An Energy-Saving Approach for Real-Time Highway Traffic Estimation Using GPS-Enabled Smartphones



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This paper presents a microscopic traffic estimation algorithm for smartphones by employing their built-in probes such as GPS and acceleration sensors to increase the accuracy of real-time traffic condition estimation without significantly increasing the smartphones’ energy consumption. In this approach, real-time traffic data is collected through the smartphones of participating users traveling on urban roads. A new reporting algorithm is provided on the clients’ side to minimize the amount of time the smartphone maintains connection to the server. Based on the data received from each individual smartphone, real-time traffic conditions and the level of service (LOS) are estimated on the server side by applying the Kalman Filtering algorithm and link aggregating speed algorithm. An iOS application is developed to work as a sample client side smartphone node. Simulations of three different traffic scenario are also created to evaluate the performance of the algorithm. Simulation results show that the proposed algorithm requires less energy usage than existing methods without sacrificing the accuracy of real-time traffic estimations.



Smartphone, Real-time traffic, Kalman filter