Traffic State Detection Based on Equipped Vehicle Data

被引：0

作者：

Shi, Yun-feng ^{[1
]}

Yang, Li-cai ^{[1
]}

Hao, Shen-xue ^{[1
]}

机构：

[1] Shandong Univ, Dept Control Sci & Engn, Jinan, Shandong, Peoples R China

来源：

2016 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATION (ICEEA 2016) | 2016年

关键词：

Traffic state; Smart-phone; KHDD; Smart vehicle; Traffic congestion;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To detect traffic state using Smart-phone equipped vehicles (for short, equipped vehicles) data is popular in recent years. Calculating the average speed of all equipped vehicles simply is no longer reasonable without considering random distribution of equipped vehicles. In this paper, a new method of detecting traffic state based on equipped vehicle data was presented. Inspired by information entropy theory, the key homogeneous distribution degree (KHDD) can be obtained, which contributes to correct the detection error caused by the random distribution of equipped vehicles. In simulations, the results show the validity of the proposed method to detect traffic state under the different KHDD.

引用

页数：6

共 50 条

[31] Traffic state detection with floating car data in road networks
Kerner, BS
Demir, C
Herrtwich, RG
Klenov, SL
Rehborn, H
Aleksic, M
Haug, A
2005 IEEE Intelligent Transportation Systems Conference (ITSC), 2005, : 700 - 705
[32] Detection of Potential Traffic Jam Based on Traffic Characteristic Data Analysis
Amelia, Anasthasia
Saptawati, G. A. Putri
2014 INTERNATIONAL CONFERENCE ON DATA AND SOFTWARE ENGINEERING (ICODSE), 2014,
[33] Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data
Kim, Kwang-Ju
Kim, Pyong-Kun
Chung, Yun-Su
Choi, Doo-Hyun
IEEE ACCESS, 2019, 7 : 78311 - 78319
[34] Anomaly Detection in Vehicle Traffic Data Using Batch and Stream Supervised Learning
Faial, David
Bernardini, Flavia
Miranda, Leandro
Viterbo, Jose
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2019, PT I, 2019, 11804 : 675 - 684
[35] An RGBD Data Based Vehicle Detection Algorithm for Vehicle Following Systems
Zhao, Changchen
Chen, Weihai
Zhao, Zhiwen
Liu, Jingmeng
PROCEEDINGS OF THE 2013 IEEE 8TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2013, : 1506 - 1511
[36] Vehicle Detection for Traffic Flow Analysis
Ramalingam, S.
Varsani, V.
2016 IEEE INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST), 2016, : 130 - 137
[37] Vehicle detection and tracking for traffic monitoring
Foresti, GL
Snidaro, L
IMAGE ANALYSIS AND PROCESSING - ICIAP 2005, PROCEEDINGS, 2005, 3617 : 1198 - 1205
[38] Traffic Scenes Invariant Vehicle Detection
Liu, Yan
Lu, Xiaoqing
Xu, Jianbo
2013 9TH ASIAN CONTROL CONFERENCE (ASCC), 2013,
[39] Traffic state classification and prediction based on trajectory data
Yuan, Yu
Zhang, Wenbo
Yang, Xun
Liu, Yang
Liu, Zhiyuan
Wang, Wei
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2021,
[40] Traffic state estimation based on data fusion techniques
Cipriani, E.
Gori, S.
Mannini, L.
2012 15TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2012, : 1477 - 1482

← 1 2 3 4 5 →