Abstract
Punctuality is a key performance indicator of train freight transport. However, train delay arises often in the practice. To improve the efficiency of cargo train, prediction of train delay is always an important research area. In this paper, a prediction model is established on the base of artificial neural network (ANN). Due the endogen drawback of ANN, Genetic Algorithm is adopted to improve the performance of ANN. Consequently, an experiment is design to train and test the ANN-based model by a set of data from the practice. The results of the experiment demonstrate the significant propagation ability of the model.
Highlights
Current economic development is highly dependent on natural resources, such as coal and oil
A neural network is established in MATLAB®, and 20 epochs are considered for each run
The case comparisons focus on the four key performance indexes (KPI): Mean error on test set; standard deviation (SD) of the errors on test sets; mean error on training sets and SD of the error on training sets
Summary
Current economic development is highly dependent on natural resources, such as coal and oil. Punctual delivery of products is a key indicator of service performance in the production of natural resources. In terms of logistics, transported products play an important role in the production of natural resources. Railway transport declined constantly over the past decades [1]. This decline is attributed to diverse reasons, such as unstable weather conditions, complex infrastructure, and bureaucratic inefficiency [2]. Given the endogenous characteristics of railway transport, its scheduling suffers from the vulnerability of the transport process; the delivery time of the cargo train is always out of control [3]. To improve the efficiency of rail traffic, delay prediction, which is a necessary function of railway operation, should be explored
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
