The Novel Positioning Algorithm Based on Cloud Computing - A Case Study of Intelligent Transportation Systems

Abstract

In recent year, the development of intelligent transportation system (ITS) has become more and more popular to improve the road safety and traffic efficiency amid the global trend of rapid urbanization and population growth. The traffic information estimations from cellular network data are more immediately, cost-effective, and easy to deploy and maintain than traditional methods. Therefore, we propose a novel speed estimation method based on fingerprint positioning algorithm (FPA). In experiments, we compare the estimated positioning information and speed information with the real information obtained from global position system (GPS) receiver. The results show that the average error of location determination by using FPA is 36.11 meters. For speed estimation, the average error ratio of speed estimation by using FPA is 3.39%. Finally, we adopt the MapReduce algorithm and propose a modified column-based data model (CDM) scheme to solve the space-wasting and time-wasting problems which are due to the sparse matrices generated by FPA. This approach will be feasible to estimate the overall traffic information for ITS improvement.