Kanok Boriboonsomsin

Effective incentive strategies have played a major role in the electrification of on-road vehicles. Similar approaches to the off-road sector will be needed to continue decarbonizing the transportation sector. Estimating cost-effectiveness with given incentive funds is crucial to successfully develop and implement financial incentive strategies. Because of the lack of cost information for electric off-road equipment, forecasting cost-effectiveness of electrifying the off-road fleet is not feasible. We developed cost projection models for major components of off-road equipment and combined them into an equipment cost simulation tool. This simulation tool was then used to estimate NO x , particulate matter, and CO 2 emission-based cost-effectiveness when diesel off-road equipment is replaced with equivalent electric equipment. Use of the developed cost-effectiveness framework in developing incentive strategies was also demonstrated. Finally, a sensitivity analysis was conducted to address potential variations of the assumptions made for projecting the cost of components and the resulting cost-effectiveness estimates. The framework demonstrated in this paper will inform governmental inventive funding programs to design the most cost-effective strategies, considering equipment vocation, model year, and usage.

Vehicle automation and connectivity bring new opportunities for safe and sustainable mobility in urban and highway networks. Such opportunities are however not directly associated with traffic flow improvements. Research on exploitation of connected and automated vehicles (CAVs) toward a more efficient traffic currently remains at a theoretical level, and/or based on simulation models with limited reliability. Furthermore, testing CAVs in the real world is still costly and very challenging from an implementation perspective. A possible alternative is to use automated robots. By designing and testing both the low- and the high-level controllers of CAVs, it is indeed possible to reach a better understanding of the challenges that future vehicles will need to face. Robotic applications can effectively test these challenges within a wide variety of research communities—for example, via robotic competitions. Along this direction, the Joint Research Centre has organized the first European robotic traffic competition for automated miniature vehicles. Each team participated with four robots and was judged based on a set of indicators that assess the collective behaviors of the vehicles. Results show the suitability of the methodology with different teams proposing completely different approaches to deal with the challenge and thus achieving different results. Future competitions may further raise awareness about the possibility of using CAVs to improve traffic and to engage with a broader community to design systems that are really capable of achieving this goal.