scholarly journals A Gini coefficient based evaluation on the reliability of travel time forecasting

2019 ◽  
Vol 31 (4) ◽  
pp. 314-319 ◽  
Author(s):  
Soong-Bong Lee ◽  
Seongkwan Mark Lee ◽  
Ki-Young Lee
Keyword(s):  
Travel Time ◽  
Gini Coefficient ◽  
Travel Time Forecasting

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

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.

scholarly journals A Kind of Urban Road Travel Time Forecasting Model with Loop Detectors

2016 ◽  
Vol 12 (2) ◽  
pp. 9043835 ◽  
Author(s):  
Guangyu Zhu ◽  
Li Wang ◽  
Peng Zhang ◽  
Kang Song
Keyword(s):  
Travel Time ◽  
Forecasting Model ◽  
Urban Road ◽  
Loop Detectors ◽  
Travel Time Forecasting

A Study on the Construction of Past Travel Time Pattern for Freeway Travel Time Forecasting—Focused on Loop Detectors -

2007 ◽  
Vol 11 (1) ◽  
pp. 14-29 ◽  
Author(s):  
Dong-ho Kim ◽  
Dongjoo Park ◽  
Jeong-hyun Rho ◽  
Seungkirl Baek ◽  
Seong Namkoong
Keyword(s):  
Travel Time ◽  
Time Pattern ◽  
Loop Detectors ◽  
Travel Time Forecasting

Empirical Analysis of a Travel-Time Forecasting Model

2007 ◽  
Vol 39 (4) ◽  
pp. 397-417 ◽  
Author(s):  
Jinsoo You ◽  
Tschangho John Kim
Keyword(s):  
Travel Time ◽  
Empirical Analysis ◽  
Forecasting Model ◽  
Travel Time Forecasting

scholarly journals A traffic flow simulator for short-term travel time forecasting

2002 ◽  
Vol 36 (3) ◽  
pp. 265-291 ◽  
Author(s):  
William H. K. Lam ◽  
K. S. Chan ◽  
John W. Z. Shi
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Short Term ◽  
Flow Simulator ◽  
Travel Time Forecasting

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.

Weather-dependent road travel time forecasting using a neural network

2008 ◽  
Author(s):  
G. Ghiani ◽  
D. Gullì ◽  
F. Mari ◽  
R. Simino ◽  
R. Trunfio
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Travel Time Forecasting
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