Integrated Intersection Evaluation Method Based on BP Neural Network

DOI

10.2991/tlicsc-18.2018.33How to use a DOI?

Keywords

Transportation network, Social Network Analysis busyness, Systematic scientific analysis, Traffic characteristics, Neural network.

Abstract

With the development of the Wangjing regional economy, the number of cars in the region has increased. In order to allow inter-regional vehicles to pass with high efficiency, it is necessary to perform some efficient control at the boundary of the area. First, controlling the intersection of the entire transportation network is not effective and economical. Therefore, it is necessary to evaluate the boundary intersections so as to select the more important intersections on the regional boundaries for control.In this paper, the road network partition was first carried out. This paper mainly uses the basic properties of the traffic network to partition the road network.The boundary intersections are evaluated based on the repartitioning to select more important intersections for control at the regional boundaries.In this paper, we mainly use the three indicators of social network analysis method, system science analysis method, and traffic network characteristics method to evaluate the intersections, and use neural network training to get the weight of each indicator, so as to determine a comprehensive evaluation method. Feedback gate control based on fuzzy PID is applied to the traffic network based on the selected important intersection, thereby alleviating the congestion in the effective central city.

Copyright

© 2018, 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  - Yan Zhang
AU  - Yanxin Zhang
PY  - 2018/12
DA  - 2018/12
TI  - Integrated Intersection Evaluation Method Based on BP Neural Network
BT  - Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)
PB  - Atlantis Press
SP  - 204
EP  - 213
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.33
DO  - 10.2991/tlicsc-18.2018.33
ID  - Zhang2018/12
ER  -