Extracting Logical Connectivity of Intersection from Floating Car Data Using Support Vector Machine Method

DOI

10.2991/ncce-18.2018.112How to use a DOI?

Keywords

traffic rule extraction; connectivity; floating car data; intersection; support vector machine

Abstract

This paper aims to extract traffic rule at intersection from floating car data, traffic rule is the logical connectivity data in road network. Firstly, based on intersection data model, the geometric characteristics of intersection are presented, and the angle of arcs is calculated to get the traffic diversion. Secondly, routes are extracted from floating car data, which represent the number of turning car at intersection. Thirdly, a classification model based on support vector machine is used to classify the connected state of arcs, and the classification feature includes the number of car and traffic diversion. Finally, this method is applied in some intersections of Guangzhou Province using six days’ floating car data. The experiment results reveal the accuracy is about 90 percent.

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/).

Download article (PDF)

TY  - CONF
AU  - Min Huang
AU  - Teng Zhang
AU  - Jiajie Pan
AU  - Fang Liu
PY  - 2018/05
DA  - 2018/05
TI  - Extracting Logical Connectivity of Intersection from Floating Car Data Using Support Vector Machine Method
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 681
EP  - 687
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.112
DO  - 10.2991/ncce-18.2018.112
ID  - Huang2018/05
ER  -