Simulation-based benefit analysis of pattern recognition application in intelligent transportation systems

Modeling and simulation of transportation systems are extensively used because of high costs and potential safety and security issues associated with its prototyping in a real traffic. Modeling and simulation play a key role in understanding real systems through abstraction and evaluating new technologies. In this paper, we conducted a simulation-based analysis to assess potential benefits of applying pattern recognition in Intelligent Transportation Systems (ITS). We used integrated transport modeling and simulation software called AIMSUN to develop a model for a real highway road. We also developed a number of plug-in software modules which implement pattern recognition, route guidance, and message communication features. We used the developed model in two congestion-oriented scenarios, called reactive and proactive. The simulation results were promising and showed that integrating pattern recognition into transportation systems improves mobility, reduces travel time, and reduces congestion resolution time. In particular, there is 5% to 30% reduction in the average travel time and 8% to 41% reduction in the average congestion resolution time.