In intelligent transportation systems, most of the research work has focused on lane change assistant systems. No existing work considers minimizing the interruption of traffic flow by maximizing the number of lane changes while eliminating the collisions. In this thesis, we develop qualitative and quantitative approaches for minimizing the interruption of traffic flow for three lane scenarios and show that we can extend our approach to any random number of lanes. The algorithm we propose in this thesis is able to achieve the maximum number of lane changes provided that only one vehicle per group (novel concept which is described in this thesis) is allowed to change lanes at a time. Simulation results show that our approach provides much better performance when compared with different lane change algorithms without incurring large overhead, and is hence suitable for online use.