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Research is a key factor for a successful reduction in greenhouse gas (GHG) emissions from transport. This paper summarizes the main results of REACT, a project co-financed by the European Commission, which aimed to develop a European Strategic Research Agenda for low GHG transport. A literature review and a multi-stage expert consultation process were used to map technological and non-technological research areas and evaluate them according to different criteria (i.e., GHG emissions reduction, costefficiency, feasibility, timeframe of research stages). We consulted the research agendas of the European Technologies Platforms on transport and current EU research programs. Expert opinions were collected through web forms, interviews and participation in structured workshops. The REACT Research Agenda identified the following research priorities for a more climate-friendly transport system by 2030: (a) in the short term, costeffective solutions consist of (1) more efficient, lighter vehicles with advanced internal combustion engines, (2) reducing road transport demand and (3) fostering GHG emission legislation; (b) in the medium/long-term, the focus shifts towards (1) electric vehicles and Downloaded by [Cardiff University Libraries], [Lorraine Whitmarsh] at 07:18 24 November 2015 hydrogen, (2) Intelligent Transport Systems, and (3) spatial planning and economic and social measures to reduce transport demand. In addition, one of the main findings identified strong links between technology research and planning, social sciences and economy.

The past decades have seen a great deal of research on algorithms for shortest path problems. However, real-world systems like route planners and mobile navigation systems require to take into account some additional conditions. Our work concerns a truck route planner for real-time navigation developed for an Italian firm and tested on real world data of Milano road network (about 61,000 nodes and 106,000 arcs). Given the vehicle GPS position and the destination, the route planner allows to find in a few seconds a path between them which minimizes simultaneously travel time, travel cost and risk. Beside the multi-objective optimization and the CPU efficiency, other challenging features faced by the algorithm that supports the route planner are the time-dependency of some attributes (e.g. the travel costs due to the congestion charge ruling the access to limited traffic zone in the Milano centre) and the presence of forbidden turns. Results on the real network of Milano are obtained and discussed

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