Abstract
Big data applications in transportation and logistics are much discussed before the background of mainly economic improvement potential. For the area of road cargo transportation, this contribution is discussing the use of geospatial data in truck routing especially in the context of autonomous driving and social sustainability concepts. Fleet management and cruise control systems have been established during the last decade in road transportation. However, the stationary vehicle routing before the actual travelled tour is subject only to planning and optimization based on a quite low level of information. For example, geospatial data regarding topography as well as speed limitations and trajectory as well as street elevation and bend characteristics are currently not used but have significant impact on truck speed, fuel consumption and driver workload. Therefore, a conceptual outline as well as a quantitative test simulation for applying geospatial big data in ex ante vehicle routing is provided. This does encompass obvious advantages in economic (reduced transport cost), environmental (reduced transport emissions) as well as social dimensions (reduced driver workload and working time). Further inquiries shall address detailed question as to how geospatial big data could be integrated into the daily routine and processes of vehicle routing in road transportation.
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