Traffic State Estimation Using Small Imaging Satellites and Connected Vehicles

被引：3

作者：

Seo, Toru ^{[1
,2
]}

Kusakabe, Takahiko ^{[3
]}

机构：

[1] Univ Michigan, 1233 GG Brown,2350 Hayward, Ann Arbor, MI 48109 USA

[2] Tokyo Inst Technol, Meguro Ku, 2-12-1-M1-20 O Okayama, Tokyo 1528552, Japan

[3] Univ Tokyo, Ctr Spatil Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan

来源：

INTERNATIONAL SYMPOSIUM OF TRANSPORT SIMULATION (ISTS'18) AND THE INTERNATIONAL WORKSHOP ON TRAFFIC DATA COLLECTION AND ITS STANDARDIZATION (IWTDCS'18) - EMERGING TRANSPORT TECHNOLOGIES FOR NEXT GENERATION MOBILITY | 2018年 / 34卷

关键词：

remote sensing; mobile sensing; traffic flow; small satellite; connected vehicle; data assimilation; HIGHWAY;

D O I：

10.1016/j.trpro.2018.11.007

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

Small imaging satellites (SISs), which has attracted attention in remote sensing field recently, would provide a novel type of data for traffic state estimation (TSE): that is, spatial distribution of every cars in anywhere on our planet with relatively short time interval. This nature is particularly useful to complement connected vehicle (CV) data, which is sampled but time-continuous data. This paper proposes an ensemble Kalman filter-based TSE method using SIS and CV data. The proposed method endogenously estimates the fundamental diagram of traffic flow and penetration rate of CVs based only on SIS and CV data, making the method completely free from roadside detectors. The accuracy of the proposed method is verified by numerical simulation. (C) 2018 The Authors. Published by Elsevier Ltd.

引用

页码：4 / 11

页数：8

共 20 条

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