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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
Laryea, R., Farsari, I. & Nyberg, R. G. (2018). A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model.
Open this publication in new window or tab >>A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model
2018 (English)In: Article in journal (Refereed) Submitted
Abstract [en]

A Decision Analytical tool capable of handling numerically imprecise data for decision making is used in this paper to analyze the risk of the effect of data alteration in the ranking positions of country alternatives for food price volatility. Unguided decision making processes would lead to non-optimal decisions with it’s dire consequences on the resources of organizations. The paper is thus guided by the use of an accurate risk classification model to implement uncertainty and imprecision which are essential part of real life decision making processes with computer based tools to overcome the problem of possibilities uncertain and imprecise input data of criteria and alternatives. A ranking of the alternatives is conducted after imprecision is considered in the input data and a further analysis is carried out to determine which criteria is sensitive enough to alter the position of a country in the rankings.

National Category
Other Computer and Information Science
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28648 (URN)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
Laryea, R., Carling, K., Cialani, C. & Nyberg, R. G. (2018). Sensitivity analysis of a risk classification model for food price volatility. International Journal of Risk Assessment and Management, 21(4), 374-382
Open this publication in new window or tab >>Sensitivity analysis of a risk classification model for food price volatility
2018 (English)In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241, Vol. 21, no 4, p. 374-382Article in journal (Refereed) Published
Abstract [en]

A sensitivity analysis to vary the weights of an accurate predictive classification model to produce a mixed model for ranking countries on the risk of food price volatility is carried out in this paper. The classification model is a marginal utility function consisting of multiple criteria. The aim of the sensitivity analysis is to derive a mixed model to be used in ranking of country alternatives to aid in policy formulation. Since in real-life situations the data that goes into decision making could be subjected to possibilities of alterations over time, it is essential to aid decision makers to vary the weights of the criteria using both subjective and objective information to introduce imprecision and to generate relative values of the criteria with a scale to form a mixed model. The mixed model can be used to rank future relative alternative value data sets for policy formulation.

Keywords
risk; sensitivity analysis; multiple criteria; weights; decision maker; classification model; imprecision; uncertainty; data; price volatility
National Category
Economics and Business Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28647 (URN)2-s2.0-85055889650 (Scopus ID)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-11-12Bibliographically 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
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
Nyberg, R. G. (2016). A machine learning approach for recognising woody plants on railway trackbeds. In: International Conference on Railway Engineering (ICRE 2016): . Paper presented at IET, International Conference on Railway Engineering (ICRE 2016), Brussels, Belgium, 12-13 May 2016.
Open this publication in new window or tab >>A machine learning approach for recognising woody plants on railway trackbeds
2016 (English)In: International Conference on Railway Engineering (ICRE 2016), 2016Conference paper, Published paper (Refereed)
Abstract [en]

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Keywords
feature extraction; image classification; learning (artificial intelligence); mechanical engineering computing; railways
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23467 (URN)10.1049/cp.2016.0513 (DOI)978-1-78561-292-3 (ISBN)
Conference
IET, International Conference on Railway Engineering (ICRE 2016), Brussels, Belgium, 12-13 May 2016
Available from: 2016-11-24 Created: 2016-11-24 Last updated: 2018-01-13Bibliographically approved
Meszyński, S., Nyberg, R. G. & Yella, S. (2016). Agent-based modelling and simulation of insulin-glucose subsystem. In: Proceedings of the Fifth International Conference on Intelligent Systems and Applications: . Paper presented at The Fifth International Conference on Intelligent Systems and Applications, INTELLI 2016, Barcelona, Spain, November 13-17 2016 (pp. 63-68).
Open this publication in new window or tab >>Agent-based modelling and simulation of insulin-glucose subsystem
2016 (English)In: Proceedings of the Fifth International Conference on Intelligent Systems and Applications, 2016, p. 63-68Conference paper, Published paper (Refereed)
Abstract [en]

Mathematical analytical modeling and computer simulation of the physiological system is a complex problem with great number of variables and equations. The objective of this research is to describe the insulin-glucose subsystem using multi-agent modeling based on intelligence agents. Such an approach makes the modeling process easier and clearer to understand; moreover, new agents can be added or removed more easily to any investigations. The Stolwijk-Hardy mathematical model is used in two ways firstly by simulating the analytical model and secondly by dividing up the same model into several agents in a multiagent system. In the proposed approach a multi-agent system was used to build a model for glycemic homeostasis. Agents were used to represent the selected elements of the human body that play an active part in this process. The experiments conducted show that the multi-agent model has good temporal stability with the implemented behaviors, and good reproducibility and stability of the results. It has also shown that no matter what the order of communication between agents, the value of the result of the simulation was not affected. The results obtained from using the framework of multi-agent system actions were consistent with the results obtained with insulin-glucose models using analytical modeling.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23464 (URN)978-1-61208-518-0 (ISBN)
Conference
The Fifth International Conference on Intelligent Systems and Applications, INTELLI 2016, Barcelona, Spain, November 13-17 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-24Bibliographically approved
Yella, S. & Nyberg, R. G. (2016). Assessing the quality and reliability of visual estimates in determining plant cover on railway embankments. In: Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang (Ed.), Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II. Paper presented at 17th International Conference on Web Information Systems Engineering – WISE 2016 (pp. 404-410). , 10042
Open this publication in new window or tab >>Assessing the quality and reliability of visual estimates in determining plant cover on railway embankments
2016 (English)In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II / [ed] Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang, 2016, Vol. 10042, p. 404-410Conference paper, Published paper (Refereed)
Abstract [en]

This study has investigated the quality and reliability of manual assessments on railway embankments within the domain of railway maintenance. Manually inspecting vegetation on railway embankments is slow and time consuming. Maintenance personnel also require extensive knowledge of the plant species, ecology and bio-diversity to be able to recommend appropriate maintenance action. The overall objective of the study is to investigate the reliable nature of manual inspection routines in favour an automatic approach. Visual estimates of plant cover reported by domain experts’ have been studied on two separate railway sections in Sweden. The first study investigated visual estimates using aerial foliar cover (AFC) and sub-plot frequency (SF) methods to assess the plant cover on a railway section in Oxberg, Alvdalsbanan, Sweden. The second study investigated visual estimates using aerial canopy cover method on a railway section outside Vetlanda, Sweden. Visual estimates of the domain experts were recorded and analysis-of-variance (ANOVA) tests on the mean estimates were investigated to see whether if there were disagreements between the raters’. ICC(2, 1) was used to study the differences between the estimates. Results achieved in this work indicate statistically significant differences in the mean estimates of cover (p < 0.05) reported by the domain experts on both the occasions.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10042
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis; Complex Systems – Microdata Analysis, Automatisk detektering och karakterisering av vegetation längs järnvägen
Identifiers
urn:nbn:se:du-23463 (URN)10.1007/978-3-319-48743-4_33 (DOI)000389505500033 ()978-3-319-48742-7 (ISBN)978-3-319-48743-4 (ISBN)
Conference
17th International Conference on Web Information Systems Engineering – WISE 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2017-03-31Bibliographically approved
Nyberg, R. G. (2016). Automating condition monitoring of vegetation on railway trackbeds and embankments. (Doctoral dissertation). Edinburgh University Press
Open this publication in new window or tab >>Automating condition monitoring of vegetation on railway trackbeds and embankments
2016 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment.

Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body.

A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways.

The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process.

Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms.

The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Place, publisher, year, edition, pages
Edinburgh University Press, 2016. p. 301
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-21465 (URN)
Public defence
2015-10-15, C96 in Napiers' Tower, Merchiston Campus. 10 Colinton Road. Edinburgh. EH10 5DT, Edinburgh, Scotland, UK, 14:00 (English)
Opponent
Supervisors
Funder
Swedish Transport Administration
Available from: 2016-05-20 Created: 2016-05-17 Last updated: 2018-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4812-4988

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