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The rapid growth rate in urban population and flow of technologies has fastened the magnitudes and patterns of urban transportation network. The number of vehicles has increased tremendously in the metropolitan areas of the city. Whereas the traffic control involves the attempts to make the movements as fast, efficient and safe as possible with minimum damage to the local or regional environment. This study will create a relationship among travel patterns (individual behavior), transportation facilities (planning and decision-making processes),sustainable economic growth, social well-being, and environmental context of Lahore that forms the basis of transportation analysis and policy decisions in the governmental agencies. The real challenge comes from alarming inner city vehicle increases in the country. Recent years have seen an unprecedented growth of traffic in Pakistan. There are multiple reasons for this rapid increase which is greatly evident in big cities like Karachi and Lahore. This has raised a number of concerns and questions for research and modeling of advanced and efficient traffic patterns.This paper is a detail study of multidimensional traffic pattern of Lahore, its issues, flow and intensity; peak congestion hours and periods of extreme pressure at some of the major roads in the city finally ending at a focused case study (Shahrah-e-Quaid-eAzam). On the basis of systematically collected quantitative data of the sampled main roads, study provides guidelines and policy measures for the government to control this menace. Keeping in view the geographical limitations and socio economic structure of the city, it also suggests remedies to overcome traffic problems. Paper proposes a researched based practicum model to help set an efficient and sustainable traffic pattern. The rationale of the study is to create Vehicle Density History (VDH) for selected chowks at different times of the day. i.e peak, medium and low hours. Once a comprehensive VDH is created, it can help for integrated planning for efficient traffic flow patterns and management.