Dalarna University's logo and link to the university's website

du.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 12) Show all publications
Paidi, V., Håkansson, J., Fleyeh, H. & Nyberg, R. G. (2022). CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot. Sustainability, 14(7), Article ID 3742.
Open this publication in new window or tab >>CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot
2022 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 14, no 7, article id 3742Article in journal (Refereed) 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.

Keywords
cruising, pollution, parking
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:du-40866 (URN)10.3390/su14073742 (DOI)000782091300001 ()2-s2.0-85127594018 (Scopus ID)
Available from: 2022-03-22 Created: 2022-03-22 Last updated: 2023-04-14
Paidi, V. (2022). Parking support model for open parking lots. (Doctoral dissertation). Borlänge: Högskolan Dalarna
Open this publication in new window or tab >>Parking support model for open parking lots
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2022
Series
Dalarna Doctoral Dissertations ; 21
Keywords
parking, deep learning, pollution, cruising, detection, tracking, forecasting
National Category
Transport Systems and Logistics Computer and Information Sciences
Identifiers
urn:nbn:se:du-41094 (URN)978-91-88679-36-9 (ISBN)
Public defence
2022-06-03, room 311, 10:30 (English)
Opponent
Supervisors
Available from: 2022-05-03 Created: 2022-03-24 Last updated: 2023-08-17Bibliographically approved
Paidi, V. (2022). Short-term prediction of parking availability in an open parking lot. Journal of Intelligent Systems, 31(1), 541-554
Open this publication in new window or tab >>Short-term prediction of parking availability in an open parking lot
2022 (English)In: Journal of Intelligent Systems, ISSN 0334-1860, E-ISSN 2191-026X, Vol. 31, no 1, p. 541-554Article in journal (Refereed) Published
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 were 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. The LSTM 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 a 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. © 2022 Vijay Paidi, published by De Gruyter.

Keywords
exogenous variables; LSTM; parking availability; prediction
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:du-41615 (URN)10.1515/jisys-2022-0039 (DOI)000788846500003 ()2-s2.0-85129725522 (Scopus ID)
Available from: 2022-06-15 Created: 2022-06-15 Last updated: 2023-03-17
Paidi, V., Fleyeh, H., Håkansson, J. & Nyberg, R. G. (2021). Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter. Journal of Advanced Transportation, Article ID 1812647.
Open this publication in new window or tab >>Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter
2021 (English)In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, article id 1812647Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2021
National Category
Information Systems
Identifiers
urn:nbn:se:du-37969 (URN)10.1155/2021/1812647 (DOI)000692912400003 ()2-s2.0-85114610711 (Scopus ID)
Available from: 2021-08-25 Created: 2021-08-25 Last updated: 2023-04-14Bibliographically approved
Paidi, V., Fleyeh, H. & Nyberg, R. G. (2020). Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera. IET Intelligent Transport Systems, 14(10), 1295-1302
Open this publication in new window or tab >>Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera
2020 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 14, no 10, p. 1295-1302Article in journal (Refereed) 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.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30605 (URN)10.1049/iet-its.2019.0468 (DOI)000573659000015 ()2-s2.0-85091396572 (Scopus ID)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2022-05-03Bibliographically approved
Paidi, V., Nyberg, R. G. & Håkansson, J. (2020). Dynamic Scheduling and Communication System to Manage Last Mile Handovers. Logistics, 4(2), Article ID 0013.
Open this publication in new window or tab >>Dynamic Scheduling and Communication System to Manage Last Mile Handovers
2020 (English)In: Logistics, ISSN 2305-6290, Vol. 4, no 2, article id 0013Article in journal (Refereed) Published
Abstract [en]

Last mile handover is the most problematic phase in the delivery process, while real-time communication and dynamic scheduling are major problem areas associated with attended last mile handovers. These problem areas need to be addressed holistically to facilitate efficient last mile handovers. The aim of this paper is to report the design and functionalities of a decision-support system which holistically addresses these problem areas. The functionalities of decision-support system which addresses dynamic scheduling and real-time communication problem areas are discussed using case studies. We conclude that a holistic decision-support system with multiparty communication among the stakeholders facilitates improving customer satisfaction, business opportunities and reducing operational costs for logistics companies.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
Sailor; real-time communication; dynamic scheduling; consumer satisfaction
National Category
Transport Systems and Logistics
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-34266 (URN)10.3390/logistics4020013 (DOI)000672635600007 ()
Projects
Easylife
Available from: 2020-06-20 Created: 2020-06-20 Last updated: 2022-03-25Bibliographically approved
Paidi, V., Håkansson, J. & Nyberg, R. G. (2019). A holistic decision support system for last mile handovers.
Open this publication in new window or tab >>A holistic decision support system for last mile handovers
2019 (English)In: Article in journal (Other academic) Submitted
Abstract [en]

The last mile handover is assumed to be the most problematic part in the delivery process and the costs can go upto 50% of the total logistic cost. Real time consumer communication and dynamic scheduling are the major problem areas associated with effective attended last mile handovers. Therefore, aim of this paper is to report the design and development of a holistic decision support system’s functionalities which simultaneously addresses real time consumer communication and dynamic scheduling. A decision support system was designed and developed based on workshops, expert group interviews and its functionalities were proposed with the use cases. A survey was conducted with consumers of a retailer where majority of the consumers accepted the use of mobile communication devices to enable real time communication and alternate handover location which improves customer satisfaction and facilitates to avoid missed handovers. A pilot test was performed where routing distance was reduced with the use of optimized handover routes. However the improvement is subjected to the experience of driver and real time traffic conditions. We conclude that a holistic decision support system with multi-party communication among the stakeholders facilitates in reducing operational costs for logistic companies and improving customer satisfaction and business opportunities.

National Category
Transport Systems and Logistics
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30606 (URN)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2021-11-12Bibliographically approved
Paidi, V. (2019). Developing decision support systems for last mile transportation problems. (Licentiate dissertation). Borlänge: Dalarna University
Open this publication in new window or tab >>Developing decision support systems for last mile transportation problems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Last mile transportation is the most problematic phase of transportation needing additional research and effort. Longer waits or search times, lack of navigational directions and real-time information are some of the common problems associated with last mile transportation. Inefficient last mile transportation has an impact on the environment, fuel consumption, user satisfaction and business opportunities. Last mile problems exist in several transportation domains, such as: the landing of airplanes, docking of ships, parking of vehicles, attended home deliveries, etc. While there are dedicated inter-connected decision support systems available for ships and aircraft, similar systems are not widely utilized in parking or attended handover domains. Therefore, the scope of this thesis covers last mile transportation problems in parking and attended handover domains. One problem area for parking and attended handovers is due to lack of real-time information to the driver or consumer. The second problem area is dynamic scheduling where the handover vehicle must traverse additional distance to multiple handover locations due to lack of optimized routes. Similarly, during parking, lack of navigational directions to an empty parking space can lead to increased fuel consumption and CO2 emissions. Therefore, aim of this thesis is to design and develop decision support systems for last mile transportation problems by holistically addressing real time customer communication and dynamic scheduling problem areas. The problem areas discussed in this thesis consists of persistent issues even though they were widely discussed in the literature. In order to investigate the problem areas, microdata analysis approach was implemented in the thesis. The phases involved in Microdata analysis are: data collection, data processing, data storage, data analysis and decision-making. Other similar research domains, such as: computer science or statistics also involve phases such as data collection, processing, storage and analysis. These research domains also work in the fields of decision support systems or knowledge creation. However, knowledge creation or decision support systems is not a mandatory phase in these research domains, unlike Microdata analysis. Three papers are presented in this thesis, with two papers focusing on parking domains, while the third paper focuses on attended handover domains.

The first paper identifies available smart parking tools, applications and discusses their uses and drawbacks in relation to open parking lots. The usage of cameras in identifying parking occupancy was recognized as one of the suitable tools in this paper. The second paper uses a thermal camera to collect the parking lot data, while deep learning methodologies were used to identify parking occupancy detection. Multiple deep learning networks were evaluated for identifying parking spaces and one method was considered suitable for acquiring real time parking occupancy. The acquired parking occupancy information can be communicated to the user to address real-time customer communication problems. However, the decision support system (DSS) to communicate parking occupancy information still needs to be developed. The third paper focuses on the attended handovers domain where a decision support system was reported which addresses real-time customer communication and dynamic scheduling problems holistically. Based on a survey, customers accepted the use of mobile devices for enabling a real-time information flow for improving customer satisfaction. A pilot test on vehicle routing was performed where the decision support system reduced the vehicle routing distance compared to the route taken by the driver. The three papers work in developing decision support systems for addressing major last mile transportation problems in parking and attended handover domains, thus improving customer satisfaction, and business opportunities, and reducing fuel costs, and pollution.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2019
Series
Dalarna Licentiate Theses ; 11
Keywords
parking spaces, attended handovers, user satisfaction, pollution, business opportunities
National Category
Transport Systems and Logistics Computer and Information Sciences
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30609 (URN)978-91-88679-02-4 (ISBN)
Presentation
2019-09-06, Clas Ohlsson, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2019-08-20 Created: 2019-08-14 Last updated: 2023-08-17
Paidi, V. & Fleyeh, H. (2019). Parking Occupancy Detection Using Thermal Camera. In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS: . Paper presented at 5th International Conference on Vehicle Technology and Intelligent Transport Systems, May 3-5 2019, Heraklion, Greece (pp. 483-490).
Open this publication in new window or tab >>Parking Occupancy Detection Using Thermal Camera
2019 (English)In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, 2019, p. 483-490Conference paper, Published paper (Refereed)
Abstract [en]

Parking a vehicle is a daunting task during peak hours. The search for a parking space leads to congestion and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. This paper aims to identify parking occupancy in an open parking lot which consists of free parking spaces using a thermal camera. A thermal camera is capable of detecting vehicles in any weather and light conditions based on emitted heat and it can also be installed in public places with less restrictions. However, a thermal camera is expensive compared to a colour camera. A thermal camera can detect vehicles based on the emitted heat without any illumination. Vehicles appear bright or dark based on heat emitted by the vehicles. In order to identify vehicles, pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented in this paper. The detection rates of the detectors reduced with diminishing of heat in the vehicles. Modified Faster RCNN deep learning network produced better detection results compared to other detectors. However, the detection rates can further be improved with larger and diverse training dataset.

Keywords
Convolutional Neural Network, Detectors, Thermal Camera
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30153 (URN)10.5220/0007726804830490 (DOI)000570379100053 ()2-s2.0-85067576912 (Scopus ID)978-989-758-374-2 (ISBN)
Conference
5th International Conference on Vehicle Technology and Intelligent Transport Systems, May 3-5 2019, Heraklion, Greece
Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2021-11-12Bibliographically approved
Paidi, V., Fleyeh, H., Håkansson, J. & Nyberg, R. G. (2018). Smart parking sensors, technologies and applications for open parking lots: a review. IET Intelligent Transport Systems, 12(8), 735-741
Open this publication in new window or tab >>Smart parking sensors, technologies and applications for open parking lots: a review
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 8, p. 735-741Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018
National Category
Computer Systems
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27619 (URN)10.1049/iet-its.2017.0406 (DOI)000444389300001 ()2-s2.0-85053198237 (Scopus ID)
Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2022-05-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2078-3327

Search in DiVA

Show all publications