Link Speed Estimation and Incident Detection Using Clustering and Neuro-fuzzy Methods
The primary issues in the development of advanced traveler information systems (ATIS) within the intelligent transportation systems (ITS) framework are the optimal estimation of freeway travel time and incident detection with reasonable accuracy. Typically ATIS aims to provide route guidance based on the traveler’s requirements using the information gathered from various sources such as loop detectors and probe vehicles. Until recent times traffic information was collected from mostly stationary devices and analyzed. In this research paper we consider data acquired form primarily GPS-based sources. The aim of the research is a comprehensive analysis of collected information from GPS sources using the fuzzy c-means algorithm (FCM) which provides the estimation of link speed. The modified FCM is used to extract patterns from the traffic data collected from a busy network of downtown streets. The link speed estimation is performed using smoothing techniques. Finally we apply the neuro-fuzzy algorithm to the task of incident detection from the traffic patterns.
KeywordsIntelligent Transportation System Traffic Information Transportation Research Part Link Travel Time Incident Detection
- 1.Hoppner, F.: A Contribution to Convergence Theory of Fuzzy c-Mean and Derivatives. IEEE Transactions of Fuzzy System 11(5) (2003)Google Scholar
- 10.Sisiopiku, V., Rouphail, N.: Exploratory Analysis of the Correlations Between Arterial link Travel Times and Detector Data from Simulation and Field Studies. Advance Working Paper No. 25 (October 1993)Google Scholar
- 11.Nelson, P., Palacharla, P.: A neural network model for data fusion in Advance. In: Proceedings of Pacific Rim Conference, Seattle, Wash., pp. 237–243 (1993)Google Scholar