AMITRAN

Assessment Methodologies for ICT in multi-modal transport from User Behaviour to CO2 reduction

The AMITRAN project will define a reference methodology to assess the impact of intelligent transport systems on CO2 emissions. The methodology shall be used as a reference by future projects and covers both passenger and freight transport through a comprehensive well-to-wheel approach. Different modes are addressed: road, rail, and shipping (short sea and inland navigation).

Objectives

- Develop a CO2 assessment methodology for ICT measures that includes multimodal passenger and freight transport and takes into account the whole chain of effects (from user behaviour to CO2 production);
- Design open interfaces for models and simulation tools implementing the project’s methodology;
- Establish a generic scaling up methodology and publicly available database with statistics to translate local effects into the European level;
- Validate the proposed methodology and its implementation using data available from other projects or studies;
- Produce an online checklist and a handbook that can be used as a reference by future projects.

Duration and Funding

1 November 2011 – 30 April 2014
Co-funded by the 7th Framework Programme, FP7-ICT-2011-7

Consortium

Coordinator: TNO
Partners: DLR, Tecnalia, PTV, ERTICO - ITS Europe, ECORYS, TEAMNET

Factsheet

Download the Amitran project factsheet here.

 

 

Contact

Nuno Quental, +32 (0)2 400 07 34
n.quental@mail.ertico.com

The AMITRAN project will define a reference methodology to assess the impact of intelligent transport systems on CO2 emissions. It will cover both passenger and freight transport by different modes (road, rail, short sea and inland navigation) through a comprehensive well-to-wheel approach.

The AMITRAN project will define a reference methodology to assess the impact of intelligent transport systems on CO2 emissions. It will cover both passenger and freight transport by different modes (road, rail, short sea and inland navigation) through a comprehensive well-to-wheel approach.