Improving a Travel-Time Estimation Algorithm by Using Dual Loop Detectors

Author(s):  
J. W. C. van Lint ◽  
N. J. van der Zijpp

An algorithm is presented for off-line estimation of route-level travel times for uninterrupted traffic flow facilities, such as motorway corridors, based on time series of traffic-speed observations taken from the sections that constitute a route. The proposed method is an extension of the widely used trajectory method. The novelty of the presented method is that trajectories are based on the assumption of piecewise linear (and continuous at section boundaries) vehicle speeds rather than piecewise constant (and discontinuous at section boundaries) speeds. From these assumptions, mathematical expressions are derived that describe the trajectories within each section. These expressions can be used to replace their existing counterparts in the traditional trajectory methods. A comparison of the accuracy of the new method and of the existing method was carried out by using simulated data. This comparison showed that the root-mean-square error ( RMSE) value for the new method is about half the RMSE value for the existing method. When this RMSE is decomposed in a bias and a residual error, it turns out that the existing method significantly overestimates the travel time. However, the largest part of the reduction of the RMSE value is still caused by a reduction of the residual error. In other words, if both methods are corrected for their bias, the new method performs significantly better.

2009 ◽  
Vol 42 (15) ◽  
pp. 383-390
Author(s):  
W.K. Mak ◽  
F. Viti ◽  
S.P. Hoogendoorn ◽  
A. Hegyi

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.


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