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利用物联网数据进行每日出行方式划分估计和OD矩阵生成的实时系统：以塔尔图市为例

Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu City.

作者信息

Khoshkhah Kaveh, Pourmoradnasseri Mozhgan, Hadachi Amnir, Tera Helen, Mass Jakob, Keshi Erald, Wu Shan

机构信息

ITS Lab, Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.

出版信息

Sensors (Basel). 2022 Apr 15;22(8):3030. doi: 10.3390/s22083030.

DOI:10.3390/s22083030
PMID:35459014
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9030519/
Abstract

In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter for enhanced mobility and a sustainable environment. In this paper, we present a case study of Tartu city, where we developed and designed a daily real-time system for extracting and performing a modal split analysis. Our web-based platform relied on an optimization approach for calibrating our simulation in order to perform the analysis with the use of real data streams from IoT devices installed around the city. The results obtained from our system demonstrated acceptable performance versus the quality of the available data source. In addition, our platform provides downloadable OD matrices for each mode of mobility for the community.

摘要

近年来，我们见证了交通领域重大工作解决方案的实施与整合的出现，尤其是在智慧城市概念范畴内。欧洲及全球许多城市都支持这一让城市更智能以提升出行便利性和实现环境可持续性的倡议。在本文中，我们呈现了塔尔图市的一个案例研究，在那里我们开发并设计了一个用于提取和进行方式划分分析的每日实时系统。我们基于网络的平台依靠一种优化方法来校准我们的模拟，以便利用安装在城市各处的物联网设备的真实数据流来进行分析。从我们的系统获得的结果相对于可用数据源的质量而言表现出可接受性。此外，我们的平台为社区提供了每种出行方式的可下载的起讫点矩阵。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/db211f6a08d0/sensors-22-03030-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/625f40f708b6/sensors-22-03030-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/d07e069c8f3c/sensors-22-03030-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/db211f6a08d0/sensors-22-03030-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/c188c7a7033d/sensors-22-03030-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/73a589194d87/sensors-22-03030-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/bebb2a7d22d6/sensors-22-03030-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/b55f57b462ee/sensors-22-03030-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/b966c3b29b79/sensors-22-03030-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/625f40f708b6/sensors-22-03030-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/d07e069c8f3c/sensors-22-03030-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f8/9030519/8a1893071b2b/sensors-22-03030-g012.jpg
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Hourly Origin-Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning.基于智能交通系统数据和深度学习的逐时 OD 矩阵估计
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