Effective transportation of people and goods requires an efficient positioning service. This service currently relies to a certain extent on Global Navigation Satellite Systems (GNSS). However, the problems associated with obtaining an accurate and reliable position solution in challenging environments are often underestimated and need to be dealt in greater detail. This paper discusses the concept of GNSS moving baseline station in critical ITS applications and provides preliminary results from the testing of the technique in variant observation scenarios.

Minimizing fuel consumption while maximizing driving performance (i.e., less travel time, increased safety and less gas emissions) is a key factor for the successful implementation of any transport system. In this regard, monitoring driving behaviour and converting this information into real-time advice to drivers can benefit seriously the performance of transport and mobility services. This paper presents the design and implementation of a prototype system and a BI (Business Intelligence) model for fuel consumption optimization for heavy-duty vehicles. The system consists of an OBDH prototype, various external sensors (including GNSS/INS and environmental ones), and an on-purpose built FMS (Fleet Management System) bus logger. The paper revisits the concept of BI and attempts knowledge transfer from the field of data analytics for business information to a pure engineering problem. Preliminary analyses using real truck trajectory data reveals and quantifies the effect of driving behaviour on fuel consumption while it indicates the potential of the system to transform into an integrated, self-trained driver coaching system.

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