of the context and overall objectives of the project

The transportation sector is undergoing a considerable transformation as it enters a new landscape where demand for mobility services is increasing mainly due to the current vast development in urbanization. Better modal choices must emerge, since over-reliance on private cars results in cities' suffering more from congestion, poor air quality and higher risks for travellers. Such modal choices can be the product of greater integration of the modal networks and the promotion of collective travelling options. OPTIMUM aspires to greatly impact urban mobility and foster behavioural changes that will alter citizens’ habits towards sustainable utilization of transportation networks. By the provision of multimodal trip alternatives and the integration of proactive information in real time, OPTIMUM will alleviate uncertainties related to public transport usage and have an impact on the access levels of public transport systems. Furthermore, OPTIMUM will raise citizens’ awareness and provide information that can support users to choose the optimal and most rewarding modality in terms of time consumption, environmental impact and cost. OPTIMUM’s proactive information models for transportation networks will provide additional value for rural/urban areas and will increase the efficiency and quality of local and regional transport systems, showing to users that the public transport network is a reliable and less costly solution.
In doing so, OPTIMUM will capitalise on the benefits and potentials of big data fusion and proactive behaviour in the diverse and multimodal transportation context. Thus, OPTIMUM will develop a smart sensing system able to cope with a huge amount of heterogeneous data in real-time. Processed data will support a platform composed of dynamic and context-aware forecasting and situations of interest detection techniques, system-aware optimisation mechanisms, real-time multimodal routing and navigation algorithms, as well as adaptive charging and crediting models. The user will interact with the platform through proactive and personalised information provisioning and persuasive mechanisms, therefore OPTIMUM will promote proactive decision making and sustainable transportation behaviours.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Initial work for the definition of the platform focused on the review of the current state of the art, the elicitation of user requirements and the formalisation of use cases to describe the functionality of the platform. The integration of findings from the above activities led to the development of the OPTIMUM conceptual model and related architecture. The developed architecture guided the modelling and implementation R&D activities, which were structured into the 4 layers of the Observe, Orient, Decide and (Pro-) Act (OODA) approach. For the Observe layer, a working data infrastructure, constituting a single point of access for all data, was built. The current version of the data infrastructure allows the active collection from 18 data-source groups, it is distributed and has integrated mechanisms for data analytics, quality assurance, processing and re-usability. Travel demand and forecasting models have been developed as part of the Orient layer of our approach. The travel demand model was derived using data from 70 individuals from Austria, Slovenia, and the UK, who, apart from the socio-economic data that they provided, took part in multimodal alternatives stated preference experiments. In terms of forecasting models, the first version includes a number of statistical and machine learning techniques, as well as mechanisms for the extraction of transport related information from public tweets in order to support the prediction task. As part of the Decide layer of our architecture, a dynamic charging model was developed using data from a sample of 50 drivers and 25 freight operators in Portugal. The model calculates the price or the discount for any given road section that contains a specific toll. Furthermore, the first “user-centered” version of the multimodal routing algorithm has been implemented, in such a way that a future extension towards system awareness would be easily realizable. The (Pro-)Act layer, involved the development of the OPTIMUM user model and a range of information personalisation and persuasive recommendation services. The former comprises of user characteristics and attributes which provide the basis for the generation of tailored interventions. The latter is a suite of services that are tailored to the individual users and aim to persuade or nudge them towards the selection of environmental friendly routes, as well as to proactively push suggestions to the users without their explicit request at the right moment. All the above stated components have been integrated together as part of the OPTIMUM’s two applications (mobile and web), which will be used as part of the first round of pilots that will commence in January 2017. Apart from the technical activities, the overall strategy and objectives of dissemination have been put in place and, as indicators showed, dissemination targets were reached. The planned tools were utilized and specified target audience engagement has started. OPTIMUM will continue leveraging on the existing professional networks of project partners to further engage key stakeholders consisting of decision makers, technology experts, similar projects, researchers and potential users that could act as multipliers, companies, European policy makers and will further liaise with already existing clusters of networks on the field. Finally, regarding the business plan that we foresee to follow in order to further exploit the OPTIMUM developments, we successfully identified the overall exploitation methodology and we mapped the current ITS ecosystem as a network of interconnected business entities (key actors) in the specific pilot cases.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Main innovative achievements during this period, that advance existing technologies and go beyond the state of the art, for realising OPTIMUM architecture and applications, with the aim to fulfil OPTIMUM objectives, include:
-Use of GPS Traces for Complex Event Analysis and Travel Pattern Prediction
-Develop novel models for Traffic Forecasting via the fusion of sensor and social media data
-Detection of stress based on Social Media (relevant publications delivered)
-Integrated multi-modal route planning
-Dynamic charging models for freight vehicles
-Route recommendation service
During this 1st period the partners' efforts have been mainly focused on technical activities in order to deliver first models, algorithms and prototypes. The results from those activities have been disseminated accordingly via various channels towards the scientific and academic communities. Therefore, a strong scientific and academic impact has being realised via more than 10 scientific publications in high impact scientific journals, conferences and workshops, both organized and attended by the project partners. Moreover, the collaboration with other similar ITS projects enhances the potential scientific and technological impacts of the project. Notably, we foresee further technological impact out of the web and mobile applications that are currently under development, as well as further economic (less costs) and environmental impacts (decreased utilisation of cars, decreased CO2 emissions, etc.) that will be quantified via the actual pilot trial phases that are starting in the next months.

Related information

Record Number: 198172 / Last updated on: 2017-05-17
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