Link Travel Time Estimation Model Fusing Data from Mobile and Stationary Detector Based on BP Neural Network

2006 ◽  
Author(s):  
Zou Liang ◽  
Zhu Ling-xiang
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Estimation Model ◽  
Link Travel Time

scholarly journals Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Xiyang Zhou ◽  
Zhaosheng Yang ◽  
Wei Zhang ◽  
Xiujuan Tian ◽  
Qichun Bing
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Time Window ◽  
Low Frequency ◽  
Time Estimation ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Link Travel Time

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on theK-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.

scholarly journals DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

2018 ◽  
Author(s):  
Hanyuan Zhang ◽  
Hao Wu ◽  
Weiwei Sun ◽  
Baihua Zheng
Keyword(s):  
Travel Time ◽  
Deep Neural Networks ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Mobility ◽  
Time Cost ◽  
Trajectory Data ◽  
Estimation Model ◽  
New Development ◽  
Supervision Model

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to leverage the natural abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches. 

Accuracy of Link Travel Time Estimation Using Probe Vehicles

2003 ◽  
Vol 36 (14) ◽  
pp. 137-141 ◽  
Author(s):  
Alexandre Torday ◽  
André-Gilles Dumont
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Link Travel Time

Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction

2009 ◽  
Vol 36 (4) ◽  
pp. 580-591 ◽  
Author(s):  
Dongjoo Park ◽  
Soyoung You ◽  
Jeonghyun Rho ◽  
Hanseon Cho ◽  
Kangdae Lee
Keyword(s):  
Travel Time ◽  
Mean Square Error ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Mean Square ◽  
Loop Detector ◽  
Link Travel Time ◽  
Travel Time Forecasting

With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.

scholarly journals Urban link travel time estimation using traffic states-based data fusion

2018 ◽  
Vol 12 (7) ◽  
pp. 651-663 ◽  
Author(s):  
Lin Zhu ◽  
Fangce Guo ◽  
John W. Polak ◽  
Rajesh Krishnan
Keyword(s):  
Data Fusion ◽  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Link Travel Time
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