Högskolan Dalarnas logga och länk till högskolans webbplats

du.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Parking support model for open parking lots
Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.ORCID-id: 0000-0002-2078-3327
2022 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Parking is a common process performed by vehicle drivers when they arrive at their destination. It is considered to be the last mile transportation problem of personal vehicles. Some of the common problems observed by drivers are additional cruising, congestion, pollution, and driver frustration. This thesis is focused on open parking lots that provide free parking spaces. Since parking spaces are provided free, open parking lots are in high demand leading to additional cruising and pollution. One of the primary reasons for these problems is the lack of information on parking availability. Such information can be provided using a parking support model, or a smart parking system. As open parking lots do not provide any direct return on investments, no parking support models were available on the market. Therefore, this thesis aims to develop a parking support model suitable for open parking lots which can facilitate in providing real-time and short-term forecast of parking availability. This thesis also examines the magnitude of additional cruising and CO2 emissions observed in an open parking lot. A thermal camera was utilized for collecting data on open parking lots as it is not susceptible to varying illumination and environmental conditions. Since there were no pre-trained algorithms for enabling object detection using thermal camera images, a dataset was created with varying environmental and illumination conditions. This dataset was utilized by deep learning algorithms to facilitate multi-object, real-time detection. The developed parking support model facilitates in providing a real-time and short-term forecast of parking availability. Despite the use of low volume of data, the methods utilized in this thesis facilitated providing better detection and forecasting results. Algorithms, such as ResNet18 and Yolo, facilitated in providing real-time, multi-object detection with high accuracy. Similarly, a short-term forecast of parking availability was provided for the open parking lot using methods such as the Ensemble-based method, LSTM and SARIMAX. Ensemble-based method and LSTM provided better test prediction results with lower errors compared to SARIMAX. A new CO2 emissions model was proposed to estimate the magnitude of emissions observed at an open parking lot. The mean CO2 emissions of additional cruising is 2.7 times more than optimal cruising. Despite the individual CO2 emissions of vehicles being lower, aggregating CO2 emissions from multiple vehicles leads to higher pollution. This problem can be reduced by utilizing the parking support model.

Ort, förlag, år, upplaga, sidor
Borlänge: Högskolan Dalarna, 2022.
Serie
Dalarna Doctoral Dissertations ; 21
Nyckelord [en]
parking, deep learning, pollution, cruising, detection, tracking, forecasting
Nationell ämneskategori
Transportteknik och logistik Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:du-41094ISBN: 978-91-88679-36-9 (tryckt)OAI: oai:DiVA.org:du-41094DiVA, id: diva2:1647013
Disputation
2022-06-03, room 311, 10:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2022-05-03 Skapad: 2022-03-24 Senast uppdaterad: 2023-08-17Bibliografiskt granskad
Delarbeten
1. Smart parking sensors, technologies and applications for open parking lots: a review
Öppna denna publikation i ny flik eller fönster >>Smart parking sensors, technologies and applications for open parking lots: a review
2018 (Engelska)Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, nr 8, s. 735-741Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

Ort, förlag, år, upplaga, sidor
Institution of Engineering and Technology, 2018
Nationell ämneskategori
Datorsystem
Forskningsämne
Forskningsprofiler 2009-2020, Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-27619 (URN)10.1049/iet-its.2017.0406 (DOI)000444389300001 ()2-s2.0-85053198237 (Scopus ID)
Tillgänglig från: 2018-05-04 Skapad: 2018-05-04 Senast uppdaterad: 2022-05-03Bibliografiskt granskad
2. Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera
Öppna denna publikation i ny flik eller fönster >>Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera
2020 (Engelska)Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 14, nr 10, s. 1295-1302Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

Nationell ämneskategori
Datorseende och robotik (autonoma system)
Forskningsämne
Forskningsprofiler 2009-2020, Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-30605 (URN)10.1049/iet-its.2019.0468 (DOI)000573659000015 ()2-s2.0-85091396572 (Scopus ID)
Tillgänglig från: 2019-08-12 Skapad: 2019-08-12 Senast uppdaterad: 2022-05-03Bibliografiskt granskad
3. Short-term prediction of parking availability in an open parking lot
Öppna denna publikation i ny flik eller fönster >>Short-term prediction of parking availability in an open parking lot
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Abstract [en]

The parking of cars is a globally recognized problem, especially at locations where there is a high demand for empty parking spaces. Drivers tend to cruise additional distances while searching for empty parking spaces during peak hours leading to problems such as pollution, congestion, and driver frustration. Providing short-term predictions of parking availability would facilitate the driver in making informed decisions and planning their arrival to be able to choose parking locations with higher availability. Therefore, the aim of this study is to provide short-term predictions of available parking spaces with a low volume of data. The open parking lot provides parking spaces free of charge and one such parking lot, located beside a shopping center, was selected for this study. Parking availability data for 21 days was collected where 19 days were used for training, while multiple periods of the remaining 2 days were used to test and evaluate the prediction methods. The test dataset consists of data from a weekday and a weekend. Based on the reviewed literature, three prediction methods suitable for short-term prediction were selected, namely, Long-short term memory (LSTM), Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), and the Ensemble-based method. TheLSTM method is a deep learning-based method, while SARIMAX is a regression-based method, and the Ensemble method is based on decision trees and random forest to provide predictions. The performance of the three prediction methods with low volume of data and the use of visitor trends data as an exogenous variable was evaluated. Based on the test prediction results, the LSTM and Ensemble-based methods provided better short-term predictions at multiple times on a weekday, while the Ensemble-based method provided better predictions over the weekend. However, the use of visitor trend data did not facilitate improving the predictions of SARIMAX and the Ensemble-based method, while it improved the LSTM prediction for the weekend.

Nyckelord
parking, prediction, open parking lot, deep learning
Nationell ämneskategori
Transportteknik och logistik Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:du-41135 (URN)
Tillgänglig från: 2022-03-28 Skapad: 2022-03-28 Senast uppdaterad: 2023-03-17Bibliografiskt granskad
4. Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter
Öppna denna publikation i ny flik eller fönster >>Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter
2021 (Engelska)Ingår i: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, artikel-id 1812647Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.

Ort, förlag, år, upplaga, sidor
Hindawi Publishing Corporation, 2021
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
urn:nbn:se:du-37969 (URN)10.1155/2021/1812647 (DOI)000692912400003 ()2-s2.0-85114610711 (Scopus ID)
Tillgänglig från: 2021-08-25 Skapad: 2021-08-25 Senast uppdaterad: 2023-04-14Bibliografiskt granskad
5. CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot
Öppna denna publikation i ny flik eller fönster >>CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot
2022 (Engelska)Ingår i: Sustainability, E-ISSN 2071-1050, Vol. 14, nr 7, artikel-id 3742Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Parking lots are places of high air pollution as numerous vehicles cruise to find vacant parking places. Open parking lots receive high vehicle traffic, and when limited empty spaces are available it leads to problems, such as congestion, pollution, and driver frustration. Due to lack of return on investment, open parking lots are little studied, and there is a research gap in understanding the magnitude of CO2 emissions and cruising observed at open parking lots. Thus, this paper aims to estimate CO2 emissions and cruising distances observed at an open parking lot. A thermal camera was utilized to collect videos during peak and non-peak hours. The resulting videos were utilized to collect cruising trajectories of drivers searching for empty parking spaces. These trajectories were analyzed to identify optimal and non-optimal cruising, time to park, and walking distances of drivers. A new CO2 model was proposed to estimate emissions in smaller geographical regions, such as open parking lots. The majority of drivers tend to choose parking spaces near a shopping center, and they prefer to cruise non-optimal distances to find an empty parking space near the shopping center. The observed mean non-optimal cruising distance is 2.7 times higher than the mean optimal cruising distance. Excess CO2 emissions and non-optimal cruising were mainly observed during visitor peak hours when there were limited or no empty parking spaces. During visitor peak hours, several vehicles could not find an empty parking space in the region of interest, which also leads to excess cruising.

Nyckelord
cruising, pollution, parking
Nationell ämneskategori
Transportteknik och logistik
Identifikatorer
urn:nbn:se:du-40866 (URN)10.3390/su14073742 (DOI)000782091300001 ()2-s2.0-85127594018 (Scopus ID)
Tillgänglig från: 2022-03-22 Skapad: 2022-03-22 Senast uppdaterad: 2023-04-14

Open Access i DiVA

fulltext(1107 kB)691 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 1107 kBChecksumma SHA-512
d311cb6ff49e41c8f85880bc0a92d277a269dba7042f27ea48571f9d811a6a5661aac8ed54f38c06966edcf2c0bea6d8c629037a38b2e9cb899541ef70723d26
Typ fulltextMimetyp application/pdf

Person

Paidi, Vijay

Sök vidare i DiVA

Av författaren/redaktören
Paidi, Vijay
Av organisationen
Mikrodataanalys
Transportteknik och logistikData- och informationsvetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 693 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

isbn
urn-nbn

Altmetricpoäng

isbn
urn-nbn
Totalt: 1352 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf