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  • 1.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Archer, Jeffery
    Sweco Infrastructure, Sweden.
    Bayen, Alexandre
    University of California, Berkeley, USA.
    Blandin, Sebastian
    University of California, Berkeley, USA.
    Butler, Joe
    California Center for Innovative Transportation. USA.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Koutsopoulose, Haris N
    Royal Institute of Technology (KTH), Sweden.
    Lundgren, Jan
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rahmani, Mahmood
    Royal Institute of Technology (KTH), Sweden.
    Tossavainen, Olli-Pekka
    NAVTEQ LLC, USA.
    Mobile Millennium Stockholm2011In: 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, 2011Conference paper (Other academic)
  • 2.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Barceló, Jaume
    Department of Statistics and Operations Research, Universitat Politècnica de Catalunya.
    Ekström, Joakim
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Grumert, Ellen
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Traffic management for smart cities2017In: Designing, developing, and facilitating smart cities: urban design to IoT solutions. Part III / [ed] Vangelis Angelakis, Elias Tragos, Henrich C. Pöhls, Adam Kapovits and Alessandro Bassi, Switzerland: Springer, 2017, p. 211-240Chapter in book (Other academic)
    Abstract [en]

    Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

  • 3.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology. Sweco TransportSystem, Stockholm, Sweden.
    Bayen, Alexandre M.
    University of California Berkeley, USA.
    Fransson, Magnus
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology. Sweco TransportSystem, Stockholm, Sweden.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Patire, Anthony D.
    University of California Berkeley, USA.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Sandin, Mats
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Calibration Framework based on Bluetooth Sensors for Traffic State Estimation Using a Velocity based Cell Transmission Model2014In: Transportation Research Procedia, ISSN 2352-1465, Vol. 3, p. 972-981Article in journal (Refereed)
    Abstract [en]

    The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are presented for a highway stretch north of Stockholm, Sweden.

  • 4.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ekström, Joakim
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bayen, Alexandre M.
    Department of Civil and Environmental Engineering, University of California.
    Patire, Anthony D.
    Department of Civil and Environmental Engineering, University of California.
    A hybrid approach for short-term traffic state and travel time prediction on highways2016In: TRB 95th annual meeting compendium of papers, 2016Conference paper (Refereed)
    Abstract [en]

    Traffic management and traffic information are essential in urban areas, and require a good knowledge about both the current and the future traffic state. Both parametric and non-parametric traffic state prediction techniques have previously been developed, with different advantages and shortcomings. While non-parametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions such as incidents and events. Hybrid approaches, combining the two prediction paradigms have previously been proposed by using non-parametric methods for predicting boundary conditions used in a parametric method. In this paper we instead combine parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction method a neural network method is adopted, and the parametric prediction is carried out using a cell transmission model with velocity as state. The results show that our hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction horizon of up to 50 minutes ahead in time to allow the journey to be completed.

  • 5.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ekström, Joakim
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bayen, Alexandre M.
    Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
    Patire, Anthony D.
    Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
    Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways2016In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, Vol. 2554, p. 60-68Article in journal (Refereed)
    Abstract [en]

    Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed

  • 6.
    Allström, Andreas
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Evaluation of travel time estimation based on LWR-v and CTM-v: A case study in Stockholm2012In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2012, Piscataway, N.J, USA: IEEE , 2012, p. 1644-1649Conference paper (Refereed)
    Abstract [en]

    Real-time estimations of current and future traffic states are an essential part of traffic management and traffic information systems. Within the Mobile Millennium project considerable effort has been invested in the research and development of a real-time estimation system that can fuse several sources of data collected in California. During the past year this system has been adapted to also handle traffic data collected in Stockholm. This paper provides an overview of the model used for highways and presents results from an initial evaluation of the system. As part of the evaluation process, GPS data collected in an earlier field-test and estimations generated by the existing system used by the TMC in Stockholm, are compared with the estimations generated by the Mobile Millennium system. Given that the Mobile Millennium Stockholm system has not undergone any calibration, the results from the evaluation are considered promising. The estimated travel times correspond well to those measured in the field test. Furthermore, the estimations generated by the Mobile Millennium system can be regarded as superior to those of existing traffic management system in Stockholm. The highway model was found to perform well even with a reduction in the number of sensors providing data. The findings of this study indicate the robustness of the Mobile Millennium system and demonstrate how the system can be migrated to other geographical areas with similar sources of available data.

  • 7.
    Angelakis, Vangelis
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Askoxylakis, Ioannis
    FORTH, Institute of Computer Science, Greece.
    Fowler, Scott
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Traganitis, Apostolos
    FORTH, Institute of Computer Science, Greece.
    Yuan, Di
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Security and Resilience in Cognitive Radio Networks2011In: European Research Consortium for Informatics and Mathematics Magazine, ISSN 0926-4981, no 85, p. 48-49Article in journal (Refereed)
    Abstract [en]

    After more than a decade of research, system securityand resilience is now the major technological barrier forthe Cognitive Radio (CR) to be adopted by the telecommunication industry. New ideas are required tomake CR networks secure and robust against attacks taking advantage the inherent characteristics of the CR functionality. This work explores key points that urgentlyneed to be addressed.

  • 8.
    Angelakis, Vangelis
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rajna, Botond
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Vrotsou, Katerina
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Carlsson, Richard
    Ericsson Research, Services & Software.
    Forgeat, Julien
    Ericsson Research, Services & Software.
    Hu, Tracy H
    Ericsson Research, Services & Software.
    Liu, Evan L
    Ericsson Research, Services & Software.
    Moritz, Simon
    Ericsson Research, Services & Software.
    Zhao, Sky
    Ericsson Research, Services & Software.
    Zheng, Yaotian
    Ericsson Research, Services & Software.
    Mobility modeling for transport efficiency: Analysis of travel characteristics based on mobile phone data2013In: Netmob 2013: Mobile phone data for development / [ed] Vincent Blondel, 2013Conference paper (Refereed)
    Abstract [en]

    Signaling data from the cellular networks can provide a means of analyzing the efficiency of a deployed transportation system and assisting in the formulation of transport models to predict its future use. An approach based on this type of data can be especially appealing for transportation systems that need massive expansions, since it has the added benefit that no specialized equipment or installations are required, hence it can be very cost efficient.

    Within this context in this paper we describe how such obtained data can be processed and used in order to act as enablers for traditional transportation analysis models. We outline a layered, modular architectural framework that encompasses the entire process and present results from initial analysis of mobile phone call data in the context of mobility, transport and transport infrastructure. We finally introduce the Mobility Analytics Platform, developed by Ericsson Research, tailored for mobility analysis, and discuss techniques for analyzing transport supply and demand, and give indication on how cell phone use data can be used directly to analyze the status and use of the current transport infrastructure.

  • 9.
    Breyer, Nils
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bäckman, Johan
    Former Tele2.
    Trip extraction for traffic analysis using cellular network data2017In: 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) / [ed] IEEE Italy Section, Naples: IEEE Press, 2017, p. 321-326Conference paper (Refereed)
    Abstract [en]

    To get a better understanding of people’s mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low. 

  • 10.
    Breyer, Nils
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data2018In: The Journal of urban technology, ISSN 1063-0732, E-ISSN 1466-1853, no 2, p. 85-104Article in journal (Refereed)
    Abstract [en]

    The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network from call detail records (CDRs) which are spatially and temporally sparse. The route estimation methods are compared based on the individual routes estimated. We also investigate the effect of different route estimation methods when employed in a complete network assignment for a larger city. Using an available CDR dataset for Dakar, Senegal, we show that the choice of the route estimation method can have a significant impact on resulting link flows.

  • 11.
    Grumert, Ellen
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering. Traffic analysis and logistics, Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Bernhardsson, Viktor
    Traffic analysis and logistics, Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Ekström, Joakim
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Tapani, Andreas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Variabla hastighetsgränser för Stockholms motorvägsnät: Effekter av alternativa algoritmer och möjligheter till styrning genom skattade trafiktillstånd2019Report (Other academic)
    Abstract [en]

    Variable speed limits are commonly used on Stockholm’s urban motorways, and it is part of the Stockholm Motorway Control System (MCS). The goal of today’s system is to reduce the risk of accidents during congested conditions, traffic jams etc. This is done by updating the speed limits shown on variable message signs based on a measured average speed at fixed detectors. As the demand for travel in Stockholm during peak-hours exceeds the available capacity in the road network, the need for an efficient traffic system is high. Variable speed limit systems have the possibility to contribute to increased efficiency, but since today’s system aims to increase safety, effects that lead to increased efficiency are limited. Further, in todays’ variable speed limit systems there are a large dependency of precise and available measurements from stationary detectors to be able to display speed limits that reflects the current traffic conditions. The purpose of this report is to investigate alternative control algorithms to decide on the variable speed limits to be displayed at variable message signs on the urban motorway of Stockholm.

    The goal is to increase efficiency compared to today's system. Two different road stretches with different complexity and different traffic conditions, resulting in two different types of congestion, are studied. Thereby, the studied control algorithms on the two road stretches are chosen based on the possibility of solving a specific problematic traffic situation in the best way. Hence, the studied control algorithms might differ for the two road stretches. Furthermore, for one of the roads stretches it is investigated if estimation of the traffic state can be used as input to the control algorithm as a complement to missing and erogenous measurements from stationary detectors in order to improve the calculations of the variable speed limits. The control algorithms are evaluated with microscopic traffic simulation and the method developed in the project Mobile Millenium Stockholm (MMS), using a macroscopic traffic flow model together with a Kalman filter, is used for estimation of the traffic state.

    The result shows that there are control algorithms with the potential to increase efficiency. However, the choice of suitable control algorithm for improving traffic efficiency is dependent on the traffic situation, the complexity of the road design and the traffic conditions. Furthermore, estimation of the traffic state is useful when information is lost due to malfunctioning detectors or as a complement to reduce the amount of stationary detectors.

  • 12.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Generating Road Traffic Information Based on Cellular Network Signaling2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cellular networks of today generate a massive amount of signalling data. A large part of this signalling is generated to handle the mobility of subscribers, irrespective of the subscriber actively uses the terminal or not. Hence it contains location information that can be used to fundamentally change our understanding of human travel patterns.

    This thesis aims to analyse the potential and limitations of using this signalling data in the context of road traffic information, i.e. how we can estimate the road network traffic state based on standard signalling data already available in cellular networks. This is achieved by analytical examination and experiments with signalling data and measurements generated by standard cell phones.

    The thesis describes the location data that is available from signalling messages in GSM, GPRS and UMTS networks, both in idle mode and when engaged in a telephone call or a data session. The signalling data available in a ll three networks is useful to estimate traffic information, although the resolution in time and space will to a large extent depend on in which mode the terminal is operating.

    Spatial analysis of handover signalling data has been performed for terminals engaged in telephone calls. The analysis indicates that handover events from both GSM and UMTS networks can be used as efficient input to systems for travel time estimation, given that route classification and filtering of non -vehicle terminals can be solved.

    By analysing signalling data and received signal strength (RSS) measur ements from cell phones, it can be seen the route classification problem in the context of estimating travel times based on handover events is non -trivial even for highway environments. However, it is presented that the problem can be sa tisfactory solved for highway environments by using basic classification methods, like for example Bayesian classification.

    Furthermore the thesis points out that the new era of smartphones can be an enabler for road traffic information from cellular networks in the close future. By examining measurements collected by a smartphone client, it is illu strated how the radio map for cell phone positioning can be built by participatory sensing. It is also shown that the location accuracy of RSS-based cell phone positioning is accurate enough to p rovide both travel time and OD-matrix estimation.

    List of papers
    1. Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS
    Open this publication in new window or tab >>Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS
    2006 (English)In: Proceedings 2006 6th International Conference onITS Telecommunications / [ed] Guangjun Wen, Shozo Komaki, Pingzhi Fan and Grabrielle Landrac, 2006, p. 1128-1133Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper summarizes different approaches to collecting road traffic information from second-generation cellular systems (GSM) and point out the possibilities that arise when third generation systems (UMTS) are used. Cell breathing is a potential problem, but smaller cells, soft handover and flexible measurements have the potential to increase the usage area and information quality when road traffic information is extracted from the UMTS network compared to using the GSM network

    Keywords
    Intelligent Transport Systems, cellular positioning
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-40860 (URN)10.1109/ITST.2006.288805 (DOI)54353 (Local ID)0-7803-9587-5 (ISBN)0-7803-9587-5 (ISBN)54353 (Archive number)54353 (OAI)
    Conference
    6th International Conference on ITS Telecommunications (ITS-T), June 21-23, Chengdu, China
    Available from: 2013-04-05 Created: 2009-10-10 Last updated: 2016-05-04Bibliographically approved
    2. Handover location accuracy for travel time estimation in GSM and UMTS
    Open this publication in new window or tab >>Handover location accuracy for travel time estimation in GSM and UMTS
    2009 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 3, no 1, p. 87-94Article in journal (Refereed) Published
    Abstract [en]

    Field measurements from the GSM and UMTS networks are analysed in a road traffic information context. The measurements indicate a potentially large improvement using UMTS signalling data compared with GSM regarding handover location accuracy. These improvements can be used to generate real-time traffic information with higher quality and extend the geographic usage area for cellular-based travel time estimation systems. The results con. rm previous reports indicating that the technology has a large potential in GSM and also show that the potential might be even larger and more. exible using UMTS. Assuming that non-vehicle terminals can be. ltered out, that vehicles are tracked to the correct route and that handovers can be predicted correctly, a conclusion from the experiments is that the handover location accuracy in both GSM and UMTS will be sufficient to estimate useful travel times, also in urban environments. In a real system, these tasks are typically very challenging, especially in an urban environment. Further, it is reasonably established that the location error will be minor for the data obtained from UMTS.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-16517 (URN)10.1049/iet-its:20070067 (DOI)
    Available from: 2013-04-05 Created: 2009-01-30 Last updated: 2018-11-15Bibliographically approved
    3. Route Classification in Travel Time Estimation Based on Cellular Network Signaling
    Open this publication in new window or tab >>Route Classification in Travel Time Estimation Based on Cellular Network Signaling
    2009 (English)In: Proceedings of 12th International IEEE Conference on Intelligent Transport Systems (ITSC), October 3-7, St. Louis, USA, 2009, p. 474-479Conference paper, Published paper (Refereed)
    Abstract [en]

    Travel time estimation based on cellular network signaling is a promising technology for delivery of wide area travel times in real-time. The technology has received much attention recently, but few academic research reports has so far been published in the area, which together with uncertain location estimates and environmental dependent performance makes it difficult to assess the potential of the technology. This paper aims to investigate the route classification task in a cellular travel time estimation context in detail. In order to estimate the magnitude of the problem, two classification algorithms are developed, one based on nearest neighbor classification and one based on Bayesian classification. These are then evaluated using field measurements from the GSM network. A conclusion from the results is that the route classification problem is not trivial even in a highway environment, due to effects of multipath propagation and changing radio environment. In a highway environment the classification problem can be solved rather efficiently using e.g., one of the methods described in this paper, keeping the effect on travel time accuracy low. However, in order to solve the route classification task in urban environments more research is required.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-50949 (URN)10.1109/ITSC.2009.5309692 (DOI)978-1-4244-5520-1 (ISBN)978-1-4244-5519-5 (ISBN)
    Conference
    12th International IEEE Conference on Intelligent Transport Systems (ITSC), October 3-7, St. Louis, USA
    Available from: 2013-04-05 Created: 2009-10-15 Last updated: 2018-11-15Bibliographically approved
    4. The Smartphone as Enabler for Road Traffic Information Based on Cellular Network Signalling
    Open this publication in new window or tab >>The Smartphone as Enabler for Road Traffic Information Based on Cellular Network Signalling
    2012 (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    The higher penetration rate of GPS-enabled smartphones together with their improved processing power and battery life makes them suitable for a nu mber of participatory sensing applications. The purpose of this paper is to an alyse how GPS-enabled smartphones can be used in a participatory sensingcontext to build a radio map for RSS-based positioning, with a special focus on road traffic information based on cellular network signalling.

    The CEP-67 location accuracy achieved is 75 meters for both GSM and UMTS using Bayesian classification. For this test site, the accuracy is similar for GSM and UMTS, with slightly better results for UMTS in the CEP-95 error metric.

    The location accuracy achieved is good enough to avoid large errors in travel time estimation for highway environments, especially considering the possibility to filter out estimates with low accuracy using for example the posterior bin probability in Bayesian classification. For urban environments more research is required to determine how the location accuracy will affect the path inference problem in a dense road network. The location accuracy achieved in this paper is also sufficient for other traffic information types, for example origin-destination estimation based on location area updates.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-87739 (URN)
    Available from: 2013-01-22 Created: 2013-01-22 Last updated: 2016-05-04Bibliographically approved
  • 13.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Transport Analytics Based on Cellular Network Signalling Data2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cellular networks of today generate a massive amount of signalling data. A large part of this signalling is generated to handle the mobility of subscribers and contains location information that can be used to fundamentally change our understanding of mobility patterns. However, the location data available from standard interfaces in cellular networks is very sparse and an important research question is how this data can be processed in order to efficiently use it for traffic state estimation and traffic planning.

    In this thesis, the potentials and limitations of using this signalling data in the context of estimating the road network traffic state and understanding mobility patterns is analyzed. The thesis describes in detail the location data that is available from signalling messages in GSM, GPRS and UMTS networks, both when terminals are in idle mode and when engaged in a telephone call or a data session. The potential is evaluated empirically using signalling data and measurements generated by standard cellular phones. The data used for analysis of location estimation and route classification accuracy (Paper I-IV in the thesis) is collected using dedicated hardware and software for cellular network analysis as well as tailor-made Android applications. For evaluation of more advanced methods for travel time estimation, data from GPS devices located in Taxis is used in combination with data from fixed radar sensors observing point speed and flow on the road network (Paper V). To evaluate the potential in using cellular network signalling data for analysis of mobility patterns and transport planning, real data provided by a cellular network operator is used (Paper VI).

    The signalling data available in all three types of networks is useful to estimate several types of traffic data that can be used for traffic state estimation as well as traffic planning. However, the resolution in time and space largely depends on which type of data that is extracted from the network, which type of network that is used and how it is processed.

    The thesis proposes new methods based on integrated filtering and classification as well as data assimilation and fusion that allows measurement reports from the cellular network to be used for efficient route classification and estimation of travel times. The thesis also shows that participatory sensing based on GPS equipped smartphones is useful in estimating radio maps for fingerprint-based positioning as well as estimating mobility models for use in filtering of course trajectory data from cellular networks.

    For travel time estimation, it is shown that the CEP-67 location accuracy based on the proposed methods can be improved from 111 meters to 38 meters compared to standard fingerprinting methods. For route classification, it is shown that the problem can be solved efficiently for highway environments using basic classification methods. For urban environments the link precision and recall is improved from 0.5 and 0.7 for standard fingerprinting to 0.83 and 0.92 for the proposed method based on particle filtering with integrity monitoring and Hidden Markov Models.

    Furthermore, a processing pipeline for data driven network assignment is proposed for billing data to be used when inferring mobility patterns used for traffic planning in terms of OD matrices, route choice and coarse travel times. The results of the large-scale data set highlight the importance of the underlying processing pipeline for this type of analysis. However, they also show very good potential in using large data sets for identifying needs of infrastructure investment by filtering out relevant data over large time periods.

    List of papers
    1. The Smartphone As Enabler for Road Traffic Information Based on Cellular Network Signalling
    Open this publication in new window or tab >>The Smartphone As Enabler for Road Traffic Information Based on Cellular Network Signalling
    2013 (English)In: Intelligent Transportation Systems (ITSC), 2013, IEEE , 2013, p. 2106-2112Conference paper, Published paper (Refereed)
    Abstract [en]

    The higher penetration rate of GPS-enabled smartphones together with their improved processing power and battery life makes them suitable for a number of participatory sensing applications. The purpose of this paper is to analyse how GPS-enabled smartphones can be used in a participatory sensing context to build a radio map for RSS-based positioning, with a special focus on road traffic information based on cellular network signalling. The CEP-67 location accuracy achieved is 75 meters for both GSM and UMTS using Bayesian classification. For this test site, the accuracy is similar for GSM and UMTS, with slightly better results for UMTS in the CEP-95 error metric. The location accuracy achieved is good enough to avoid large errors in travel time estimation for highway environments, especially considering the possibility to filter out estimates with low accuracy using for example the posterior bin probability in Bayesian classification. For urban environments more research is required to determine how the location accuracy will affect the path inference problem in a dense road network. The location accuracy achieved in this paper is also sufficient for other traffic information types, for example origin-destination estimation based on location area updates.

    Place, publisher, year, edition, pages
    IEEE, 2013
    National Category
    Engineering and Technology Transport Systems and Logistics
    Identifiers
    urn:nbn:se:liu:diva-102022 (URN)10.1109/ITSC.2013.6728540 (DOI)978-147992914-6 (ISBN)
    Conference
    16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 6-9 October 2013, The Hague, Netherlands
    Available from: 2013-11-26 Created: 2013-11-26 Last updated: 2018-11-15
    2. Handover location accuracy for travel time estimation in GSM and UMTS
    Open this publication in new window or tab >>Handover location accuracy for travel time estimation in GSM and UMTS
    2009 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 3, no 1, p. 87-94Article in journal (Refereed) Published
    Abstract [en]

    Field measurements from the GSM and UMTS networks are analysed in a road traffic information context. The measurements indicate a potentially large improvement using UMTS signalling data compared with GSM regarding handover location accuracy. These improvements can be used to generate real-time traffic information with higher quality and extend the geographic usage area for cellular-based travel time estimation systems. The results con. rm previous reports indicating that the technology has a large potential in GSM and also show that the potential might be even larger and more. exible using UMTS. Assuming that non-vehicle terminals can be. ltered out, that vehicles are tracked to the correct route and that handovers can be predicted correctly, a conclusion from the experiments is that the handover location accuracy in both GSM and UMTS will be sufficient to estimate useful travel times, also in urban environments. In a real system, these tasks are typically very challenging, especially in an urban environment. Further, it is reasonably established that the location error will be minor for the data obtained from UMTS.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-16517 (URN)10.1049/iet-its:20070067 (DOI)
    Available from: 2013-04-05 Created: 2009-01-30 Last updated: 2018-11-15Bibliographically approved
    3. Route Classification in Travel Time Estimation Based on Cellular Network Signaling
    Open this publication in new window or tab >>Route Classification in Travel Time Estimation Based on Cellular Network Signaling
    2009 (English)In: Proceedings of 12th International IEEE Conference on Intelligent Transport Systems (ITSC), October 3-7, St. Louis, USA, 2009, p. 474-479Conference paper, Published paper (Refereed)
    Abstract [en]

    Travel time estimation based on cellular network signaling is a promising technology for delivery of wide area travel times in real-time. The technology has received much attention recently, but few academic research reports has so far been published in the area, which together with uncertain location estimates and environmental dependent performance makes it difficult to assess the potential of the technology. This paper aims to investigate the route classification task in a cellular travel time estimation context in detail. In order to estimate the magnitude of the problem, two classification algorithms are developed, one based on nearest neighbor classification and one based on Bayesian classification. These are then evaluated using field measurements from the GSM network. A conclusion from the results is that the route classification problem is not trivial even in a highway environment, due to effects of multipath propagation and changing radio environment. In a highway environment the classification problem can be solved rather efficiently using e.g., one of the methods described in this paper, keeping the effect on travel time accuracy low. However, in order to solve the route classification task in urban environments more research is required.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-50949 (URN)10.1109/ITSC.2009.5309692 (DOI)978-1-4244-5520-1 (ISBN)978-1-4244-5519-5 (ISBN)
    Conference
    12th International IEEE Conference on Intelligent Transport Systems (ITSC), October 3-7, St. Louis, USA
    Available from: 2013-04-05 Created: 2009-10-15 Last updated: 2018-11-15Bibliographically approved
    4. Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering
    Open this publication in new window or tab >>Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering
    Show others...
    2015 (English)In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, IEEE conference proceedings, 2015, p. 2121-2128Conference paper, Published paper (Refereed)
    Abstract [en]

    The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2015
    Series
    IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
    Keywords
    Cell Transmisson Model, Data fusion, Ensemble Kalman Filtering, Traffic state estimation
    National Category
    Other Electrical Engineering, Electronic Engineering, Information Engineering Transport Systems and Logistics
    Identifiers
    urn:nbn:se:liu:diva-129376 (URN)10.1109/ITSC.2015.343 (DOI)000376668802033 ()978-1-4673-6595-6 (ISBN)
    Conference
    2015 IEEE 18th International Conference on Intelligent Transportation Systems. 15-18 Sept. 2015, Las Palmas
    Available from: 2016-06-17 Created: 2016-06-17 Last updated: 2018-11-15Bibliographically approved
    5. Travel demand estimation and network assignment based on cellular network data
    Open this publication in new window or tab >>Travel demand estimation and network assignment based on cellular network data
    2016 (English)In: COMPUTER COMMUNICATIONS, ISSN 0140-3664, Vol. 95, p. 29-42Article in journal (Refereed) Published
    Abstract [en]

    Cellular networks signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in order to act as means for generating input for traditional transportation analysis models. Specifically, we propose a tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones. The results are based on the analysis of data from the Data for development "D4D" challenge and include data from Cote dlvoire and Senegal. (C) 2016 Elsevier B.V. All rights reserved.

    Place, publisher, year, edition, pages
    ELSEVIER SCIENCE BV, 2016
    Keywords
    Mobility analytics; Travel demand estimation; Traffic modeling; Mobile phone call data; Cellular network data; Call detail records; Intelligent transport systems
    National Category
    Computer Engineering
    Identifiers
    urn:nbn:se:liu:diva-134086 (URN)10.1016/j.comcom.2016.04.015 (DOI)000390722300004 ()
    Note

    Funding Agencies|Swedish Governmental Agency for Innovation Systems (VINNOVA)

    Available from: 2017-01-26 Created: 2017-01-22 Last updated: 2018-11-19
  • 14.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Akram, Awais
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Fowler, Scott
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Ahmad, Hamad
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Cellular Positioning Using Fingerprinting Based on Observed Time Differences2013In: IEEE 4th International Conference on Smart Communications in Network Technologies (SaCoNet), IEEE conference proceedings, 2013, p. 1-5Conference paper (Refereed)
    Abstract [en]

    Cellular positioning has been a very active research area for the last decade. Large improvement in accuracy has been made to support, for example, e-call and other location-based services. Traditionally, cellular positioning has been limited to cellular operators equipped with expensive synchronization hardware in order to achieve good accuracy. Lately, third parties have employed fingerprinting methods to enable positioning systems independent from the cellular operators. With improved available processing power, denser cellular networks, cheaper data collection and efficient pattern matching algorithms, the fingerprinting positioning methods have also gained popularity. In this paper, we analyzed the potential of using System Frame Number (SFN) - SFN observed time differences, which are traditionally used in Time Difference of Arrival (TDOA) positioning, for fingerprinting-based positioning. A field test was performed using measurements from TEMS (Telecommunication Management System) Investigation. By combining SFN-SFN observed time differences with Received Signal Code Power (RSCP) measurements, we demonstrated an improved accuracy of the fingerprinting method by 20% compared to only using RSCP measurements. The results are promising and show good potential in using SFN-SFN observed time differences for positioning based on fingerprinting.

  • 15.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Allström, Andreas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bergfeldt, Erik
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Ringdahl, Rasmus
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Bayen, Alexandre M.
    University of California, Berkeley, USA.
    Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering2015In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, IEEE conference proceedings, 2015, p. 2121-2128Conference paper (Refereed)
    Abstract [en]

    The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

  • 16.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS2006In: Proceedings 2006 6th International Conference onITS Telecommunications / [ed] Guangjun Wen, Shozo Komaki, Pingzhi Fan and Grabrielle Landrac, 2006, p. 1128-1133Conference paper (Refereed)
    Abstract [en]

    This paper summarizes different approaches to collecting road traffic information from second-generation cellular systems (GSM) and point out the possibilities that arise when third generation systems (UMTS) are used. Cell breathing is a potential problem, but smaller cells, soft handover and flexible measurements have the potential to increase the usage area and information quality when road traffic information is extracted from the UMTS network compared to using the GSM network

  • 17.
    Gundlegård, David
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Karlsson, Johan M
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Handover Location Accuracy for Travel Time Estimation in GSM and UMTS2007In: ITS World Congress,2007, 2007Conference paper (Other academic)
  • 18.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Handover location accuracy for travel time estimation in GSM and UMTS2009In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 3, no 1, p. 87-94Article in journal (Refereed)
    Abstract [en]

    Field measurements from the GSM and UMTS networks are analysed in a road traffic information context. The measurements indicate a potentially large improvement using UMTS signalling data compared with GSM regarding handover location accuracy. These improvements can be used to generate real-time traffic information with higher quality and extend the geographic usage area for cellular-based travel time estimation systems. The results con. rm previous reports indicating that the technology has a large potential in GSM and also show that the potential might be even larger and more. exible using UMTS. Assuming that non-vehicle terminals can be. ltered out, that vehicles are tracked to the correct route and that handovers can be predicted correctly, a conclusion from the experiments is that the handover location accuracy in both GSM and UMTS will be sufficient to estimate useful travel times, also in urban environments. In a real system, these tasks are typically very challenging, especially in an urban environment. Further, it is reasonably established that the location error will be minor for the data obtained from UMTS.

  • 19.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Nearest neighbour route classification in travel time estiimation based on cellular network signalling2009In: 16th ITS world congress, 2009, p. 1-10Conference paper (Refereed)
  • 20.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Road Traffic Estimation using Cellular Network Signaling in Intelligent Transportation Systems2009In: Wireless technologies in Intelligent Transportation Systems / [ed] Ming-Tuo Zhou, Yan Zhang, Laurence T Yang, Hauppauge, NY: Nova Science Publishers , 2009, p. 1-33Chapter in book (Other academic)
    Abstract [en]

    In the area of Intelligent Transportation Systems the introduction of wireless communications is reshaping the information distribution concept, and is one of the most important enabling technologies. The distribution of real-time traffic information, scheduling and route-guidance information is helping the transportation management systems in their strive to optimize the system. The communication required to transfer all this information is rather expensive in terms of transmission power, use of the scarce resources of frequencies and also the building of an infrastructure to support the transceivers. By using information that already exists and is exchanged within the infrastructures of the GSM and UMTS networks, a lot of the resource problems are solved. The information that could be extracted from these cellular networks could be used to obtain accurate road traffic information to support real-time traffic information. In this way the cellular networks not only becomes the means to distribute information but also a source of road traffic information.

    From the analysis made it is obvious that the potential of retrieving valuable road traffic information from cellular systems in a cost efficient way, i.e. by using already existing signalling data, is very high. It has however not been clear what to expect from these systems in terms of accuracy, availability and coverage. In this chapter the basics for this is laid out and discussed in detail. A practical trial has also been performed and the results show clearly the potential as well as the differences in using the GSM compared to the UMTS system. The advantages and drawbacks are discussed and backed up by real measurements from an existing road segment environment. The main advantages of using the existing signalling data, i.e., passive monitoring compared to active monitoring where the terminal sends extra data is discussed and could be summarized in three components, no user acceptance is necessary, no extra signalling is necessary and it does not drain the terminal battery.

    In the future it is likely that vehicles need to communicate more frequently with each other and with some kind of traffic control centre. This traffic will also be very useful in order to estimate road traffic information using the signalling information obtained from the cellular system. However, the enhanced communication systems will also change traffic patterns in the cellular networks which will affect the potential of estimating road traffic from cellular systems. The evolvement indicates that the terminals will be in active state almost constantly, and hence the updating information will be more frequent and the information more accurate.

  • 21.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Route Classification in Travel Time Estimation Based on Cellular Network Signaling2009In: Proceedings of 12th International IEEE Conference on Intelligent Transport Systems (ITSC), October 3-7, St. Louis, USA, 2009, p. 474-479Conference paper (Refereed)
    Abstract [en]

    Travel time estimation based on cellular network signaling is a promising technology for delivery of wide area travel times in real-time. The technology has received much attention recently, but few academic research reports has so far been published in the area, which together with uncertain location estimates and environmental dependent performance makes it difficult to assess the potential of the technology. This paper aims to investigate the route classification task in a cellular travel time estimation context in detail. In order to estimate the magnitude of the problem, two classification algorithms are developed, one based on nearest neighbor classification and one based on Bayesian classification. These are then evaluated using field measurements from the GSM network. A conclusion from the results is that the route classification problem is not trivial even in a highway environment, due to effects of multipath propagation and changing radio environment. In a highway environment the classification problem can be solved rather efficiently using e.g., one of the methods described in this paper, keeping the effect on travel time accuracy low. However, in order to solve the route classification task in urban environments more research is required.

  • 22.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    The Smartphone as Enabler for Road Traffic Information Based on Cellular Network Signalling2012Manuscript (preprint) (Other academic)
    Abstract [en]

    The higher penetration rate of GPS-enabled smartphones together with their improved processing power and battery life makes them suitable for a nu mber of participatory sensing applications. The purpose of this paper is to an alyse how GPS-enabled smartphones can be used in a participatory sensingcontext to build a radio map for RSS-based positioning, with a special focus on road traffic information based on cellular network signalling.

    The CEP-67 location accuracy achieved is 75 meters for both GSM and UMTS using Bayesian classification. For this test site, the accuracy is similar for GSM and UMTS, with slightly better results for UMTS in the CEP-95 error metric.

    The location accuracy achieved is good enough to avoid large errors in travel time estimation for highway environments, especially considering the possibility to filter out estimates with low accuracy using for example the posterior bin probability in Bayesian classification. For urban environments more research is required to determine how the location accuracy will affect the path inference problem in a dense road network. The location accuracy achieved in this paper is also sufficient for other traffic information types, for example origin-destination estimation based on location area updates.

  • 23.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Karlsson, Johan M
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    The Smartphone As Enabler for Road Traffic Information Based on Cellular Network Signalling2013In: Intelligent Transportation Systems (ITSC), 2013, IEEE , 2013, p. 2106-2112Conference paper (Refereed)
    Abstract [en]

    The higher penetration rate of GPS-enabled smartphones together with their improved processing power and battery life makes them suitable for a number of participatory sensing applications. The purpose of this paper is to analyse how GPS-enabled smartphones can be used in a participatory sensing context to build a radio map for RSS-based positioning, with a special focus on road traffic information based on cellular network signalling. The CEP-67 location accuracy achieved is 75 meters for both GSM and UMTS using Bayesian classification. For this test site, the accuracy is similar for GSM and UMTS, with slightly better results for UMTS in the CEP-95 error metric. The location accuracy achieved is good enough to avoid large errors in travel time estimation for highway environments, especially considering the possibility to filter out estimates with low accuracy using for example the posterior bin probability in Bayesian classification. For urban environments more research is required to determine how the location accuracy will affect the path inference problem in a dense road network. The location accuracy achieved in this paper is also sufficient for other traffic information types, for example origin-destination estimation based on location area updates.

  • 24.
    Gundlegård, David
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Karlsson, Johan M
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology. Linköping University, Department of Science and Technology, Communications and Transport Systems.
    Mileros, Martin D
    FlexToll AB.
    Furdös, Alexander
    FlexToll AB.
    A Novel Approach to Road User Charging based on Cellular Networks and UCPI2008In: European Congress on ITS,2008, 2008Conference paper (Other academic)
  • 25.
    Gundlegård, David
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Karlsson, Johan M
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Mileros, Martin D
    FlexToll AB.
    Furdös, Alexander
    FlexToll AB.
    Autonomous Cellular Road User Charging Based On Unique Cell Point Identification2008Conference paper (Refereed)
    Abstract [en]

      

  • 26.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Barcelo, Jaime
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Dokoohaki, Nima
    Swedish Institute of Computer Science.
    Hess, Andrea
    Swedish Institute of Computer Science.
    Görnerup, Olof
    Swedish Institute of Computer Science.
    Travel demand analysis with differential private releases2015In: Netmob 2015: Mobile phone data for development / [ed] Vincent Blondel, 2015Conference paper (Refereed)
    Abstract [en]

    The use of mobile phone data for planning of transport infrastructure has been shown to have great potential in providing a means of analyzing the efficiency of a transportation system and assisting in the formulation of transport models to predict its future use. In this paper we describe how this type of data can be processed and used in order to act as both enablers for traditional transportation analysis models, and provide new ways of estimating travel behavior. Specifically, we propose a technique for describing the travel demand by constructing time sliced origin destination matrices which respect the level of detail available in Call Detail Records (CDR) from mobile phone use.

    When analyzing large quantities of human mobility traces, the aspects of sensitivity of traces to be analyzed, and the scale at which such analysis can be accounted for is of high importance. The sensitivity implies that identifiable information must not be inferred from the data or any analysis of it. Thus, prompting the importance of maintaining privacy during or post-analysis stages. We aggregate the raw data with the goal to retain relevant information while at the same time discard sensitive user specifics, through site sequence clustering and frequent sequence extraction. These techniques have at least three benefits: data reduction, information mining, and anonymization. Further, the paper reviews the aggregation techniques with regard to privacy in a post-processing step.

    The approaches presented in the paper for estimation of travel demand and route choices, and the additional privacy analysis, build a comprehensive framework usable in the processing of mobile phone data for transportation planning.

    The project presented in this paper a part of the D4D-Senegal challenge.

  • 27.
    Gundlegård, David
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Breyer, Nils
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rajna, Botond
    Linköping University, Department of Science and Technology. Linköping University, Faculty of Science & Engineering.
    Travel demand estimation and network assignment based on cellular network data2016In: COMPUTER COMMUNICATIONS, ISSN 0140-3664, Vol. 95, p. 29-42Article in journal (Refereed)
    Abstract [en]

    Cellular networks signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in order to act as means for generating input for traditional transportation analysis models. Specifically, we propose a tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones. The results are based on the analysis of data from the Data for development "D4D" challenge and include data from Cote dlvoire and Senegal. (C) 2016 Elsevier B.V. All rights reserved.

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