Data Fusion-Based Traffic Density Estimation and Prediction

Abstract

Traffic congestion has become a major challenge in recent years in many countries of the world. One way to alleviate congestion is to manage the traffic efficiently by applying intelligent transportation systems (ITS). One set of ITS technologies helps in diverting vehicles from congested parts of the network to alternate routes having less congestion. Congestion is often measured by traffic density, which is the number of vehicles per unit stretch of the roadway. Density, being a spatial characteristic, is difficult to measure in the field. Also, the general approach of estimating density from location-based measures may not capture the spatial variation in density. To capture the spatial variation better, density can be estimated using both location-based and spatial data sources using a data fusion approach. The present study uses a Kalman filter to fuse spatial and location-based data for the estimation of traffic density. Subsequently, the estimated data are utilized for predicting density to future time intervals using a time-series regression model. The models were estimated and validated using both field and simulated data. Both estimation and prediction models performed well, despite the challenges arising from heterogeneous traffic flow conditions prevalent in India.

Keywords:

• Data Fusion
• Heterogeneous Traffic Flow
• Kalman Filter
• Time-Series Analysis
• Traffic Density Estimation and Prediction

Additional information

Funding

The authors acknowledge the funding provided by the Ministry of Urban Development, Government of India, through letter number K-14011/28/2007-UT and the Ministry of Information Technology, Government of India, through letter number 23(1)/2009-IEAD for carrying out this work. The authors alone are responsible for all opinions and any errors in this article.