Urban arterial travel time is one of the most critical components for advanced traveler information and advanced traffic management systems. This paper used state space neural network (SSNN) with adaptive filters to predict the urban arterial travel time. The SSNN's network topology is derived from urban travel time prediction problem, so the design of SSNN reflects the relationships that exist in physical traffic systems. To improve SSNN effectiveness, the adaptive filters is proposed to train the SSNN instead of conventional approaches. Model performance was tested with urban arterial data of Odem. Performance of the proposed model is compared with that of SSNN and BP neural network. Results of the comparisons indicate that the proposed model predicts complex nonlinear urban arterial travel times with satisfying effectiveness, robustness and reliability.
Urban Arterial Travel Time Prediction Using State Space Neural Network with Adaptive Filters
Second International Conference on Transportation Engineering ; 2009 ; Southwest Jiaotong University, Chengdu, China
2009-07-29
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Highways and roads , Information technology (IT) , Transportation management , Construction , Freight transportation , Predictions , Neural networks , Water transportation , Filters , Air transportation , Rail transportation , Traffic safety , Urban areas , Optimization , Public transportation , Travel time , Traffic management
Urban Arterial Travel Time Prediction Using State Space Neural Network with Adaptive Filters
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