Research of Railway Wagon Flow Forecast System Based on Hadoop-Hazelcast

DOI

10.2991/iccte-16.2016.93How to use a DOI?

Keywords

Railway station; Wagon Flow Forecast; Hadoop; Hazelcast; Distributed computing

Abstract

The existing railway wagon flow forecast method cannot meet the demand of the actual railway transport organization. The old system is embodied in the slow computing speed and low accuracy. The railway wagon flow forecast system based on Hadoop-Hazelcast is designed to improve the accuracy of the forecast, shorten the calculation time and expand the scale of calculation. Using the Hadoop framework to analyze history wagon flow data to get the wagon flow data feature, using Hazelcast architecture to solve IO bottlenecks, the new forecast system integrates the scattered memory into memory computing cluster to compute time window in parallel. The results show that this method and system can greatly improve the forecast accuracy and speed.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaodong Zhang
AU  - Baotian Dong
AU  - Weijia Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Research of Railway Wagon Flow Forecast System Based on Hadoop-Hazelcast
BT  - Proceedings of the 2016 International Conference on Civil, Transportation and Environment
PB  - Atlantis Press
SP  - 564
EP  - 569
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccte-16.2016.93
DO  - 10.2991/iccte-16.2016.93
ID  - Zhang2016/01
ER  -