The universal availability of pervasive traffic data can fundamentally change our perception of looking towards transportation systems, diagnosing their issues, and building transport policies to improve their functionality. In the present paper, we demonstrate how such data can be used in classic transportation system analysis to obtain quick insights into the travel behavior prevailing in the cities. Three mobility-related metrics were estimated for twenty-six different world cities (i) trip lengths and the time required to complete them, (ii) congestion index, and (iii) vehicles kilometers traveled. The computational pipeline to estimate these metrics using the pervasive data from smartphones is discussed briefly. The relationship of estimated metrics with the broader socioeconomic profile of cities revealed some crucial insights into the mobility pattern of urban dwellers. Broadly, the results demonstrated the potential environmental impact of observed mobility patterns and directions that can be pursued to attain sustainable mobility.