Traffic State Estimation under Uncertain Sensor Data

 

Capturing the traffic system characteristics in real time is one of the basic requirements of any Intelligent Transportation Systems (ITS) applications. With the advancement in the ITS sector, several types of nonintrusive traffic sensors using latest technologies are available for vehicle sensing. In order to make the task of data collection, processing and providing real time information to users faster, there is a need for automating the associated processes. However, these processes are highly challenging under Indian traffic condition, which is heterogeneous in nature and lacks lane discipline. The automated sensors, which are working well elsewhere, may not be able to perform accurately under such conditions leading to the automated data to be highly erroneous. This in turn would result in reduced performance of the end application and hence a reduction in accuracy and reliability of the end application is a major concern. The present study addresses these challenges, taking the real time estimation of speed and density as sample applications. Artificial Neural Networks (ANN), which is a data driven approach, and a dynamical systems based approach using the Kalman Filter (KF), which is less data demanding in terms of data, were selected as the techniques for estimation. With ANN, location based speed estimation was carried out using data from two different sensors representing erroneous and non-erroneous data. Stream based density and speed estimation were also carried out while using erroneous data as inputs to a network trained with accurate data. For the dynamical systems approach based method, density was estimated using the conservation principle and speed was estimated using a dynamic speed equation formulated by incorporating appropriate speed-density relationship for the specific traffic under study. In order to corroborate the developed estimation schemes, actual density values calculated using manually extracted flow data and speed that were obtained from travel time data collected by using Bluetooth sensors were used.

 

The results indicate that if the offline training of ANN can be carried out with correct data, ANN can take into account the errors in inputs. The estimation accuracy of the dynamical systems approach based method was checked before and after incorporating the sensor error statistics into the estimation scheme. The results indicated a significant improvement in the estimation accuracy after incorporating the sensor error statistics. On overall comparison between two approaches, speed estimation accuracy using ANN and the dynamical systems approach based method with error statistics were comparable for both shorter and longer durations. On the other hand, density estimation accuracy for shorter durations using the dynamical systems approach based method with error statistics was better. From this study, it was observed that both ANN and the dynamical systems approach based method can deal with the erroneous automated sensor data and are able to provide reasonably accurate results. This shows a promising possibility for user agencies to implement such applications even if the real time data source is erroneous.