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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
Open this publication in new window or tab >>Activity Recognition through Interactive Machine Learning in a Dynamic Sensor Setting
2024 (English)In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 28, no 1, p. 273-286Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
machine learning, interactive machine learning, active learning, machine teaching, online learning, sensor data
National Category
Other Computer and Information Science Computer Sciences
Identifiers
urn:nbn:se:mau:diva-17434 (URN)10.1007/s00779-020-01414-2 (DOI)000538990600002 ()2-s2.0-85086152913 (Scopus ID)
Note

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

Available from: 2020-06-07 Created: 2020-06-07 Last updated: 2024-09-17Bibliographically approved
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
Open this publication in new window or tab >>Artificial intelligence for improving public transport: a mapping study
2024 (English)In: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, Vol. 16, no 1, p. 99-158Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Springer, 2024
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: 2024-04-11Bibliographically approved
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.
Open this publication in new window or tab >>Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches
Show others...
2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 5, article id 1533Article, review/survey (Refereed) 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.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
passive infrared sensors (PIR), smart buildings, IoT (internet of things), occupancy information, people counting, activity detection, machine learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mau:diva-66548 (URN)10.3390/s24051533 (DOI)001183072000001 ()38475069 (PubMedID)2-s2.0-85187481668 (Scopus ID)
Available from: 2024-03-28 Created: 2024-03-28 Last updated: 2024-05-02Bibliographically approved
Akin, E., Caltenco, H., Adewole, K. S., Malekian, R. & Persson, J. A. (2024). Segment Anything Model (SAM) Meets Object Detected Box Prompts. In: 2024 IEEE International Conference on Industrial Technology (ICIT): . Paper presented at 2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, United Kingdom, 25-27 March 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Segment Anything Model (SAM) Meets Object Detected Box Prompts
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2024 (English)In: 2024 IEEE International Conference on Industrial Technology (ICIT), Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]

Segmenting images is an intricate and exceptionally demanding field within computer vision. Instance Segmentation is one of the subfields of image segmentation that segments objects on a given image or video. It categorizes the class labels according to individual instances, ensuring that distinct instance markers are assigned to each occurrence of the same object class, even if multiple instances exist. With the development of computer systems, segmentation studies have increased very rapidly. One of the state-of-the-art algorithms recently published by Meta AI, which segments everything on a given image, is called the Segment Anything Model (SAM). Its impressive zero-shot performance encourages us to use it for diverse tasks. Therefore, we would like to leverage the SAM for an effective instance segmentation model. Accordingly, in this paper, we propose a hybrid instance segmentation method in which Object Detection algorithms extract bounding boxes of detected objects and load SAM to produce segmentation, called Box Prompted SAM (BP-SAM). Experimental evaluation of the COCO2017 Validation dataset provided us with promising performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
IEEE International Conference on Industrial Technology, ISSN 2641-0184, E-ISSN 2643-2978
Keywords
SAM, Segment Anything Model, Object Detection, Instance Segmentation, Computer Vision
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:mau:diva-70258 (URN)10.1109/icit58233.2024.10541006 (DOI)2-s2.0-85195782363 (Scopus ID)979-8-3503-4026-6 (ISBN)979-8-3503-4027-3 (ISBN)
Conference
2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, United Kingdom, 25-27 March 2024
Funder
Knowledge Foundation, 20220087-H-01
Available from: 2024-08-15 Created: 2024-08-15 Last updated: 2024-08-15Bibliographically approved
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: 2024-06-17Bibliographically 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)10.1109/ITSC57777.2023.10422047 (DOI)001178996702011 ()2-s2.0-85186522768 (Scopus ID)
Conference
26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Available from: 2023-09-08 Created: 2023-09-08 Last updated: 2024-07-30Bibliographically 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
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
Jevinger, Å. & Svensson, H. (2024). Stated opinions and potential travel with DRT – a survey covering three different age groups. Transportation planning and technology (Print), 47(7), 968-995Dytckov, 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 disturbancesAI Enhanced Mobility
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9471-8405

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