The Bus Line Supporting System Based On Learning Neural Network Model Applied In GIS

DOI

10.2991/iccsae-15.2016.3How to use a DOI?

Keywords

Bus routes selection artificial neural network; Traffic congestion distribution; GIS

Abstract

With the rapid increase of urban population and rapidly widening scope of the city, the newly built residential areas emerging or leading to the existing bus lines could not meet the needs of the people’s activities. The phenomenon of the traffic congestion is becoming more and more serious. [1] According to the problem we based on the principle of convenient resident travel, we used the point - line - face integration optimization model to slow down the urban traffic congestion as the goal, combined with the population distribution model and the congestion nodes judging model. We make the city Chengdu as the research object and after our research the Chengdu bus route selection model is established. [2]At the same time, on the basis of this model combining with the ArcGIS technology, database technology achieved in the host framework programming language C# helped with the tool MATLAB combined with C++ language dynamic link library files and the algorithm model developed by the bus route selection model, we achieved our information system according the message or rules we mentioned in this passage. We compared our city bus route selection in the last part which we designed to the original route scheme optimization selection and further demonstrate the superiority of the new scheme. [3]

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/).

Download article (PDF)

TY  - CONF
AU  - Jun Song
AU  - Shengwei Quan
PY  - 2016/02
DA  - 2016/02
TI  - The Bus Line Supporting System Based On Learning Neural Network Model Applied In GIS
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 11
EP  - 14
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.3
DO  - 10.2991/iccsae-15.2016.3
ID  - Song2016/02
ER  -