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 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 Measuring Recurrent and Nonrecurrent Traffic Congestion

2003 ◽  
Vol 1856 (1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexander Skabardonis ◽  
Pravin Varaiya ◽  
Karl F. Petty
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
San Francisco ◽  
Traffic Congestion ◽  
Real Life ◽  
Time Variability ◽  
Loop Detectors ◽  
Bay Area ◽  
Freeway Corridors ◽  
Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

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