scholarly journals Improving Bus Travel Time Estimation and OD-based Calculation for Travel Time Saving Benefit

2021 ◽  
Vol 39 (3) ◽  
pp. 312-328
Author(s):  
Jaeyeob SHIM ◽  
Ikki KIM ◽  
Hansol YOO ◽  
Keunsoo HAN
2017 ◽  
Vol 22 (5) ◽  
pp. 390-406 ◽  
Author(s):  
B. Anil Kumar ◽  
Lelitha Vanajakshi ◽  
Shankar C. Subramanian

2009 ◽  
Vol 17 (2) ◽  
pp. 85-90
Author(s):  
Hermann Knoflacher

Human behavior regarding transport depends on travel time estimation and not on measured travel times. Travel time is dependent on human body energy consumption for diff erent activities, which has been studied by the author over the last 30 years. Uncertainty can enhance time perception up to a factor of 5 compared to measured time, dependent on the certainty or uncertainty of the information system. Quantitative analysis has shown that information improvements at interchanges of transport systems can have a value equal to or even greater than time saving produced by expensive, huge infrastructure projects. The paper will present empirical results from studies over the last 40 years and the theoretical background.


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.


Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu

Sign in / Sign up

Export Citation Format

Share Document