Cooperative, Connected, and Automated Mobility (CCAM) constitutes a viable solution toward sustainable future mobility in order to achieve the target of decarbonization. Artificial Intelligence (AI) and Big Data (BD) have altered several industrial sectors providing novel and affordable solutions that facilitate and improve existing operations in these sectors. Hence, the combination of the CCAM paradigm with AI methodologies based on BD could ever increase the potential benefits of CCAM in the contemporary society. For this reason, three CCAM services, which are based on AI and BD, are introduced in the current research work in order to tackle three well-known issues of mobility such as i) the estimated time of arrival, ii) the passenger demand prediction and iii) the mobility patterns identification. The proposed CCAM services were tested on various pilot sites of the EU-funded SHOW project, thus demonstrating the potential of BD and AI in future mobility services.

AbstractSeamless integration of new types of mobility such as those realized by Connected Automated Vehicles (CAVs) into the existing public transport service requires new service-oriented orchestration platforms. In this work, we present a framework for automated mobility of passengers and goods, realized by the service-oriented architecture of the EU-funded research project SHOW (GA No 857730). As part of the framework, intra-systems and cloud-to-everything interfaces are proposed, while high-level design specifications of architecture alternatives are critically reviewed. An actual example of how such an inclusive architecture is instantiated in Trikala SHOW pilot site for city-specific SHOW services deployment is presented. Finally, lessons learnt that are relevant to data access, interoperability and cybersecurity, based on the experience from all SHOW pilot sites, are outlined.

Digital Twins (DT) are increasingly penetrating all sectors of digitized work, with the transport sector and especially the domain of Cooperative Connected and Automated Mobility (CCAM) following rapidly. The current manuscript provides an insight in the DT for transport and CCAM and presents the development approach to be followed in the AUGMENTED CCAM EU funded project, that encompasses in multiple ways and levels the use of DT for the Physical and Digital Infrastructure (PDI) enabled solutions that will be deployed in the test sites of Latvia, Spain and France to assist with Operational Design Domain (ODD) extension of Connected and Automated Vehicles (CAVs).

AbstractThe perception of comfort and safety among passengers of Autonomous Vehicles (AVs) is crucial and significantly influences their adoption in current Public Transport systems. It is essential to align the objective perception with an analysis of vehicle performance data to identify vulnerabilities and factors affecting passenger comfort and safety. This paper presents the first comprehensive correlation between objective and subjective data from autonomous fleets in three well-established pilot locations (Graz, Madrid, Linköping), each using different technologies and experiencing varying environmental conditions. Our analysis (i) revealed significant differences between the three pilot sites in terms of perceived safety and comfort (both perceived and actual) and (ii) confirmed a strong correlation between safety and comfort levels and the vehicles’ behaviour in terms of speed and acceleration, particularly noting the impact of hard braking events as those were defined by the SHOW consortium.

In this work, a strain monitoring system (strip) for assessing the road pavement distress under vehicle loads was developed. The system consists of the sensing element, the data processing and storage unit, and a graphical user interface with post- processing features. The sensing elements were designed to be adhesively bonded on the pavement and are protected by an encapsulating plastic strip. Strain data are sent to a digital reconstruction (the Digital Twin) of a real-life asset (the pavement model) that is frequently and automatically updated through data sampling. This tool provides functionalities to monitor and optimize assets and make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model.

Recent advances in wireless communications enable the communication between vehicles, infrastructure and road users. Although the vehicles become safer and smarter due to Advanced Driver Assistance Systems, Cooperative Intelligent Transport Systems have an important role to ensure that every vehicle recognizes properly the road environment. Cooperative Intelligent Transport Systems applications aims to collect data from different resources like sensors, cameras, vehicles, road infrastructure, road users and analyze them to improve traffic management and road safety. The proposed system provides an innovative technological solution that implements Cooperative Intelligent Transport Systems applications without substantial and costly roadway interventions, including novel Vehicle to Infrastructure, Vehicle to Vehicle and Variable Message Signs functions. This is achieved via low-cost, integrated strips on the road pavement coupled with a cooperative communication framework. The paper presents the overall system and the Cooperative Safety application. The results attested the realization of the system with low latency.

Road safety is a major global concern, as millions of lives are lost every year because of road accidents. Towards an effort to increase road safety, several Internet-of-Vehicle systems have been developed over the last years in order to better monitor vehicle and driver behavior and issue warnings that effectively prevent life-threatening accidents. These systems face a number of challenges including connectivity issues and high installation and/or maintenance costs. The current work introduces the ODOS2020 system, an integrated Internet-of-Vehicles system aiming to increase road safety. The system comprises several On-the-Road Units for vehicle-related data collection from affordable, energy-efficient magnetometers and calculation of critical parameters, such as each passing vehicle’s speed and direction. A Road-Side Unit accumulates data from the On-the-Road Units, sends data to a cloud infrastructure for further analysis and sends dedicated warnings to the drivers based on their road behavior and/or specific traffic conditions via a dedicated Human–Machine Interface. The overall system architecture and the key features of its modules are being presented, as well as the evaluation results of specially designed tests performed in an actual motorway under real use case scenarios. The evaluation results showed both a very good technical performance of the system and a high level of user acceptance. This in turn means that the system can be employed for effective traffic control and road accident avoidance via monitoring of critical vehicle parameters and early warning of the drivers based on their and other drivers’ behavior, road conditions and real-time, unpredictable events.

Cooperative intelligent transport systems (C-ITS) are expected to considerably influence road safety, traffic efficiency and comfort. Nevertheless, their market penetration is still limited, on the one hand due to the high costs of installation and maintenance of the infrastructures and, on the other hand, due to the price of support automated driving functions. A breakthrough C-ITS technological solution was studied, designed, built and tested that is based on the implementation of custom low-cost on-road platforms (named “strips”) that embed micro/nano sensors, communication technologies and energy harvesting to shift intelligence from the vehicle to the road infrastructure. The strips, through a V2X and LTE communication gateway, transmit real-time, reliable and accurate information at lane level about the environmental and road condition, the traffic and the other road users’ position and speed. The exchanged information supports a series of C-ITS functions and services extending equipped vehicles capabilities and providing similar functions to non-equipped ones (including powered two wheelers). The general framework and the technological solution proposed is presented and the results of the field trials, conducted in three pilot sites around Europe, quantify the promising system performance as well as the positive effects of the C-ITS applications developed and tested on driver/rider’s behavior.

Proven positive effects of Cooperative Intelligent Transport Systems (C-ITS) are in many cases prohibited by the non-negligible cost required for the initial installation but also maintenance of the infrastructure and the on-board vehicle intelligent systems that need to be deployed for their operation. In parallel, a series of State of the Art cooperative safety and automated solutions do not exploit data directly originating from the infrastructure and the environment, failing, in this way, to have the most reliable possible safety critical information that is vital to the optimum fulfillment of their objectives; that being primarily the increase of traffic safety. SAFE STRIP (SAFE and green Sensor Technologies for self-explaining and forgiving Road Interactive aPplications) EU funded project envisions to simultaneously address those challenges by introducing a revolutionary C-ITS approach through the placement of low-cost innovative sensorial frameworks on the road pavement surface itself in order to acquire reliable and lane specific traffic and environmental information that is directed through I2X (Infrastructure to Everything) communication to all types of vehicles. The current manuscript presents the vision and objectives, the core use cases serving as the proof of concept of the technological solution built, the implementation approach towards delivering the solution and, finally, the multilayered validation approach anticipated by the Consortium towards delivering a prototype of an as much as possible high technological readiness as well as C-ITS functions evidencing its value.

Over the recent years, the vast variety of widely accessible cloud computing services along with the need to combine transportation services either from public or private providers, have led to the rise of the Mobility as a Service (MaaS) concept. The main feature of MaaS is that it gives users access to a set of heterogeneous transportation services from a single access point (i.e., an app). The ever-increasing adoption of MaaS by service providers introduces a variety of new business models and technologies that can successfully support the design and deployment of MaaS services. However, the outcome of this process depends on the definition of a data model for the transportation services, and its proper implementation that will ensure a seamless service provision within the MaaS platform. Towards this direction, this paper presents a definition of the transportation service data model suitable for a MaaS platform, as well as two different implementation approaches. In particular, a custom-design approach and an ontology-based approach are presented and compared with each other based on a set of key performance indicators (KPIs) such as code complexity, code maintainability, and performance. The two approaches were quantitatively compared using both artificially generated and real data derived from a

The scope of this paper is to recognize the needs, restrictions and priorities in developing effort of a breaking through technology that will achieve to enable Cooperative Intelligent Transportation System (C-ITS) applications in existing road infrastructure, to make roads self-explanatory and forgiving for all road users and all vehicle generations with reduced maintenance cost, full recyclability and added value services, as well as supporting real-time predictive road maintenance functions. The enhanced broad functionality of the system on multiple levels imposes emerging needs and demands that will constitute the groundwork of the Use Cases for the implementation of the project, which are extracted through an iterative user-centered methodology approach and correspond to the target applications of the system. More particularly, the needs, views and priorities of all the relevant stakeholders have been captured through the outputs of 431 respondents participated from 10 countries in on-line and in-depth surveys. From the infrastructure point of view, the complementary investigation of relevant accident/incident and gaps/priorities was based on literature while the legal/operational limitations have been based on the study of relevant Directives and the view of experts. These sources provided important qualitative as well as quantitative outcomes, the aggregation of which led to the prioritisation of the target applications, two of which (last in ranking) are considered to be secondary in the sense that can be implemented with some flexibility.

The current manuscript presents the iterative user-centric approach that has been followed for the prioritisation and full definition of the Use Cases of a highly innovative C-ITS integrated technological solution, newly introduced in the EU funded SAFE STRIP project (GA: n° 723211). This solution aims to shift intelligence from the vehicle to the road infrastructure, in a cost-efficient way, deploying I2X communication technologies and energy harvesting modules to support the micro/nano sensorial networks that will be embedded on the road pavement surface and will transmit real-time information (static and dynamic) about the road condition, the traffic and environmental conditions to the road users. In this way, a series of C-ITS applications can be supported with real-time, reliable, accurate and lane specific information, directly coming from the infrastructure. Next to the description of the overall approach followed, the key aggregated feedback coming from the stakeholders’ point of view is summarised.

The scope of this paper is to recognize the needs, restrictions and priorities in developing effort of a breaking through tec that will achieve to enable Cooperative Intelligent Transportation System (C make roads self-explanatory and forgiving for all road users and all vehicle generations with reduced maintenance cost, full recyclability and added value services, as well as supporting real broad functionality of the system on multiple levels imposes emerging needs and demands that will constitute the groundwork o the Use Cases for the implementation of the project, which are extracted through an iterative user approach and correspond to the target applications of the system. More particularly, the needs, views and priorities of all t relevant stakeholders have been captured through the outputs of 431 respondents participated from 10 countries depth surveys. From the infrastructure point of view, the complementary investigation of relevant accident/incident and gaps/priorities was based on literature while the legal/operational limitations have been based on the study of releva and the view of experts. These sources provided important qualitative as well as quantitative outcomes, the aggregation of wh led to the prioritisation of the target applications, two of which (last in ranking) are considered to be seconda can be implemented with some flexibility. ©2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of the Thirteenth International Conference on Organization and Traffic Safety Management in Large Cities (SPbOTSIC 2018) * Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000 E-mail:mgemou@certh.gr 00 (2018) 000–000 www.elsevier.com/locate/procedia -nc-nd/4.0/)Peer-review under onal Conference on Organization and Traffic Safety Management in -ITS solution on existing road Charilaou-Thermi Road, R-57001, Greece hnology -ITS) applications in existing road infrastructure, to -time predictive road maintenance functions. The enhanced -centered methodology

This manuscript presents the results of the trials that have been realized with student developers in the context of the AEGIS Integrated Project of the 7th European Framework. The aim was to evaluate the added value and the expected impact of two open-source toolkits that have been developed within AEGIS, aiming to support developers in the creation of accessible mobile applications, namely the Android Accessibility Designer Toolkit and the Accessibility Advisor tool. Furthermore, this evaluation process allowed collecting comments for further optimization of the tools before their final release. Evaluation concerned the assessment of the tools themselves by student developers participating in the trials, but also of the accessible applications that student developers were requested to develop with these tools, which were then provided to expert low-vision users for further assessment of their accessibility. Developers were equally distributed into two groups: one Control Group and one Experimental Group. Developers from both groups were given the same exercise and had to meet the same requirements. The Experimental Group tried the AEGIS toolkits for their developments, whereas the Control Group developers freely chose other, non-AEGIS tools. Results showed that 18 % total development time was saved when AEGIS tools were used, and that these developer tools have a big potential to help developers create easily accessible applications.

Prosperity4All is a continuous and dynamic paradigm shift towards an e-inclusion framework building on the architectural and technical foundations of other Global Public Inclusive Infrastructure (GPII) projects aiming to create a self-sustainable and growing ecosystem where developers, implementers, consumers, prosumers and other directly and indirectly actors (e.g. teachers, carers, clinicians) may play a role in its viability and diversity. An agile and dynamic approach is adopted in three evaluation phases, starting with formative evaluations with five internal implementers leading to more summative techniques towards the final evaluation phase where more (n = 25) and external professionals will use the tools and resources available in the project’s repository (DeveloperSpace) to improve and enhance their own products and services. The evaluation approach for the implementers considers three dimensions: (a) the project’s Key Performance Indicators (KPIs), (b) technical validation activities prior evaluation, and (c) three evaluation phases followed by a final impact assessment.

The current manuscript presents the research protocol that has been developed in order to enable the valid transferability of driver simulator results in real traffic conditions. It responds to the weaknesses that have been recognised in the driving simulator validity research field, with respect to the validity of the experimental process being followed as well as to the methodology applied for the comparative analysis of the collected measurements. A case study concerning a semi-dynamic driving simulator is presented. The research hypotheses, the experimental plan and the basic conditions of the analysis methodology that have supported it are described. Trials have been conducted in a semi-dynamic simulator and on-road with 36 drivers (12 trainees, 12 novice and 12 experienced), in "following vehicle" and "free driving" scenarios in highway, rural and urban roads in order to prove the established research hypotheses for a series of driving behaviour metrics.

This paper aims to present the case studies that have been developed in the context of Cloud4all project (Cloud platforms Lead to Open and Universal access for people with Disabilities and for All; CN: FP7-289016). Cloud4all is an international collaborative effort to build and test key elements of the Global Public Inclusive Infrastructure (GPII) (http://gpii.net/). Cloud4all/GPII aims to develop a complete new paradigm in accessibility, by replacing adaptation of individual products and services for a person with automatic-personalisation of any mainstream product or service, using cloud technologies to activate and augment any natural (built-in) accessibility the product or service has, based on a profile of the user's needs (http://www.cloud4all.info). A first step in the implementation path that has already started to be followed in the project has been the identification of those case studies that would be of primary concern for Cloud4all and the overall GPII.