In this paper, a new clustering technique for developing a driving cycle by using a k-shape time series-based approach is proposed. Driving cycle is important for testing and analyzing vehicle performance, fuel efficiency, and emissions. Traditional and conventional methods of developing driving cycles often rely on manual approaches, which can be time-consuming, require high manpower, and may not represent the diverse driving patterns accurately. This approach emphasizes the k-shape algorithm, specifically designed for time series data. This technique generally groups similar driving patterns together by identifying the common trends and variations in the real-world driving data. The k-shape algorithm has many advantages, and one of the most significant advantages is the ability to eliminate noise, scale to large datasets, and preserve the temporal characteristics of the driving data. By applying the k-shape clustering technique, a more representative and comprehensive set of driving cycles can be obtained, which reflects the real-world conditions, enhancing the reliability of vehicle testing and the accuracy of performance assessments. Lastly, the k-shape algorithm is implemented on the Ipoh city driving cycle data for the clustering of the traffic conditions in Ipoh.