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Publications (10 of 65) Show all publications
Engström, J. & Persson, J. A. (2023). Accurate indoor positioning by combining sensor fusion and obstruction compensation. In: 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN): . Paper presented at IEEE 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 25-28 September 2023, Nuremberg. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Accurate indoor positioning by combining sensor fusion and obstruction compensation
2023 (English)In: 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

Our dependency on Global Navigation Satellite System (GNSS) for getting directions, tracking items, locating friends, or getting maps of the world has increased tremendously over the last decade. However, as soon as we enter a building, the signal strength of the satellites is too low, and we need to resort to other technologies to achieve the same goals. An Indoor Positioning System (IPS) may utilize a wide range of methods for positioning a device, such as fingerprinting, multilateration, or sensor fusion, while using one or several radio technologies to measure Received Signal Strength (RSS) or Time of Arrival(ToA). Sensor fusion is an efficient approach where an Inertial Measurement Unit (IMU) is combined with, e.g., RSS measurements converted to distances. But this approach has significant drawbacks in areas where, e.g., walls or large objects obstruct the signal path, which introduces bias in the distance estimates. This paper addresses the bias caused by signal path obstruction by compensating the measured RSS with localized RSS attenuation adjustments and thereby increasing the accuracy of the sensor fusion model significantly. We also show that a system can learn the compensation parameters over time, reducing the installationefforts and achieving higher accuracy than a fingerprinting-based system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347, E-ISSN 2471-917X
Keywords
IPS, RTLS, Indoor Positioning, Fingerprinting, Multilateration, Sensor Fusion
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-62911 (URN)10.1109/IPIN57070.2023.10332536 (DOI)2-s2.0-85180781818 (Scopus ID)979-8-3503-2011-4 (ISBN)979-8-3503-2012-1 (ISBN)
Conference
IEEE 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 25-28 September 2023, Nuremberg
Available from: 2023-10-03 Created: 2023-10-03 Last updated: 2024-02-05Bibliographically approved
Jevinger, Å., Zhao, C., Persson, J. A. & Davidsson, P. (2023). Artificial intelligence for improving public transport: a mapping study. Public Transport, 1-60
Open this publication in new window or tab >>Artificial intelligence for improving public transport: a mapping study
2023 (English)In: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, p. 1-60Article in journal (Refereed) Epub ahead of print
Abstract [en]

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Artifcial intelligence · Machine learning · Public transit · Mass transit · Public transport · Literature review
National Category
Computer Sciences Transport Systems and Logistics
Research subject
Transportation studies
Identifiers
urn:nbn:se:mau:diva-64419 (URN)10.1007/s12469-023-00334-7 (DOI)001104065400001 ()2-s2.0-85177171423 (Scopus ID)
Projects
AI and public transport: potential and hindrances
Funder
Vinnova, VINNOVA
Note

Ytterligare finansiär: K2 - The Swedish Knowledge Centre for Public Transport

Available from: 2023-12-14 Created: 2023-12-14 Last updated: 2023-12-22Bibliographically approved
Jevinger, Å., Johansson, E., Persson, J. A. & Holmberg, J. (2023). Context-Aware Travel Support During Unplanned Public Transport Disturbances. In: Alexey Vinel, Jeroen Ploeg, Karsten Berns, Oleg Gisikhin (Ed.), Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems: . Paper presented at VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems, April 26-28, 2023, Prague, Czech Republic (pp. 160-170). Setúbal, Portugal: SciTePress, 1, Article ID 19.
Open this publication in new window or tab >>Context-Aware Travel Support During Unplanned Public Transport Disturbances
2023 (English)In: Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems / [ed] Alexey Vinel, Jeroen Ploeg, Karsten Berns, Oleg Gisikhin, Setúbal, Portugal: SciTePress, 2023, Vol. 1, p. 160-170, article id 19Conference paper, Published paper (Refereed)
Abstract [en]

This paper explores the possibilities and challenges of realizing a context-aware travel planner with bidirectional information exchange between the actor and the traveller during unplanned traffic disturbances. A prototype app is implemented and tested to identify potential benefits. The app uses data from open APIs, and beacons to detect the traveller context (which train or train platform the traveller is currently on). Alternative travel paths are presented to the user, and each alternative is associated with a certainty factor reflecting the reliability of the travel time prognoses. The paper also presents an interview study that investigates PT actors’ views on the potential use for actors and travellers of new information about certainty factors and travellers’ contexts, during unplanned traffic disturbances. The results show that this type of travel planner can be realized and that it enables travellers to find ways to reach their destination, in situations where the public t ravel planner only suggests infeasible travel paths. The value for the traveller of the certainty factors are also illustrated. Additionally, the results show that providing actors with information about traveller context and certainty factors opens up for the possibility of more advanced support for both the PT actor and the traveller.

Place, publisher, year, edition, pages
Setúbal, Portugal: SciTePress, 2023
Series
VEHITS, ISSN 2184-495X
Keywords
Public Transport, Travel Planner, Context Aware, Prognoses, kontextmedveten, reseplanerare, resestöd, kollektivtrafiken, störningar
National Category
Transport Systems and Logistics
Research subject
Transportation studies
Identifiers
urn:nbn:se:mau:diva-59392 (URN)10.5220/0011761000003479 (DOI)001090857700016 ()2-s2.0-85160775089 (Scopus ID)978-989-758-652-1 (ISBN)
Conference
VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems, April 26-28, 2023, Prague, Czech Republic
Projects
Kontextmedvetet resestöd vid störningar i kollektivtrafiken
Funder
Swedish Transport Administration, TRV 2021/40633
Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2023-12-05Bibliographically approved
Tegen, A., Davidsson, P. & Persson, J. A. (2023). Human Factors in Interactive Online Machine Learning. In: Paul Lukowicz; Sven Mayer; Janin Koch; John Shawe-Taylor; Ilaria Tiddi (Ed.), HHAI 2023: Augmenting Human Intellect: . Paper presented at HHAI 2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, 26-30 June 2023, Munich, Germany (pp. 33-45). IOS Press
Open this publication in new window or tab >>Human Factors in Interactive Online Machine Learning
2023 (English)In: HHAI 2023: Augmenting Human Intellect / [ed] Paul Lukowicz; Sven Mayer; Janin Koch; John Shawe-Taylor; Ilaria Tiddi, IOS Press, 2023, p. 33-45Conference paper, Published paper (Refereed)
Abstract [en]

Interactive machine learning (ML) adds a human-in-the-loop aspect to a ML system. Even though the input from human users to the system is a central part of the concept, the uncertainty caused by the human feedback is often not considered in interactive ML. The assumption that the human user is expected to always provide correct feedback, typically does not hold in real-world scenarios. This is especially important for when the cognitive workload of the human is high, for instance in online learning from streaming data where there are time constraints for providing the feedback. We present experiments of interactive online ML with human participants, and compare the results to simulated experiments where humans are always correct. We found combining the two interactive learning paradigms, active learning and machine teaching, resulted in better performance compared to machine teaching alone. The results also showed an increased discrepancy between the experiments with human participants and the simulated experiments when the cognitive workload was increased. The findings suggest the importance of taking uncertainty caused by human factors into consideration in interactive ML, especially in situations which requires a high cognitive workload for the human.

Place, publisher, year, edition, pages
IOS Press, 2023
Series
Frontiers in Artificial Intelligence and Application, ISSN 0922-6389, E-ISSN 1879-8314 ; 368
Keywords
interactive machine learning, online learning, human factors
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-61687 (URN)10.3233/faia230073 (DOI)001150361600003 ()2-s2.0-85171485242 (Scopus ID)978-1-64368-394-2 (ISBN)978-1-64368-395-9 (ISBN)
Conference
HHAI 2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, 26-30 June 2023, Munich, Germany
Available from: 2023-07-06 Created: 2023-07-06 Last updated: 2024-02-26Bibliographically approved
Dytckov, S., Davidsson, P. & Persson, J. A. (2023). Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network. In: : . Paper presented at 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023. Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

On-demand transport services are often envisioned as stand-alone modes or as a replacement for conventional public transport modes. This leads to a comparison of service efficiencies, or direct competition for passengers between them. The results of this work point to the positive effects of the inclusion of DRT into the public transport network. We simulate a day of operation of a DRT service in a rural area and demonstrate that a DRT system that focuses on increasing accessibility for travellers with poor public transport access can be quite efficient, especially for reducing environmental impact. We show that DRT, while it produces more vehicle kilometres than private cars would inside the DRT operating zone, can help to reduce the vehicle kilometres travelled for long-distance trips. The results of this study indicate the need for a more systemic evaluation of the impact of the new mobility modes.

Place, publisher, year, edition, pages
Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Simulation, Demand-Responsive Transport, Public transport
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transportation studies
Identifiers
urn:nbn:se:mau:diva-62399 (URN)
Conference
26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Available from: 2023-09-08 Created: 2023-09-08 Last updated: 2023-09-15Bibliographically approved
Lorig, F., Persson, J. A. & Michielsen, A. (2023). Simulating the Impact of Shared Mobility on Demand: a Study of Future Transportation Systems in Gothenburg, Sweden. International Journal of Intelligent Transportation Systems Research, 21(1), 129-144
Open this publication in new window or tab >>Simulating the Impact of Shared Mobility on Demand: a Study of Future Transportation Systems in Gothenburg, Sweden
2023 (English)In: International Journal of Intelligent Transportation Systems Research, ISSN 1348-8503, Vol. 21, no 1, p. 129-144Article in journal (Refereed) Published
Abstract [en]

Self-driving cars enable dynamic shared mobility, where customers are independent of schedules and fixed stops. This study aims to investigate the potential effects shared mobility can have on future transportation. We simulate multiple scenarios to analyze the effects different service designs might have on vehicle kilometers, on the required number of shared vehicles, on the potential replacement of private cars, and on service metrics such as waiting times, travel times, and detour levels. To demonstrate how simulation can be used to analyze future mobility, we present a case study of the city of Gothenburg in Sweden, where we model travel demand in the morning hours of a workday. The results show that a significant decrease of vehicle kilometers can be achieved if all private car trips are replaced by rideshare and that shared vehicles can potentially replace at least 5 private cars during the morning peak.

Place, publisher, year, edition, pages
Springer, 2023
National Category
Computer and Information Sciences Transport Systems and Logistics
Identifiers
urn:nbn:se:mau:diva-57773 (URN)10.1007/s13177-023-00345-5 (DOI)000919810900001 ()2-s2.0-85146806659 (Scopus ID)
Projects
Elektriska delade självkörande fordon i det framtida fossiloberoende transportsystemet (Eldsjäl)
Funder
Vinnova, 2019-05094Region Västra Götaland, KTN 2019-00124Malmö University, IOTAPMarianne and Marcus Wallenberg Foundation, WASP-HS
Available from: 2023-01-24 Created: 2023-01-24 Last updated: 2023-07-04Bibliographically approved
Engström, J., Jevinger, Å., Olsson, C. M. & Persson, J. A. (2023). Some Design Considerations in Passive Indoor Positioning Systems. Sensors, 23(12), Article ID 5684.
Open this publication in new window or tab >>Some Design Considerations in Passive Indoor Positioning Systems
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 12, article id 5684Article in journal (Refereed) Published
Abstract [en]

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
BLE, fingerprinting, indoor positioning, multilateration, RSSI, privacy
National Category
Signal Processing
Identifiers
urn:nbn:se:mau:diva-61951 (URN)10.3390/s23125684 (DOI)001017806900001 ()37420850 (PubMedID)2-s2.0-85163999180 (Scopus ID)
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-10-03Bibliographically approved
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Open this publication in new window or tab >>The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 11, article id 6516Article in journal (Refereed) Published
Abstract [en]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

Place, publisher, year, edition, pages
MDPI, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Available from: 2023-06-07 Created: 2023-06-07 Last updated: 2023-09-05Bibliographically approved
Bugeja, J. & Persson, J. A. (2022). A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups. In: Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST: . Paper presented at 18th International Conference on Web Information Systems and Technologies - WEBIST, 2022 , Valletta, Malta (pp. 182-189). SciTePress
Open this publication in new window or tab >>A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups
2022 (English)In: Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST, SciTePress, 2022, p. 182-189Conference paper, Published paper (Refereed)
Abstract [en]

A major concern in the use of Internet of Things (IoT) technologies in general is their reliability in the presence of security threats and cyberattacks. Particularly, there is a growing recognition that IoT environments featuring virtual sensing and interactive machine learning may be subject to additional vulnerabilities when compared to traditional networks and classical batch learning settings. Partly, this is as adversaries could more easily manipulate the user feedback channel with malicious content. To this end, we propose a data-centric anomaly-based detection system, based on machine learning, that facilitates the process of identifying anomalies, particularly those related to poisoning integrity attacks targeting the user feedback channel of interactive machine learning setups. We demonstrate the capabilities of the proposed system in a case study involving a smart campus setup consisting of different smart devices, namely, a smart camera, a climate sensmitter, smart lighting, a smart phone, and a user feedback channel over which users could furnish labels to improve detection of correct system states, namely, activity types happening inside a room. Our results indicate that anomalies targeting the user feedback channel can be accurately detected at 98% using the Random Forest classifier.

Place, publisher, year, edition, pages
SciTePress, 2022
Series
WEBIST, E-ISSN 2184-3252
Keywords
Anomaly Detection, Interactive Machine Learning, Internet of Things, Virtual Sensors, Intrusion Detection, Poisoning Attack, IoT Security
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mau:diva-55923 (URN)10.5220/0011560100003318 (DOI)2-s2.0-85146200321 (Scopus ID)978-989-758-613-2 (ISBN)
Conference
18th International Conference on Web Information Systems and Technologies - WEBIST, 2022 , Valletta, Malta
Available from: 2022-11-10 Created: 2022-11-10 Last updated: 2023-12-12Bibliographically approved
Jevinger, Å., Johansson, E., Persson, J. A. & Holmberg, J. (2022). Kontextmedvetet resestöd vid störningar i kollektivtrafiken (juli 2021-oktober 2022): Slutrapport forskningsprojekt TRV 2021/40633. Malmö universitet
Open this publication in new window or tab >>Kontextmedvetet resestöd vid störningar i kollektivtrafiken (juli 2021-oktober 2022): Slutrapport forskningsprojekt TRV 2021/40633
2022 (Swedish)Report (Other academic)
Place, publisher, year, edition, pages
Malmö universitet, 2022. p. 33
Keywords
kontextmedveten, reseplanerare, resestöd, kollektivtrafiken, störningar
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:mau:diva-55703 (URN)
Projects
Kontextmedvetet resestöd vid störningar i kollektivtrafiken
Funder
Swedish Transport Administration, TRV 2021/40633
Available from: 2022-11-08 Created: 2022-11-08 Last updated: 2023-07-04Bibliographically approved
Projects
Internet of Things and People Research Profile; Malmö University; Publications
Banda, L., Mjumo, M. & Mekuria, F. (2022). Business Models for 5G and Future Mobile Network Operators. In: 2022 IEEE Future Networks World Forum (FNWF): . Paper presented at IEEE Future Networks World Forum FNWF 2022, Montreal, QC, Canada, 10-14 October 2022. IEEE, Article ID M17754.
Smart Public Environments II; Malmö UniversityIntelligent Mobility of the Future in Greater Copenhagen; Publications
Dytckov, S., Persson, J. A., Lorig, F. & Davidsson, P. (2022). Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark. Sustainability, 14(6), Article ID 3252.
Dynamic Intelligent Sensor Intensive Systems; Malmö University; Publications
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Towards integrated and adaptive public transport; Publications
Dytckov, S., Davidsson, P. & Persson, J. A. (2023). Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network. In: : . Paper presented at 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023. Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE)
Context-aware travel support in public transport disturbances
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ORCID iD: ORCID iD iconorcid.org/0000-0002-9471-8405

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