Periodic Reporting for period 2 - IN2CCAM (Enhancing Integration and Interoperability of CCAM eco-system (IN2CCAM))
Période du rapport: 2024-05-01 au 2025-10-31
IN2CCAM consortium aims to accelerate the implementation of innovative CCAM technologies and systems for passengers and goods.
The goal is providing benefits to all citizens by implementing a full integration of CCAM services in the transport system.
To this aim the approach is based on the implementation and integration of enhanced Physical, Digital and Operational Infrastructures (PDOIs) to enrich CCAM services and increase safety and traffic efficiency.
To foster the advance of the CCAM solutions, the IN2CCAM consortium has set five general objectives:
1: Determining the concepts of fleet and traffic management in the CCAM eco-system considering PDOI to optimize the mobility of people and goods. To this aim IN2CCAM will consider the needs and requirements of society and individual in to guarantee safety, environment and inclusiveness.
2: Design, implement and test the physical, digital infrastructure intermodal interfaces between the platforms of the CCAM ecosystem and the local platforms for fleet and traffic management also by implementing services for the interoperability in multimodal transport systems in different geographic locations.
3: Design, implement and test advanced simulation and digital twin models to assess new traffic management strategies for CCAM.
4: Design adaptive traffic optimization and flow balancing strategies based on real-time traffic intensity, prediction of traffic situations and green times adjustment in line with real time traffic volume, involving also users in real time.
5: Proposing, developing and testing new effective cooperation, governance and business models for operating CCAM services integrated in the real-life fleet and traffic management systems.
The goal is providing benefits to all citizens by implementing a full integration of CCAM services in the transport system.
To this aim the approach is based on the implementation and integration of enhanced Physical, Digital and Operational Infrastructures (PDOIs) to enrich CCAM services and increase safety and traffic efficiency.
To foster the advance of the CCAM solutions, the IN2CCAM consortium has set five general objectives:
1: Determining the concepts of fleet and traffic management in the CCAM eco-system considering PDOI to optimize the mobility of people and goods. To this aim IN2CCAM will consider the needs and requirements of society and individual in to guarantee safety, environment and inclusiveness.
2: Design, implement and test the physical, digital infrastructure intermodal interfaces between the platforms of the CCAM ecosystem and the local platforms for fleet and traffic management also by implementing services for the interoperability in multimodal transport systems in different geographic locations.
3: Design, implement and test advanced simulation and digital twin models to assess new traffic management strategies for CCAM.
4: Design adaptive traffic optimization and flow balancing strategies based on real-time traffic intensity, prediction of traffic situations and green times adjustment in line with real time traffic volume, involving also users in real time.
5: Proposing, developing and testing new effective cooperation, governance and business models for operating CCAM services integrated in the real-life fleet and traffic management systems.
The reference architecture of the CCAM platform has been proposed. IN2CCAM services were developed and integrated into the existing Intelligent Transport Systems by defining intermodal interfaces such as shared mobility, network load balancing, and goods management and delivery. Advanced simulation models were designed to analyse the impact of the proposed CCAM services in a virtual environment. In addition, the Digital Twin (DT) components and the optimal control algorithms were proposed based on Artificial Intelligence (AI) technologies such as Deep Neural Networks (DNN) and Deep Reinforcement Learning (DRL).
The demonstrations in the Lead LLs and the simulations in the Follower LLs have been conducted . The impact of IN2CCAM solutions from different perspectives was evaluated. A common methodology and Key Performance Indicators (KPIs) were defined, and data were collected from the LLs. User attitudes and social acceptance were studied through surveys and interviews, while scalability was analysed using simulations and DTs. Finally, traffic efficiency, safety, environmental, and economic impacts were measured with real transport data.
An inclusive evidence-based framework for an effective governance of CCAM-enabled traffic and fleet management solutions have been designed by organizing meeting to collect needs of policy makers, regulatory authorities, service providers and transport operators. New cost-efficient multi-stakeholder business and operating models are provided by relying on an efficient integration of CCAM and fleet and traffic management systems, based on the sharing of both benefits and risks associated with the future operation of the IN2CCAM innovation. Finally, IN2CCAM provided regulatory and policy recommendations targeted at EU and international decision makers, proposing actions facilitating widespread of the IN2CCAM innovations based on the project demonstration outcomes in the LLs.
The demonstrations in the Lead LLs and the simulations in the Follower LLs have been conducted . The impact of IN2CCAM solutions from different perspectives was evaluated. A common methodology and Key Performance Indicators (KPIs) were defined, and data were collected from the LLs. User attitudes and social acceptance were studied through surveys and interviews, while scalability was analysed using simulations and DTs. Finally, traffic efficiency, safety, environmental, and economic impacts were measured with real transport data.
An inclusive evidence-based framework for an effective governance of CCAM-enabled traffic and fleet management solutions have been designed by organizing meeting to collect needs of policy makers, regulatory authorities, service providers and transport operators. New cost-efficient multi-stakeholder business and operating models are provided by relying on an efficient integration of CCAM and fleet and traffic management systems, based on the sharing of both benefits and risks associated with the future operation of the IN2CCAM innovation. Finally, IN2CCAM provided regulatory and policy recommendations targeted at EU and international decision makers, proposing actions facilitating widespread of the IN2CCAM innovations based on the project demonstration outcomes in the LLs.
Determine the concepts of fleet and traffic management in the CCAM eco-system considering PDOI to optimize the mobility of people and goods.
Design, implement and test the physical and digital infrastructure
Four intermodal interfaces are implemented and tested by the platforms of the CCAM ecosystem for fleet and traffic management. Physical infrastructures are implemented and tested in the Lead LLs: dedicated lanes, intelligent traffic lights, sensors connected with the vehicles. A set of services are developed and tested in the lead LLs or in the follower LLs by the simulation: route planner service, car pooling service, interoperability solutions in multimodal transport systems.
Design, implement and test advanced simulation and Digital Twin (DT) models.
Seven simulations models and two DT models are designed and implemented. The simulation models allow testing the services and the traffic control approaches including large fleet of autonomous vehicles to assess new traffic management strategies for CCAM. A novel strategy for the last mile delivery using autonomous vehicles has been proposed and tested by simulation. A DT is tested and used in the Turin LL for the implementation of a rerouting strategy.
Design adaptive traffic optimization and flow balancing strategies.
For optimizing traffic and balancing vehicle flow some services are developed based on real-time traffic intensity, prediction of traffic situations in the immediate future and green times adjustment. The services are designed by using Artificial Intelligence based solutions. The tests and the filed evaluations proved that the proposed services enabled the new traffic management, decreased traffic congestion and emissions. Suitable KPIs are computed and compared in the LLs.
Proposing, developing and testing new effective cooperation, governance and business models
New effective governance and business models were proposed and developed for operating CCAM services integrated in the real-life fleet and traffic management systems including the interaction between different transportation modes. These results have been reached by workshops and working groups involving all the LLs..
Design, implement and test the physical and digital infrastructure
Four intermodal interfaces are implemented and tested by the platforms of the CCAM ecosystem for fleet and traffic management. Physical infrastructures are implemented and tested in the Lead LLs: dedicated lanes, intelligent traffic lights, sensors connected with the vehicles. A set of services are developed and tested in the lead LLs or in the follower LLs by the simulation: route planner service, car pooling service, interoperability solutions in multimodal transport systems.
Design, implement and test advanced simulation and Digital Twin (DT) models.
Seven simulations models and two DT models are designed and implemented. The simulation models allow testing the services and the traffic control approaches including large fleet of autonomous vehicles to assess new traffic management strategies for CCAM. A novel strategy for the last mile delivery using autonomous vehicles has been proposed and tested by simulation. A DT is tested and used in the Turin LL for the implementation of a rerouting strategy.
Design adaptive traffic optimization and flow balancing strategies.
For optimizing traffic and balancing vehicle flow some services are developed based on real-time traffic intensity, prediction of traffic situations in the immediate future and green times adjustment. The services are designed by using Artificial Intelligence based solutions. The tests and the filed evaluations proved that the proposed services enabled the new traffic management, decreased traffic congestion and emissions. Suitable KPIs are computed and compared in the LLs.
Proposing, developing and testing new effective cooperation, governance and business models
New effective governance and business models were proposed and developed for operating CCAM services integrated in the real-life fleet and traffic management systems including the interaction between different transportation modes. These results have been reached by workshops and working groups involving all the LLs..