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Assimilation of parking space information derived from remote sensing data into a transport demand model

Hellekes, Jens and Merkle, Nina Marie and López Díaz, María and Henry, Corentin and Heinrichs, Matthias and Azimi, Seyedmajid and Kurz, Franz (2021) Assimilation of parking space information derived from remote sensing data into a transport demand model. In: ITS World Congress 2021: Book of Abstracts, pp. 2579-2590. ITS World Congress 2021, 11.-15. Okt. 2021, Hamburg, Deutschland.

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Abstract

Accurate data on parking spaces and their utilization is important for optimizing traffic management today and will become even more essential in light of upcoming ITS technologies and autonomous driving. For many cities, however, no comprehensive, standardized and up-to-date database exists. In this paper, we present a novel processing chain combining state-of-the-art remote sensing methods with geospatial analysis. Deep neural networks are used for vehicle detection and traffic area segmentation to identify all types of parking areas and their occupancy on aerial image sequences of the city of Brunswick in Germany. A discretization method is formulated to estimate parking capacity and a regression analysis is performed to draw conclusion for areas not covered by aerial imagery. By comparing the number of stopped vehicles with simulation results from a transport demand model, light can be shed on parking related traffic.

Item URL in elib:https://elib.dlr.de/147964/
Document Type:Conference or Workshop Item (Speech)
Title:Assimilation of parking space information derived from remote sensing data into a transport demand model
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hellekes, JensJens.Hellekes (at) dlr.dehttps://orcid.org/0000-0002-0080-3124
Merkle, Nina MarieNina.Merkle (at) dlr.dehttps://orcid.org/0000-0003-4177-1066
López Díaz, MaríaMaria.LopezDiaz (at) dlr.deUNSPECIFIED
Henry, Corentincorentin.henry (at) dlr.deUNSPECIFIED
Heinrichs, MatthiasMatthias.Heinrichs (at) dlr.dehttps://orcid.org/0000-0002-0175-2787
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.deUNSPECIFIED
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004
Date:October 2021
Journal or Publication Title:ITS World Congress 2021: Book of Abstracts
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 2579-2590
Status:Published
Keywords:Deep Learning, Aerial Imagery, Image Segmentation, Vehicle Detection, Parking Space Management, OpenStreetMap, Geospatial Analysis, Travel Demand Model
Event Title:ITS World Congress 2021
Event Location:Hamburg, Deutschland
Event Type:international Conference
Event Dates:11.-15. Okt. 2021
Organizer:ERTICO ITS Europe
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - UrMo Digital
Location: Berlin-Adlershof , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Institute of Transport Research > Mobility and Urban Development
Deposited By: Hellekes, Jens
Deposited On:11 Jan 2022 09:52
Last Modified:11 Jan 2022 09:52

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