Network-wide on-line travel time estimation with inconsistent data from multiple sensor systems under network uncertainty

2017 ◽  
Vol 14 (1-2) ◽  
pp. 110-129 ◽  
Author(s):  
Hu Shao ◽  
William H. K. Lam ◽  
Agachai Sumalee ◽  
Anthony Chen
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Sensor Systems ◽  
Inconsistent Data ◽  
On Line ◽  
Multiple Sensor

Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model

2015 ◽  
Vol 776 ◽  
pp. 80-86
Author(s):  
Amirotul M.H. Mahmudah ◽  
A. Budiarto ◽  
S.J. Legowo
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Estimation Error ◽  
Time Estimation ◽  
Transportation Policy ◽  
Time Slice ◽  
Accurate Estimation ◽  
Travel Time Estimation ◽  
Traffic Condition ◽  
On Line

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

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.

Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

2020 ◽  
Vol 4 (1) ◽  
pp. 1-23
Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Time Travel ◽  
Travel Time Estimation
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