Traffic flow optimization at an intersection helps to maintain a smooth urban traffic flow. It can reduce travel time and emission. Regularly, new algorithms are introduced to control approaching vehicles and traffic light phases. Reinforcement learning and traffic optimization is a novel combination that is used by the research community. This thesis suggests a methodology to reduce travel time and emission of vehicles for a specific intersection design. Here the author provides a clear solution by considering the driving route of approaching the vehicle to the intersection. By using reinforcement learning and route information, this research suggests a vehicle ordering mechanism in order to improve the throughput of the intersection. Before proposing the solution, the author gives a thorough research review of previous studies. Various findings regarding various Reinforcement learning algorithms and how it has used to traffic optimization are explained in Literature review. Further, the author is using GLOSA as a baseline to evaluate the new solution. Several types of GLOSA variations are discussed in this report. A new approach, which can be seen as an extension of the existing GLOSA algorithms, is described in the concept chapter. A deep Q network approach and a rule-based policy are introduced as the solution. The proposed solution was implemented and evaluated. The author was able to achieve promising results from a rule-based policy approach. Further, the issues related to both approaches were discussed in detail and solutions were given to further improve the proposed solutions.