This study addresses issues of vehicular mobility scenario generation using the framework called En Route. By applying the framework to planet scale imagery dataset (traffic cameras) we were able to model the traffic demand of a large scale urban scenario (city of London) by means of origin/destination matrix and trips starting within 30 minutes. Along the way, we investigate the relationship of traffic flow (vehicles per minute) vs occupancy, lognormality of trip duration distribution and number of trips started within 30 minutes using available datasets of Berkeley highways and taxicabs of Beijing and San Francisco (SF). We then compare different routing methods and study the effects of system parameters on the number of generated trips.