Creating a driving cycle that replicates traffic behavior is crucial for developing an emission and energy consumption model. This study introduces a methodological framework for constructing a driving cycle for passenger cars in diverse traffic environments. Data collection involved installing a GPS-based video V-Box de-vice in passenger cars operating under real-world traffic conditions. The data analysis involves analyzing base data and evaluating micro-trips. A speed-acceleration frequency matrix is then generated to estimate five key driving parameters: percent time in acceleration (PTA), percent time in deceleration (PTD), percent time in idle (PTI), percent time in cruise (PTC), and average speed. Subsequently, the driving data is transformed into multiple micro-trips based on the driving parameters. A random selection technique was used for these transformed micro-trips to match them with the base data. These selected micro-trips are then sequenced to form a representation of a driving cycle for passenger cars operating under current traffic conditions. Performance validation of the developed driving cycle is conducted through a comparative analysis with standard driving cycles.