A Study on Co-modality and Eco Driving Mobility
10.67 12.10
2011
TML's Data Enrichment Group investigated market opportunities for Toyota Motor Europe within the context of the European White Paper on transport, with a focus on modal shift and technology outlook. TML analysed mobility data and developed scenarios at micro, meso, and macro levels to identify long-term opportunities for Eco Driving and emission reduction.
In this study, TML's Data Enrichment Group (DEG) identified unique opportunities for Toyota Motor Europe (TME) in the context of the European Commission's White Paper entitled "Roadmap to a Single European Transport Area - Towards a competitive and resource efficient transport system". DEG conducted an in-depth case study to assess the potential of a modal shift and technological prospects and, within an Eco Driving project, identified long-term market opportunities for TME.
Our DEG group has extensive expertise in data mining and data enrichment and used this to analyse the accurate mobility data sourced from TME. This included the analysis of travel measurements and the development of scenarios that took into account parameters such as emissions, energy, time, and cost.
DEG used and developed the necessary computational tools to analyse the data in such a way that a deeper understanding of the underlying structures in the data emerged. This allowed to estimate the micro-level of mobility (i.e., driver behaviour), the meso- (i.e., trip planning) and the macro-level (i.e., long-term mobility planning).
In this study, TML's Data Enrichment Group (DEG) identified unique opportunities for Toyota Motor Europe (TME) in the context of the European Commission's White Paper entitled "Roadmap to a Single European Transport Area - Towards a competitive and resource efficient transport system". DEG conducted an in-depth case study to assess the potential of a modal shift and technological prospects and, within an Eco Driving project, identified long-term market opportunities for TME.
Our DEG group has extensive expertise in data mining and data enrichment and used this to analyse the accurate mobility data sourced from TME. This included the analysis of travel measurements and the development of scenarios that took into account parameters such as emissions, energy, time, and cost.
DEG used and developed the necessary computational tools to analyse the data in such a way that a deeper understanding of the underlying structures in the data emerged. This allowed to estimate the micro-level of mobility (i.e., driver behaviour), the meso- (i.e., trip planning) and the macro-level (i.e., long-term mobility planning).
