This study aims to address the need for alternative, guiding guidance for smart transportation systems, which have become increasingly important with the increasing number of people and vehicles, and the consequent need for location determination applications in mobile vehicles, such as those insufficient or incompletely accessible GPS signals in large areas, tunnels, or areas surrounded by tall buildings. A low-cost system, suitable for use both within and outside cities, is designed to estimate the vehicle's location without GPS information or any verification signals, using the vehicle's last known location along with inertial information such as speed, altitude, and compass. Furthermore, this system is intended to be an alternative navigation system solution for estimating the location of military vehicles, both on land and at sea, in the event of satellite disruption during times of war or political crisis. The input data for the developed system consists of speed information from the vehicle's electronic circuitry, and information from the handwheel and accelerometer sensors and mobile phones. Data collected from the sensors is used iteratively in the next location calculation, along with the previously known or calculated position. By analyzing movement and road patterns and utilizing the K-means clustering algorithm for location estimation, we utilize inertial data rather than relying on traditional GPS signals, enabling more reliable location estimation. Furthermore, we present a system that has the potential to provide more effective traffic management by identifying different road patterns. This approach aims to address shortcomings in existing traffic management systems and provide more effective traffic management.