Cellular Network Based Real-Time Urban Road Traffic State Estimation Framework Using Neural Network Model Estimation

This paper presents real time road traffic state estimation framework together with its evaluation. To evaluate the framework, a three-layer Artificial Neural Network model is proposed and used to estimate complete link traffic state. The inputs to the ANN model include probe vehicle´s position, time stamps and speeds. To model the arterial road network the microscopic simulation SUMO is used to generate aggregated speed and FCD export files which are used in the training and evaluation of the ANN model. Besides, real A-GPS data gathered using A-GPS mobile phone on a moving vehicle on the sample roads is used to evaluate the ANN model. The performance of the ANN model is evaluated using the performance indicators RMSE and MPAE and on average the MPAE is less than 1.2%. The trained ANN model is also used to estimate the sample road link speeds and compared with ground truth speed (aggregate edge states) on a 10-minute interval for 1hr. The estimation accuracy using MAE and estimation availability indicated that reliable link speed estimation can be generated and used to indicate real-time urban road traffic condition.