Public transport is a fast and convenient way of travel, but there are many issues related to it. Challenges in current public transport system are: how to estimate the exact arrival time of vehicle and real tracking of vehicle. Solution of these two problems directly save the user time and provide better management for scheduling of vehicles. Many proposal exist in the literature to address above mentioned issues. Keeping the need of intelligent transportation system, this paper provides comparative analysis of all the state-of-art existing proposals. Tracking the vehicles generally takes two types of data: historical, and real time data. For real time tracking of vehicles, Global Positioning System (GPS), sensors, Internet of Things (IoT) devices, etc are used. Due to generation of huge amount of data from IoT enabled devices present in transport system, kalman filtering, artificial neural network, data analytics and machine learning are also used for better scheduling of vehicles. In last section we provide the open issues and challenges that needs to be taken care while designing the Intelligent Transport System (ITS).

Vehicle Tracking, ITS, GPS, Smart City, Historical data, Real time data, Sensor, IoT

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