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Tegen, A., Davidsson, P. & Persson, J. A. (2024). Activity Recognition through Interactive Machine Learning in a Dynamic Sensor Setting. Personal and Ubiquitous Computing, 28(1), 273-286
Öppna denna publikation i ny flik eller fönster >>Activity Recognition through Interactive Machine Learning in a Dynamic Sensor Setting
2024 (Engelska)Ingår i: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 28, nr 1, s. 273-286Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.

Ort, förlag, år, upplaga, sidor
Springer, 2024
Nyckelord
machine learning, interactive machine learning, active learning, machine teaching, online learning, sensor data
Nationell ämneskategori
Annan data- och informationsvetenskap Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-17434 (URN)10.1007/s00779-020-01414-2 (DOI)000538990600002 ()2-s2.0-85086152913 (Scopus ID)
Anmärkning

Correction available: https://doi.org/10.1007/s00779-020-01465-5

Tillgänglig från: 2020-06-07 Skapad: 2020-06-07 Senast uppdaterad: 2024-03-06Bibliografiskt granskad
Jevinger, Å., Zhao, C., Persson, J. A. & Davidsson, P. (2024). Artificial intelligence for improving public transport: a mapping study. Public Transport, 16(1), 99-158
Öppna denna publikation i ny flik eller fönster >>Artificial intelligence for improving public transport: a mapping study
2024 (Engelska)Ingår i: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, Vol. 16, nr 1, s. 99-158Artikel i tidskrift (Refereegranskat) Published
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.

Ort, förlag, år, upplaga, sidor
Springer, 2024
Nyckelord
Artifcial intelligence · Machine learning · Public transit · Mass transit · Public transport · Literature review
Nationell ämneskategori
Datavetenskap (datalogi) Transportteknik och logistik
Forskningsämne
Transportstudier
Identifikatorer
urn:nbn:se:mau:diva-64419 (URN)10.1007/s12469-023-00334-7 (DOI)001104065400001 ()2-s2.0-85177171423 (Scopus ID)
Projekt
AI and public transport: potential and hindrances
Forskningsfinansiär
Vinnova, VINNOVA
Anmärkning

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

Tillgänglig från: 2023-12-14 Skapad: 2023-12-14 Senast uppdaterad: 2024-04-11Bibliografiskt granskad
Shokrollahi, A., Persson, J. A., Malekian, R., Sarkheyli-Hägele, A. & Karlsson, F. (2024). Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches. Sensors, 24(5), Article ID 1533.
Öppna denna publikation i ny flik eller fönster >>Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches
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2024 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 24, nr 5, artikel-id 1533Artikel, forskningsöversikt (Refereegranskat) Published
Abstract [en]

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Nyckelord
passive infrared sensors (PIR), smart buildings, IoT (internet of things), occupancy information, people counting, activity detection, machine learning
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:mau:diva-66548 (URN)10.3390/s24051533 (DOI)001183072000001 ()38475069 (PubMedID)2-s2.0-85187481668 (Scopus ID)
Tillgänglig från: 2024-03-28 Skapad: 2024-03-28 Senast uppdaterad: 2024-04-11Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>Accurate indoor positioning by combining sensor fusion and obstruction compensation
2023 (Engelska)Ingår i: 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347, E-ISSN 2471-917X
Nyckelord
IPS, RTLS, Indoor Positioning, Fingerprinting, Multilateration, Sensor Fusion
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
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)
Konferens
IEEE 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 25-28 September 2023, Nuremberg
Tillgänglig från: 2023-10-03 Skapad: 2023-10-03 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Context-Aware Travel Support During Unplanned Public Transport Disturbances
2023 (Engelska)Ingår i: 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, s. 160-170, artikel-id 19Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Setúbal, Portugal: SciTePress, 2023
Serie
VEHITS, ISSN 2184-495X
Nyckelord
Public Transport, Travel Planner, Context Aware, Prognoses, kontextmedveten, reseplanerare, resestöd, kollektivtrafiken, störningar
Nationell ämneskategori
Transportteknik och logistik
Forskningsämne
Transportstudier
Identifikatorer
urn:nbn:se:mau:diva-59392 (URN)10.5220/0011761000003479 (DOI)001090857700016 ()2-s2.0-85160775089 (Scopus ID)978-989-758-652-1 (ISBN)
Konferens
VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems, April 26-28, 2023, Prague, Czech Republic
Projekt
Kontextmedvetet resestöd vid störningar i kollektivtrafiken
Forskningsfinansiär
Trafikverket, TRV 2021/40633
Tillgänglig från: 2023-05-03 Skapad: 2023-05-03 Senast uppdaterad: 2023-12-05Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Human Factors in Interactive Online Machine Learning
2023 (Engelska)Ingår i: HHAI 2023: Augmenting Human Intellect / [ed] Paul Lukowicz; Sven Mayer; Janin Koch; John Shawe-Taylor; Ilaria Tiddi, IOS Press, 2023, s. 33-45Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IOS Press, 2023
Serie
Frontiers in Artificial Intelligence and Application, ISSN 0922-6389, E-ISSN 1879-8314 ; 368
Nyckelord
interactive machine learning, online learning, human factors
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
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)
Konferens
HHAI 2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, 26-30 June 2023, Munich, Germany
Tillgänglig från: 2023-07-06 Skapad: 2023-07-06 Senast uppdaterad: 2024-02-26Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network
2023 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE), 2023
Nyckelord
Simulation, Demand-Responsive Transport, Public transport
Nationell ämneskategori
Transportteknik och logistik Datavetenskap (datalogi)
Forskningsämne
Transportstudier
Identifikatorer
urn:nbn:se:mau:diva-62399 (URN)
Konferens
26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Tillgänglig från: 2023-09-08 Skapad: 2023-09-08 Senast uppdaterad: 2023-09-15Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Simulating the Impact of Shared Mobility on Demand: a Study of Future Transportation Systems in Gothenburg, Sweden
2023 (Engelska)Ingår i: International Journal of Intelligent Transportation Systems Research, ISSN 1348-8503, Vol. 21, nr 1, s. 129-144Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Nationell ämneskategori
Data- och informationsvetenskap Transportteknik och logistik
Identifikatorer
urn:nbn:se:mau:diva-57773 (URN)10.1007/s13177-023-00345-5 (DOI)000919810900001 ()2-s2.0-85146806659 (Scopus ID)
Projekt
Elektriska delade självkörande fordon i det framtida fossiloberoende transportsystemet (Eldsjäl)
Forskningsfinansiär
Vinnova, 2019-05094Västra Götalandsregionen, KTN 2019-00124Malmö universitet, IOTAPMarianne och Marcus Wallenbergs Stiftelse, WASP-HS
Tillgänglig från: 2023-01-24 Skapad: 2023-01-24 Senast uppdaterad: 2023-07-04Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Some Design Considerations in Passive Indoor Positioning Systems
2023 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 12, artikel-id 5684Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2023
Nyckelord
BLE, fingerprinting, indoor positioning, multilateration, RSSI, privacy
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:mau:diva-61951 (URN)10.3390/s23125684 (DOI)001017806900001 ()37420850 (PubMedID)2-s2.0-85163999180 (Scopus ID)
Tillgänglig från: 2023-08-17 Skapad: 2023-08-17 Senast uppdaterad: 2023-10-03Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>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 (Engelska)Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 11, artikel-id 6516Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2023
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Tillgänglig från: 2023-06-07 Skapad: 2023-06-07 Senast uppdaterad: 2023-09-05Bibliografiskt granskad
Projekt
Forskningsprofilen Internet of Things and People; Malmö universitet; Publikationer
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.
Smarta Offentliga Miljöer II; Malmö universitetFramtidens Intelligenta Mobilitet i Greater Copenhagen; Publikationer
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 universitet; Publikationer
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.
Framtidens integrerade och adaptiva kollektivtrafik; Publikationer
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)
Kontextmedvetet resestöd vid störningar i kollektivtrafiken
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-9471-8405

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