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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
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30606 (URN)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-14Bibliographically approved
Paidi, V., Fleyeh, H. & Nyberg, R. G. (2019). Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera.
Open this publication in new window or tab >>Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera
2019 (English)In: Article in journal (Other academic) Submitted
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
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30605 (URN)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-14Bibliographically 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 in Microdata Analysis ; 10
Keywords
parking spaces, attended handovers, user satisfaction, pollution, business opportunities
National Category
Transport Systems and Logistics
Research subject
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: 2019-08-20
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
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30153 (URN)10.5220/0007726804830490 (DOI)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: 2019-07-01Bibliographically 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
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: 2019-10-17Bibliographically approved
Paidi, V., Fleyeh, H., Håkansson, J. & Nyberg, R. G. (2018). Smart Parking Tools Suitability for Open Parking Lots: A Review. In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems: . Paper presented at 4th International Conference on Vehicle Technology and Intelligent Transport Systems, March 16-18 2018, Funchal, Madeira (pp. 600-609). Madeira
Open this publication in new window or tab >>Smart Parking Tools Suitability for Open Parking Lots: A Review
2018 (English)In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, p. 600-609Conference paper, Published paper (Refereed)
Abstract [en]

Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

Place, publisher, year, edition, pages
Madeira: , 2018
Keywords
Decision support system, sensors, technologies, applications
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27616 (URN)10.5220/0006812006000609 (DOI)2-s2.0-85051933085 (Scopus ID)978-989-758-293-6 (ISBN)
Conference
4th International Conference on Vehicle Technology and Intelligent Transport Systems, March 16-18 2018, Funchal, Madeira
Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2019-10-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2078-3327

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