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  • 1. Bachmann, A.
    et al.
    Borgelt, C.
    Gidófalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Incremental frequent route based trajectory prediction2013In: IWCTS 2013 - 6th ACM SIGSPATIAL International Workshop on Computational Transportation Science, Association for Computing Machinery (ACM), 2013, p. 49-54Conference paper (Refereed)
    Abstract [en]

    Recent technological trends enable modern traffic prediction and management systems in which the analysis and prediction of movements of objects is essential. To this extent the present paper proposes IncCCFR - a novel, incremental approach for managing, mining, and predicting the incrementally evolving trajectories of moving objects. In addition to reduced mining and storage costs, a key advantage of the incremental approach is its ability to combine multiple temporally relevant mining results from the past to capture temporal and periodic regularities in movement. The approach and its variants are empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.

  • 2.
    Beatrix Cleff, Evelyn
    et al.
    Aarhus University, School of Business.
    Gidofalvi, Gyözö
    Geomatic ApS - Center for Geoinformatics .
    The legal aspects of a location-based mobile advertising platform2008In: International Journal of Intellectual Property Management, ISSN 1478-9647, Vol. 2, no 3, p. 261-275Article in journal (Refereed)
    Abstract [en]

    Recent advances in Information and Communication Technology (ICT), such as the increasing accuracy of Global Positioning Systems (GPSs) technology and the miniaturisation of wireless communication devices, pave the road for Location-Based Services (LBSs). Among these services, m-advertising is predicted to represent a high-yield revenue stream. In this article, the possibilities of using a Location-Aware Mobile Messenger (LAMM) for the purpose of m-advertising is introduced. To avoid m-advertising becoming an extremely intrusive practice by neglecting the user's privacy, the objective of this article is to introduce a location-based advertising platform which complies with the provisions imposed by European Union (EU) law with regard to personal data protection.

  • 3.
    Cumbane, Silvino Pedro
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Review of Big Data and Processing Frameworks for Disaster Response Applications2019In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 8, no 9, article id 387Article in journal (Refereed)
    Abstract [en]

    Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response systems development usually requires the integration of data from different sources (streaming data sources and data sources at rest) with different characteristics and types, which consequently have different processing needs. Deciding which processing framework to use for a specific big data to perform a given task is usually a challenge for researchers from the disaster management field. Therefore, this paper contributes in four aspects. Firstly, potential big data sources are described and characterized. Secondly, the big data processing frameworks are characterized and grouped based on the sources of data they handle. Then, a short description of each big data processing framework is provided and a comparison of processing frameworks in each group is carried out considering the main aspects such as computing cluster architecture, data flow, data processing model, fault-tolerance, scalability, latency, back-pressure mechanism, programming languages, and support for machine learning libraries, which are related to specific processing needs. Finally, a link between big data and processing frameworks is established, based on the processing provisioning for essential tasks in the response phase of disaster management.

  • 4.
    Cumbane, Silvino Pedro
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    A Framework for Traffic Prediction Integrated with Deep Learning2019Conference paper (Refereed)
    Abstract [en]

    City-scale traffic prediction is an important task for public safety, traffic management, and deployment of intelligent transportation systems. Many approaches have been proposed to address traffic prediction task using machine learning techniques. In this paper, we present a framework to help on addressing the task at hand (density-, traffic flow- and origin-destination flow predictions) considering data type, features, deep learning techniques such as Convolutional Neural Networks (CNNs), e.g., Autoencoder, Recurrent Neural Networks (RNNs), e.g., Long Short Term Memory (LSTM), and Graph Convolutional Networks (GCNs). An autoencoder model is designed in this paper to predict traffic density based on historical data. Experiments on real-world taxi order data demonstrate the effectiveness of the model.

  • 5.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    A Mobile Consumer Analysis Platform2010In: Proceedings of the Workshop on Innovation in Movement Behaviour Analysis: 7th International Conference on Methods and Techniques in Behavioral Research, August 24 - 27, 2010, Eindhoven, Netherlands, 2010, p. 2-Conference paper (Other academic)
    Abstract [en]

    Advances in mobile communication, computing and positioning technologies allow the real–time acquisition of continuously evolving locations of moving objects, e.g., users carrying location-aware mobile devices. This short paper proposes the geo-contextual analysis and data mining of these location traces to deliver deep insight into consumer behavior and enable a number of promising Business Intelligence services.

  • 6.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Scalable Selective Traffic Congestion Notification2015Conference paper (Refereed)
    Abstract [en]

    Congestion is a major problem in most metropolitan areas. Systems that can in a timely manner inform drivers about relevant, current or predicted traffic congestion are paramount for effective traffic management. Without loss of generality, this paper proposes such a system that by adopting a grid-based discretization of space, can flexibly scale the computation cost and the geographic level of detail of traffic information that it provides. From the continuous stream of grid-based position and speed reports from vehicles, the system incrementally derives 1) statistics for detecting directional traffic congestions and 2) model parameters for a time-inhomogeneous, Markov jump process that is used to predict the likelihood that a given vehicle will encounter a detected directional congestion within the notification horizon. A simple but efficient SQL-based prototype implementation of the system that can naturally be ported to Big Data processing frameworks is also explained in detail. Empirical evaluations on millions of object trajectories show that 1) the proposed movement model captures the topology of the underlying road network space and the directional aspects of movement on it, 2) the congestion notification accuracy of the system is superior to a linear movement model based system, and 3) the prototype implementation of the system (i) scales linearly with its input load, notification horizon and spatio-temporal resolution and (ii) can in real-time process 1.14 million object trajectories.

  • 7.
    Gidofalvi, Gyözö
    Geomatic ApS - Center for Geoinformatics.
    Spatio-Temporal Data Mining for Location-Based Services2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Largely driven by advances in communication and information technology, such as the increasing availability and accuracy of GPS technology and the miniaturization of wireless communication devices, Location–Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed.

    The objectives of this thesis are three–fold. First, to extend popular data mining methods to the spatio–temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in two promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio–temporal data mining by devising systems for privacy–preserving location data collection and mining.

     

    To this extent, Chapter 2 presents a general methodology, pivoting, to extend a popular data mining method, namely rule mining, to the spatio–temporal domain. By considering the characteristics of a number of real–world data sources, Chapter 2 also derives a taxonomy of spatio–temporal data, and demonstrates the usefulness of the rules that the extended spatio–temporal rule mining method can discover. In Chapter 4 the proposed spatio–temporal extension is applied to find long, sharable patterns in trajectories of moving objects. Empirical evaluations show that the extended method and its variants, using high–level SQL implementations, are effective tools for analyzing trajectories of moving objects.

    Real–world trajectory data about a large population of objects moving over extended periods within a limited geographical space is difficult to obtain. To aid the development in spatio–temporal data management and data mining, Chapter 3 develops a Spatio–Temporal ACTivity Simulator (ST–ACTS). ST–ACTS uses a number of real–world geo–statistical data sources and intuitive principles to effectively generate realistic spatio–temporal activities of mobile users.

     

    Chapter 5 proposes an LBS in the transportation domain, namely cab–sharing. To deliver an effective service, a unique spatio–temporal grouping algorithm is presented and implemented as a sequence of SQL statements. Chapter 6 identifies ascalability bottleneck in the grouping algorithm. To eliminate the bottleneck, the chapter expresses the grouping algorithm as a continuous stream query in a data stream management system, and then devises simple but effective spatio–temporal partitioning methods for streams to parallelize the computation. Experimental results show that parallelization through adaptive partitioning methods leads to speed–ups of orders of magnitude without significantly effecting the quality of the grouping. Spatio–temporal stream partitioning is expected to be an effective method to scale computation–intensive spatial queries and spatial analysis methods for streams.

     

    Location–Based Advertising (LBA), the delivery of relevant commercial information to mobile consumers, is considered to be one of the most promising business opportunities amongst LBSes. To this extent, Chapter 7 describes an LBA framework and an LBA database that can be used for the management of mobile ads. Using a simulated but realistic mobile consumer population and a set of mobile ads, the LBA database is used to estimate the capacity of the mobile advertising channel. The estimates show that the channel capacity is extremely large, which is evidence for a strong business case, but it also necessitates adequate user controls.

     

    When data about users is collected and analyzed, privacy naturally becomes a concern. To eliminate the concerns, Chapter 8 first presents a grid–based framework in which location data is anonymized through spatio–temporal generalization, and then proposes a system for collecting and mining anonymous location data. Experimental results show that the privacy–preserving data mining component discovers patterns that, while probabilistic, are accurate enough to be useful for many LBSes.

     

    To eliminate any uncertainty in the mining results, Chapter 9 proposes a system for collecting exact trajectories of moving objects in a privacy–preserving manner. In the proposed system there are no trusted components and anonymization is performed by the clients in a P2P network via data cloaking and data swapping. Realistic simulations show that under reasonable conditions and privacy/anonymity settings the proposed system is effective.

  • 8.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Ehsan, Saqib
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Developing a Benchmark for Using Trajectories of Moving Objects in Traffic Prediction and Management2010Conference paper (Other academic)
  • 9.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Ehsan, Saqib
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    From Trajectories of Moving Objects to Route-Based Traffic Prediction and Management2010In: Proceedings of the Workshop on Movement Pattern Analysis 2010, Zurich, Switzerland, September 14, 2010. / [ed] Björn Gottfried and Patrick Laube and Alexander Klippel andNico Van de Weghe and Roland Billen, CEUR , 2010, p. 132-135Conference paper (Refereed)
  • 10.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Ehsan, Saqib
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Using Trajectories of Moving Objects in Traffic Prediction and Management2010Conference paper (Refereed)
  • 11.
    Gidofalvi, Gyözö
    et al.
    Uppsala University, Department of Computer Science.
    Herenyi, Gergely
    Bach Pedersen, Torben
    Instant Social Ride-Sharing2008In: Proceedings of the Fifteenth World Congress on Intelligent Transport Systems, Nov 16-20, 2008, New York, NY, USA, Intelligent Transportation Society of America , 2008, p. 8-Conference paper (Refereed)
    Abstract [en]

    This paper explores the use of ride–sharing as a resource-efficient mode of personal transportation. While the perceived benefits of ride–sharing include reduced travel times, transportation costs, congestion, and carbon emissions, its wide–spread adoption is hindered by a number of barriers. These include the scheduling and coordination of routes, safety risks, social discomfort in sharing private spaces, and an imbalance of costs and benefits among parties. To address these barriers, the authors describe a system for ride–sharing that utilizing the concepts of social networks and social interest groups. The Social Ride/Sharing Service (SRSS) automatically groups ride offers and requests into ride–shares according to two objectives: to minimize ride–share detours, and to maximize the amount of social connections amongst participants of ride–shares. A mobile application places the SRSS at the fingertips of mobile users (anyplace and anytime), Minimizing detours and maximizing social connections in ride–shares is used to increase the social comfort level and trust among ride-share participants, ultimately leading to increased user acceptance and adoption of ride–sharing. As the number of users increases, the number of social connections between users increases, which allows more ride–share opportunities and more effective transport. The authors present realistic, city–wide simulations for Copenhagen that demonstrate that their proposed social ride–sharing system is viable and effective.

  • 12.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics .
    Huang, Xuegang
    Aalborg University, Department of Computer Science.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Privacy-Preserving Data Mining on Moving Object Trajectories2007Report (Other academic)
    Abstract [en]

    The popularity of embedded positioning technologies in mobile devices and the development of mobile communication technology have paved the way for powerful location-based services (LBSs). To make LBSs useful and user–friendly, heavy use is made of context information, including patterns in user location data which are extracted by data mining methods. However, there is a potential conflict of interest: the data mining methods want as precise data as possible, while the users want to protect their privacy by not disclosing their exact movements. This paper aims to resolve this conflict by proposing a general framework that allows user location data to be anonymized, thus preserving privacy, while still allowing interesting patterns to be discovered. The framework allows users to specify individual desired levels of privacy that the data collection and mining system will then meet. Privacy-preserving methods are proposed for two core data mining tasks, namely finding dense spatio–temporal regions and finding frequent routes. An extensive set of experiments evaluate the methods, comparing them to their non-privacy-preserving equivalents. The experiments show that the framework still allows most patterns to be found, even when privacy is preserved.

  • 13.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center of Geoinformatics.
    Huang, Xuegang
    Aalborg University, Department of Computer Science.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Privacy-Preserving Data Mining on Moving Object Trajectories2007In: 2007 International Conference on Mobile Data Management / [ed] Christian Becker and Christian S. Jensen and Jianwen Su and Daniela Nicklas, IEEE conference proceedings, 2007, p. 60-68Conference paper (Refereed)
    Abstract [en]

    The popularity of embedded positioning technologies in mobile devices and the development of mobile communication technology have paved the way for powerful location-based services (LBSs). To make LBSs useful and user–friendly, heavy use is made of context information, including patterns in user location data which are extracted by data mining methods. However, there is a potential conflict of interest: the data mining methods want as precise data as possible, while the users want to protect their privacy by not disclosing their exact movements. This paper aims to resolve this conflict by proposing a general framework that allows user location data to be anonymized, thus preserving privacy, while still allowing interesting patterns to be discovered. The framework allows users to specify individual desired levels of privacy that the data collection and mining system will then meet. Privacy-preserving methods are proposed for a core data mining task, namely finding dense spatio–temporal regions. An extensive set of experiments evaluate the methods, comparing them to their non-privacy-preserving equivalents. The experiments show that the framework still allows most patterns to be found, even when privacy is preserved.

  • 14.
    Gidofalvi, Gyözö
    et al.
    Uppsala University, Department of Information Technology.
    Huang, Xuegang
    Aalborg University, Department of Computer Science.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Privacy-preserving trajectory collection2008In: GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems / [ed] Walid G. Aref and Mohamed F. Mokbel and Markus Schneider, ACM Press, 2008, p. 387-390Conference paper (Refereed)
    Abstract [en]

    In order to provide context–aware Location–Based Services, real location data of mobile users must be collected and analyzed by spatio–temporal data mining methods. However, the data mining methods need precise location data, while the mobile users want to protect their location privacy. To remedy this situation, this paper first formally defines novel location privacy requirements. Then, it briefly presents a system for privacy–preserving trajectory collection that meets these requirements. The system is composed of an untrusted server and clients communicating in a P2P network. Location data is anonymized in the system using data cloaking and data swapping techniques. Finally, the paper empirically demonstrates that the proposed system is effective and feasible.

  • 15.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
    Huang, Xuegang
    Aalborg University, Department of Computer Science.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Probabilistic Grid-Based Approaches for Privacy-Preserving Data Mining on Moving Object Trajectories2010In: Privacy-Aware Knowledge Discovery : Novel Applications and New Techniques / [ed] Francesco Bonchi and Elena Ferrari, CRC Press, 2010, 1, p. 183-210Chapter in book (Refereed)
    Abstract [en]

    The efficient management of moving object databases has gained much interest in recent years due to the development of mobile communication and positioning technologies. A typical way of representing moving objects is to use the trajectories. Much work in the database community has focused on the topics of indexing, query processing and data mining of moving object trajectories, but little attention has been paid to the preservation of privacy in this setting.

     

    In many applications such as intelligent transport systems (ITS) and fleet management, floating car data (FCD), i.e., tracked vehicle locations, are collected, and used for mining traffic patterns. For instance, by mining vehicle trajectories in urban transportation networks over time one can easily identify dense areas (roads, junctions, etc.), and use this knowledge to predict traffic congestion. By data mining the periodic movement patterns (objects follow similar routes at similar times) of individual drivers, personalized, context–aware services can be delivered. However, exposing location / trajectory data of moving objects to application servers can cause threats to the location privacy of individual users. For example, a service provider with access to trajectory data can study a user’s personal habits. The naïve approach of keeping the user’s identity a secret by hiding / encoding the user’s ID does not work: Frequent user locations, such as the home and office addresses can be found by first self–correlating the user’s trajectory, and then cross–referencing the frequent locations with publicly available spatial data sources, e.g., Yellow Pages, thereby revealing the user’s identity.

     

    In recent years, the study of privacy–preserving data mining has appeared due to the advances in data collection and dissemination technologies which force existing data mining algorithms to be reconsidered from the point of view of privacy protection. Various privacy concepts and measures, such as kanonymity and ldiversity, and related privacy–preservation techniques, such as perturbation, condensation, generalization and data hiding with conceptual reconstruction have been proposed in the general setting. However, their extension or applicability to the spatio–temporal domain, in particular the privacy–preserving data mining of moving object trajectories has not been investigated. Hence the chapter is focused on addressing the unique challenge of obtaining detailed, accurate patterns from anonymized location and trajectory data.

     

    After a thorough status report on research works related to the issue of privacy–preserving data mining on moving object trajectories, first, the chapter proposes a novel anonymization model for preservation of location privacy on moving object trajectories. In this model, users specify their requirements of location privacy, based on the notions of anonymization rectangles and location probabilities, intuitively saying how precisely they want to be located in given areas. Second, the chapter shows a common problem with existing methods that are based on the notion of k–anonymity. This problem allows an adversary to infer a frequently occurring location of a user, e.g., the home address, by correlating several observations. Third, the chapter presents an effective grid–based framework for data collection and mining over the anonymized trajectory data. The framework is based on the notions of anonymization grids and anonymization partitionings which allow effective management of both the user–specified location privacy requirements and the anonymized trajectory data. Along with the framework, three policies for constructing anonymization rectangles, called common regular partitioning, individual regular partitioning, and individual irregular partitioning are presented. All three policies avoid the aforementioned privacy problems of existing methods. Fourth, the chapter presents a client–server architecture for an efficient implementation of the system. A distinguishing feature of the architecture is that anonymization is performed solely on the client, thus removing the need for trusted middleware. Fifth, the chapter presents techniques for solving two basic trajectory data mining operation, namely finding dense spatio–temporal areas and finding frequent routes. The techniques are based on probabilistic counting. Finally, extensive experiments with prototype implementations show the effectiveness of the approach, by comparing the presented solutions to their non–privacy–preserving equivalents. The experiments show that the framework still allows most patterns to be found, even when privacy is preserved.

  • 16.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Kaul, Manohar
    Aarhus University, Department of Computer Science.
    Borgelt, Christian
    European Centre for Soft Computing, Intelligent Data Analysis and Graphical Models Research Unit.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Frequent route based continuous moving object location- and density prediction on road networks2011In: GIS '11 Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems / [ed] Isabel F. Cruz and Divyakant Agrawal and Christian S. Jensen and Eyal Ofek and Egemen Tanin, ACM Press, 2011, p. 381-384Conference paper (Refereed)
    Abstract [en]

    Emerging trends in urban mobility have accelerated the need for effective traffic prediction and management systems. The present paper proposes a novel approach to using continuously streaming moving object trajectories for traffic prediction and management. The approach continuously performs three functions for streams of moving object positions in road networks: 1) management of current evolving trajectories, 2) incremental mining of closed frequent routes, and 3) prediction of near-future locations and densities based on 1) and 2). The approach is empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.

  • 17.
    Gidofalvi, Gyözö
    et al.
    Geomatics ApS - Center for Geoinformatics.
    Larsen, Hans Ravnkjær
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Estimating the Capacity of the Location: Based Advertising Channel2007In: Conference Proceedings - 6th International Conference on the Management of Mobile Business, ICMB 2007, IEEE Computer Society, 2007, p. 4278546-Conference paper (Refereed)
    Abstract [en]

    Delivering “relevant” advertisements to consumers carrying mobile devices is regarded by many as one of the most promising mobile business opportunities. The relevance of a mobile ad depends on at least two factors: (1) the proximity of the mobile consumer to the product or service being advertised, and (2) the match between the product or service and the interest of the mobile consumer. The interest of the mobile consumer can be either explicit (expressed by the mobile consumer) or implicit (inferred from user characteristics). This paper tries to empirically estimate the capacity of the mobile advertising channel, i.e., the number of relevant ads that can be delivered to mobile consumers. The estimations are based on a simulated mobile consumer population and simulated mobile ads. Both of the simulated data sets are realistic and derived based on real world data sources about population geo–demographics, businesses offering products or services, and related consumer surveys. The estimations take into consideration both the proximity and interest requirements of mobile ads, i.e., ads are only delivered to mobile consumers that are close-by and are interested, where interest is either explicit or implicit. Results show that the capacity of the LBA channel is rather large, which is evidence for a strong business case, but also indicate the need for user–control of the received mobile ads.

  • 18.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics.
    Larsen, Hans Ravnkjær
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Estimating the capacity of the Location-Based Advertising channel2008In: International Journal of Mobile Communications, ISSN 1470-949X, E-ISSN 1741-5217, Vol. 6, no 3, p. 357-375Article in journal (Refereed)
    Abstract [en]

    Delivering ‘relevant’ advertisements to consumers carrying mobile devices is regarded by many as one of the most promising mobile business opportunities. The relevance of a mobile ad depends on at least two factors: (1) the proximity of the mobile consumer to the product or service being advertised, and (2) the match between the product or service and the interest of the mobile consumer. The interest of the mobile consumer can be either explicit (expressed by the mobile consumer) or implicit (inferred from user characteristics). This paper tries to empirically estimate the capacity of the Mobile Advertising channel, i.e. the number of relevant ads that can be delivered to mobile consumers. The estimations are based on a simulated mobile consumer population and simulated mobile ads. Both of the simulated data sets are realistic and derived based on real-world data sources about population geo-demographics, businesses offering products or services, and related consumer surveys. The estimations take into consideration both the proximity and interest requirements of mobile ads, i.e. ads are delivered only to mobile consumers that are close-by and are interested, where interest is either explicit or implicit. Results show that the capacity of the Location-Based Advertising channel is rather large, which is evidence for a strong business case, but it also indicates the need for user-control of the received mobile ads.

  • 19.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatik och Geodesi.
    Moran, Carlos
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Estimating Traffic Performance in Road Networks from Anonymized GPS Vehicle Probes2010In: Proceedings of the Workshop on Movement Research: Are you in the flow?: The 13th AGILE International Conference on Geographic Information Science, 10-14 May 2010, Guimarães, Portugal, 2010, p. 2-Conference paper (Other academic)
  • 20.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Cab-sharing: An Effective, Door-to-Door, On-Demand Transportation Service2007In: Proceedings of the 6th European Congress on Intelligent Transport Systems and Services, 18-20 Jun, 2007, Aalborg, Denmark, ERTICO , 2007, p. 8-Conference paper (Refereed)
    Abstract [en]

    City transportation is an increasing problem. Public transportation is cost effective, but do not provide door-to-door transportation. This makes the far more expensive cabs attractive and scarce. This paper proposes a location–based Cab–Sharing Service (CSS), which reduces cab fare costs and effectively utilizes available cabs. The CSS accepts cab requests from mobile devices in the form of origin–destination pairs. Then it automatically groups closeby requests to minimize the cost, utilize cab space, and service cab requests in a timely manner. Simulation–based experiments showthat the CSS can group cab requests in a way that effectively utilizes resources and achieves significant savings, making cab–sharing a new, promising mode of transportation.

  • 21.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Mining Long, Sharable Patterns in Trajectories of Moving Objects2006In: Proceedings of the Third Workshop on Spatio-Temporal Database Management, STDBM 06, September 11, 2006, Seoul, South Korea / [ed] Christophe Claramunt and Ki-Joune Li and Simonas Šaltenis, CEUR , 2006, p. 49-58Conference paper (Refereed)
    Abstract [en]

    The efficient analysis of spatio–temporal data, generated by moving objects, is an es-sential requirement for intelligent locationbased services. Spatio-temporal rules can befound by constructing spatio–temporal baskets, from which traditional association rulemining methods can discover spatio–temporal rules. When the items in the baskets arespatio–temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable requent routes.The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of subroutes of long routes. A SQL–based implementation is described, and experiments on real life data show the effectiveness of the method.

  • 22.
    Gidofalvi, Gyözö
    et al.
    Uppsala University, Department of Information Technology.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Mining Long, Sharable Patterns in Trajectories of Moving Objects2009In: Geoinformatica, ISSN 1384-6175, E-ISSN 1573-7624, Vol. 13, no 1, p. 27-55Article in journal (Refereed)
    Abstract [en]

    The efficient analysis of spatio-temporal data, generated by moving objects, is an essential requirement for intelligent location-based services. Spatio-temporal rules can be found by constructing spatio-temporal baskets, from which traditional association rule mining methods can discover spatio-temporal rules. When the items in the baskets are spatio-temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable frequent routes. The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of sub-routes of long routes. Considering alternative modelling options for trajectories, leads to the development of two effective variants of the method. SQL-based implementations are described, and extensive experiments on both real life- and large-scale synthetic data show the effectiveness of the method and its variants.

  • 23.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Spatio-temporal Rule Mining: Issues and Techniques2005In: DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS / [ed] A. Min Tjoa and Juan Trujillo, Springer, 2005, Vol. 3589, p. 275-284Conference paper (Refereed)
    Abstract [en]

    Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location–Based Services (LBS). To achieve high quality for such services, spatio–temporal data mining techniques are needed. In this paper, we describe experiences with spatio–temporal rule mining in a Danish data mining company. First, a number of real world spatio–temporal data sets are described, leading to a taxonomy of spatio–temporal data. Second, the paper describes a general methodology that transforms the spatio–temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio–temporal rules for LBS. Finally, unique issues in spatio–temporal rule mining are identified and discussed.

  • 24.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    ST-ACTS: a spatio-temporal activity simulator2006In: GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems / [ed] Rolf A. de By and Silvia Nittel, ACM Press, 2006, p. 155-162Conference paper (Refereed)
    Abstract [en]

    Creating complex spatio–temporal simulation models is a hot issue in the area of spatio–temporal databases [7]. While existing Moving Object Simulators (MOSs) address different physical aspects of mobility, they neglect the important social and geo–demographical aspects of it. This paper presents ST–ACTS, a Spatio–Temporal ACTivity Simulator that, using various geo–statistical data sources and intuitive principles, models the so far neglected aspects. ST–ACTS considers that (1) objects (representing mobile users) move from one spatio–temporal location to another with the objective of performing a certain activity at the latter location; (2) not all users are equally likely to perform a given activity; (3) certain activities are performed at certain locations and times; and (4) activities exhibit regularities that can be specific to a single user or to groups of users. Experimental results show that ST-ACTS is able to effectively generate realistic spatio–temporal distributions of activities, which make it essential for the development of adequate spatio–temporal data management and data mining techniques.

  • 25.
    Gidofalvi, Gyözö
    et al.
    Geomatic ApS - Center for Geoinformatics.
    Pedersen, Torben Bach
    Aalborg University, Department of Computer Science.
    Risch, Tore
    Uppsala University, Department of Information Technology.
    Zeitler, Erik
    Uppsala University, Department of Information Technology.
    Highly scalable trip grouping for large-scale collective transportation systems2008In: Advances in Database Technology - EDBT 2008 - 11th International Conference on Extending Database Technology, Proceedings / [ed] Alfons Kemper and Patrick Valduriez and Noureddine Mouaddib and Jens Teubner and Mokrane Bouzeghoub and Volker Markl and Laurent Amsaleg and Ioana Manolescu, ACM Press, 2008, Vol. 261, p. 678-689Conference paper (Refereed)
    Abstract [en]

    Transportation–related problems, like road congestion, parking, and pollution, are increasing in most cities. In order to reduce traffic, recent work has proposed methods for vehicle sharing, for example for sharing cabs by grouping “closeby” cab requests and thus minimizing transportation cost and utilizing cab space. However, the methods published so far do not scale to large data volumes, which is necessary to facilitate large–scale collective transportation systems, e.g., ride–sharing systems for large cities.

    This paper presents highly scalable trip grouping algorithms, which generalize previous techniques and support input rates that can be orders of magnitude larger. The following three contributions make the grouping algorithms scalable. First, the basic grouping algorithm is expressed as a continuous stream query in a data stream management system to allow for a very large flow of requests. Second, following the divide–and–conquer paradigm, four space–partitioning policies for dividing the input data stream into sub–streams are developed and implemented using continuous stream queries. Third, using the partitioning policies, parallel implementations of the grouping algorithm in a parallel computing environment are described. Extensive experimental results show that the parallel implementation using simple adaptive partitioning methods can achieve speed–ups of several orders of magnitude without significantly degrading the quality of the grouping.

  • 26.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Scalable Detection of Traffic Congestion from Massive Floating Car Data Streams2015Conference paper (Refereed)
    Abstract [en]

    Motivated by the high utility and growing availability of Floating Car Data (FCD) streams for traffic congestion modeling and subsequent traffic congestion-related intelligent traffic management tasks, this paper proposes a grid-based, time-inhomogeneous model and method for the detection of congestion from large FCD streams. Furthermore, the paper proposes a simple but effective, high-level implementation of the method using off-the-shelf relational database technology that can readily be ported to Big Data processing frameworks. Empirical evaluations on millions of real-world taxi trajectories show that 1) the spatio-temporal distribution and clustering of the detected congestions are reasonable and 2) the method and its prototype implementation scale linearly with the input size and the geographical level of detail / spatio-temporal resolution of the model.

  • 27.
    Gidófalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Fang, Dong
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    When And Where Next: Individual Mobility Prediction2012Conference paper (Refereed)
    Abstract [en]

    The ability to predict when an individual mobile user will leave his current location and where we will move next enables a myriad of qualitatively different Location-Based Services (LBSes) and applications. To this extent, the present paper proposes a statistical method that explicitly performs these related temporal and spatial prediction tasks in three continuous, sequential phases. In the First phase, the method continuously extracts grid-based staytime statistics from the GPS coordinate stream of the location-aware mobile device of the user. In the second phase, from the grid-based staytime statistics, the method periodically extracts and manages regions that the user frequently visits. Finally, in the third phase, from the stream of region-visits, the method continuously estimates parameters for an inhomogeneous continuous-time Markov model and in a continuous fashion predicts when the user will leave his current region and where he will move next. Empirical evaluations, using a number of long, real world trajectories from the Geo-Life data set, show that the proposed method outperforms a state-of-the-art, rule-based trajectory predictor both in terms of temporal and spatial prediction accuracy.

  • 28.
    Palmberg, Robin C. O.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Schwertner, Emilia
    Karolinska Institutet, Department of Neurobiology, Division of Clinical Geriatrics, Center for Alzheimer Research, Care Sciences and Society.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Religa, Dorota
    Karolinska Institutet, Department of Neurobiology, Division of Clinical Geriatrics, Center for Alzheimer Research, Care Sciences and Society.
    Susilo, Yusak O.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Using Smart Technologies to Understand Travellers Who have Dementia: Potentials and ChallengesManuscript (preprint) (Other academic)
    Abstract [en]

    Age-related cognitive diseases are becoming a growing problem in Sweden. With the fast ageing population and lowered mortality rate comes the spread of cognitive diseases related to dementia. In order to accommodate this growing target group in transport and the built environment, it is crucial to understand the mobility and travel behaviour of patients suffering from these diseases.

    However, the adopted techniques to uncover travel behaviour of today do not allow for errors caused by cognitive impairment, since they require retrospective validation. Such design choices make it hard to understand how to improve the environment to accommodate the target group.  Recently, technologies have emerged that allow for new design methods which can be beneficial for the said target group. This paper aims to address the issue of how to collect and analyse data regarding the mobility of the target group, and roles of the built environment in affecting their behaviour. A literature review has been conducted to 1) uncover the state of the art of the technologies and design methods that relate to automated data collection about the travel behaviour, 2) understand the limits of the user related to software interaction and, in turn, data collection and 3) find possibilities for new solutions to collect travel data from patients who have dementia.

  • 29.
    Palmberg, Robin C. O.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Susilo, Yusak O.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Developing and Trialling an Implicit Interaction Platform to Monitor Elderly TravellersManuscript (preprint) (Other academic)
    Abstract [en]

    As the population grows older, age-induced illnesses related to cognitive impairments arise. Little is known regarding what and how the built environment affects that target group. It is theorized that external factors in the built environment might play a part in elderlies getting lost because of conditions related to illnesses such as dementia. To accommodate the target group in a future society, it is crucial to understand any possible correlation between locations and psychophysiological conditions.

    Technological advancements of wearable devices allow for the creation of software that collects data relevant to location as well as biometric data automatically, without affecting the user. By utilising consumer-grade hardware, it is possible to scale up the studies that the software allows for indefinitely.

    This paper covers the development of such a tool, by detailing what has become possible because of previous advancements in research regarding automatic travel diaries and the recognition of psychophysiological conditions through biometric data collection. Initial testing shows, that while data can be collected as proposed, there are drawbacks in terms of run time due to the battery capacity of wearable devices. More data is required to indicate whether the data collected can be used for correlation and causality analysis.

  • 30.
    Palmberg, Robin C. O.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Susilo, Yusak O.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Naqavi, Fatemeh
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Built Environment Characteristics, Daily Travel, and Biometric Readings: Creation of an Experimental Tool based on a Smartwatch PlatformManuscript (preprint) (Other academic)
    Abstract [en]

    The utilisation of travel surveys can uncover layers of information regarding travel behaviour, travel needs, and more. The collected information is utilised to make strategic planning choices when reorganising or planning new built environments. Over the years, the methods for conducting travel surveys have changed from manual interviews and paper forms to automated travel diaries which are monitoring the trips made by the survey participants. With the fast progression of technological advancements, new possibilities for operationalising said types of automated travel diaries can be changed from utilising mobile devices to wearable devices. Wearable devices are often equipped with sensors which can collect continuous biometric data from sources which are not reachable from standard mobile devices such as smartphones. The biometric data that can be collected through wearable devices ranging from heart rate and blood pressure to temperature and perspiration, given the proper sensors. This advancement opens for new possible layers of information in the collection of travel data. Such biometric data can be used to derive psychophysiological conditions related to cognitive load, which can uncover more in-depth knowledge regarding stress and emotions, given the right variables and sample rate. This paper aims to explore the possibilities in terms of data analysis on a data set collected through a software combining traditional travel survey data, such as position and time, with biometric data, in this case; heartrate, to gain knowledge of the implications of such collected data. The knowledge about the implications of spatial configurations can be used in the planning phase of new areas, in order to create more accessible environments, as the information could be used to make neutral, or even encouraging, environments for travellers.

  • 31.
    Palmberg, Robin
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Enabling Technologies to Serve the Ageing Urban Society Better (ENTRUST)2018Conference paper (Other academic)
    Abstract [en]

    The life span of the inhabitants of Sweden is increasing and with this comes age related cognitive diseases such as those related to dementia. Our society is not prepared to accommodate for the needs of the people who are affected by this.

    The diseases related to dementia often affect the person’s ability to localize themselves and to remember previous and upcoming events. A common issue that occurs is a state called “elopement”.

  • 32.
    Palmberg, Robin
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Developing and trialling an implicit interaction platform to monitor and aiding dementia travellers2019Conference paper (Other academic)
    Abstract [en]

    Age related cognitive diseases are becoming a growing problem in Sweden. With the fast ageing population and lowered mortality rate comes the spread of cognitive diseases related to dementia. In order to accommodate this growing target group in transport and the built environment, it is important to understand the mobility and travel behaviour of patients suffering from these diseases. One subset of this target group is travellers suffering from age induced illnesses related with dementia, which most often have fluctuating symptoms that are affecting the cognitive skills of the traveller. This makes it hard to use standardized forms and survey-based information that would require the traveller to actively respond retroactively, either in oral or written form, since the traveller might have forgotten or mixed up their past experiences, among other things, it becomes very hard to gain confidence in the results as it might be hard to tell in which condition the patient is during the collection.

    We propose an automated collection of biometric data such as heart rate in combination with position. Since the validity of the information collected in this manner is directly related to the quality of the sensors used it means that the precision and accuracy of the results could be virtually endlessly improved by upgrading the hardware and optimizing the software. To take a first step towards a solution like this we have started developing a smart watch application which is utilizing PPG technology to collect heart rate and combine it with positions collected through GPS technology.

    Early testing has shown the possibility to correlate the heart rate of a traveller to their specific location. The implications of this must be validated through data labelling as we wish to utilize machine learning algorithms to analyse the data collected.

  • 33.
    Palmberg, Robin
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Uncovering Effects of Spatial and Transportation Elements on Travellers Using Biometric Data2019In: TOWARDS HUMAN SCALE CITIES - OPEN AND HAPPY / [ed] Tuuli Toivonen, Karst Geurs, Elias Willberg, Helsinki: Department of Geosciences and Geography, University of Helsinki , 2019Conference paper (Refereed)
    Abstract [en]

    Travel surveys has been used for decades to observe the patterns, locations, and choices, which travellers chose and do during the given observed period. This information can be utilized as background for informed planning decisions. Despite the progress in the travel survey technologies, the applications mostly focus on more traditional travel parameters. With programmable smart watches now, we can also collect real time data that is not solely pertaining to position and travel mode choices, but also to users’ biometric data. Such an application would open another level of possibilities in dynamically integrating land use and transport planning with public health research.

    Utilising a smart watch platform, we are aiming to develop a tool that will collect biometric data, in combination with spatial context, such as position, spatial features and objects in the built environment, and by utilizing machine learning algorithms, try to detect how travellers are affected by their choice of transport mode, the built environment in general as well as how the public transport is operated.

    Early testing reveals the possibility to find correlations between heart rate and position, which in turn could reveal the effect of spatial and transportation elements on the traveller. By targeting widely available hardware, the scalability for this tool is virtually endless, making it possible to collect large amounts of data and utilizing machine learning algorithms to analyse it.

  • 34.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Mobility Collector: Battery Conscious Mobile Tracking2013In: Mobile Ghent 2013, 2013Conference paper (Refereed)
    Abstract [en]

    Tracking and analyzing the location of users to understand, to predict (and ultimately control) the movement of humans (or animals) has been an important part of research in different groups such as human geographers, urban planers, behavioral scientists or movement ecologists. Despite the availability of tracking technology, the above research activities have been limited by: 1)the spatial granularity of tracking data, 2) the willingness of users to share their private 3) the fact that a tracking mobile application drains a user's battery, and last but not least 4) the absence of a generic, configurable, open-source trajectory collector and annotator. Most studies that exceeded this barrier restrict the collection to settings where an obvious “unlimited” power source is available (i.e., taxi cabs and cars). Thus, to combat the aforementioned limitations, this paper describes the features and design of the Mobility Collector, a configurable, open-source, battery conscious Android mobile tracking application and provides a prototype implementation that works uniformly across multiple hardware devices and Android OS versions.According to the official Android developer's web page [weblink], two main parameters are considered when requesting location updates: minTime, which controls the location update interval and minDistance, which is the minimum distance between location updates. The intended advantage of the method, i.e., battery preserving equitime location sampling, is linked to a degradation of spatial data quality. This approach is relevant for the majority of mapping-oriented applications, which require data that is equally distributed in time, but, in the case of tracking services, an implementation that focuses mostly on equidistance sampling can be vital in order to accurately determine and infer activities while being aware of the user's context.The Mobility Collector provides high quality data in a battery conscious manner. On one hand, the custom implementation of the Location Manager class using a linear movement model based on the recent samples, which duty-cycles the parameters dynamically, allows the data to have a high spatial granularity, making it suitable for different tracking settings (Figure 1, Table 1). On the other hand, the battery life is considerably extended by using a motion-enabled alarm, which switches the servicethat gets location updates on and off, thus allowing for any Android phone to be used for data collection without compromising its usability (Figure 2). While using the Mobility Collector, the usability of a phone is approximately 75% of the daily basis usage plan.The Mobility Collector was designed specifically for research purposes and it offers a high degree of extensibility and usability. First, the source-code will be provided for customization and the platform can be configured either as a standalone client application or as part of a client-server architecture. Second, it provides a configurable user-friendly interface for point- and period-based trajectory annotation. Finally, while the configuration can be done manually by modifying the source-code, a web client that takes configuration-specific parameters (i.e., equitime vs. equidistance sampling, sampling frequency, annotations, etc.) and produces a version of the application according to specific needs is available.

  • 35.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    MEILI: A travel diary collection, annotation and automation system2018In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 70, no July 2018, p. 24-34Article in journal (Refereed)
    Abstract [en]

    The increased interest in the automation of travel diary collection, together with the ease of access to new artificial intelligence methods led scientists to explore the prerequisites to the automatic generation of travel diaries. One of the most promising methods for this automation relies on collecting GPS traces of multiple users over a period of time, followed by asking the users to annotate their collected data by specifying the base entities for a travel diary, i.e., trips and triplegs. This led scientist on one of two paths: either develop an in-house solution for data collection and annotation, which is usually an undocumented prototype implementation limited to few users, or contract an external provider for the development, which results in additional costs. This paper provides a third path: an open-source highly modular system for the collection and annotation of travel diaries of multiple users, named MEILI. The paper discusses the architecture of MEILI with an emphasis on the data model, which allows scientists to implement and evaluate their methods of choice for the detection of the following entities: trip start/end, trip destination, trip purpose, tripleg start/end, and tripleg mode. Furthermore, the open source nature of MEILI allows scientists to modify the MEILI solution in compliance with their legal and ethical specifications. MEILI was successfully trialed in multiple case studies in Stockholm and Gothenburg, Sweden between 2014 and 2017.

    The full text will be freely available from 2020-02-04 13:09
  • 36.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Transportation mode detection – an in-depth review of applicability and reliability2016In: Transport reviews, ISSN 0144-1647, E-ISSN 1464-5327Article in journal (Refereed)
    Abstract [en]

    The wide adoption of location-enabled devices, together with the acceptance of services that leverage (personal) data as payment, allows scientists to push through some of the previous barriers imposed by data insufficiency, ethics and privacy skepticism. The research problems whose study require hard-to-obtain data (e.g. transportation mode detection, service contextualisation, etc.) have now become more accessible to scientists because of the availability of data collecting outlets. One such problem is the detection of a user's transportation mode. Different fields have approached the problem of transportation mode detection with different aims: Location-Based Services (LBS) is a field that focuses on understanding the transportation mode in real-time, Transportation Science is a field that focuses on measuring the daily travel patterns of individuals or groups of individuals, and Human Geography is a field that focuses on enriching a trajectory by adding domain-specific semantics. While different fields providing solutions to the same problem could be viewed as a positive outcome, it is difficult to compare these solutions because the reported performance indicators depend on the type of approach and its aim (e.g. the real-time availability of LBS requires the performance to be computed on each classified location). The contributions of this paper are three fold. First, the paper reviews the critical aspects desired by each research field when providing solutions to the transportation mode detection problem. Second, it proposes three dimensions that separate three branches of science based on their main interest. Finally, it identifies important gaps in research and future directions, that is, proposing: widely accepted error measures meaningful for all disciplines, methods robust to new data sets and a benchmark data set for performance validation.

  • 37.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Susilo, Yusak Octavius
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
    Longest common subsequences: Identifying the stability of individuals’ travel patternsManuscript (preprint) (Other academic)
    Abstract [en]

    There is a strong consensus in the travel behaviour research community that the one day travel diary collection is insufficient to understand the finer aspects of behaviour that transcend attributes such as average trip length, duration, travel modes, etc. While a large body research was done on exploring the spatial, temporal and spatio-temporal travel behavioural patterns, the sequential aspect of behaviour is seldom studied. The consensus of the few papers that have studied travel behaviour variability from a sequential perspective has been to use edit distance and compute the costs of transforming one day of travel activities into another. While useful, this approach generates difficult to understand metrics since it does not directly extract (sub)sequences but computes penalties. This paper provides an alternative for investigating the sequential aspect of travel behaviour that makes use of longest common subsequences to extract the activities that are common to multiple days and / or users. The proposed methodology provides indexes for measuring the inter- and intra-personal stability of a given user base and its usefulness is proved in a case study on travel diaries collected from 51 users for a period of 7 days.

  • 38.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Susilo, Yusak Octavius
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
    Measures of transport mode segmentation of trajectories2016In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 30, no 9, p. 1763-1784Article in journal (Refereed)
    Abstract [en]

    Rooted in the philosophy of point- and segment-based approaches for transportation mode segmentation of trajectories, the measures that researchers have adopted to evaluate the quality of the results (1) are incomparable across approaches, hence slowing the progress in the field and (2) do not provide insight about the quality of the continuous transportation mode segmentation. To address these problems, this paper proposes new error measures that can be applied to measure how well a continuous transportation mode segmentation model performs. The error measures introduced are based on aligning multiple inferred continuous intervals to ground truth intervals, and measure the cardinality of the alignment and the spatial and temporal discrepancy between the corresponding aligned segments. The utility of this new way of computing errors is shown by evaluating the segmentation of three generic transportation mode segmentation approaches (implicit, explicit–holistic, and explicit–consensus-based transport mode segmentation), which can be implemented in a thick client architecture. Empirical evaluations on a large real-word data set reveal the superiority of explicit–consensus-based transport mode segmentation, which can be attributed to the explicit modeling of segments and transitions, which allows for a meaningful decomposition of the complex learning task.

  • 39.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Susilo, Yusak Octavius
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
    Mobility Collector2014In: Journal of Location Based Services, ISSN 1748-9725, Vol. 8, no 4, p. 229-255Article in journal (Refereed)
    Abstract [en]

    Despite the availability of mobile positioning technologies and scientists' interests in tracking, modelling and predicting the movements of individuals and populations, these technologies are seldom efficiently used. The continuous changes in mobile positioning and other sensor technologies overburden scientists who are interested in data collection with the task of developing, implementing and testing tracking algorithms and their efficiency in terms of battery consumption. To this extent, this article proposes an adaptive, battery conscious tracking algorithm that collects trajectory data fused with accelerometer data and presents Mobility Collector, which is a prototype platform that, using the tracking algorithm, can produce highly configurable, off-the-shelf, multi-user tracking systems suitable for research purposes. The applicability of the tracking system is tested within the transport science domain by collecting labelled movement traces and related motion data, i.e. accelerometer data and derived information (number of steps and other useful movement features based on temporal aggregates of the raw readings) to develop and evaluate a method that automatically classifies the transportation mode of users with a 90.8% prediction accuracy.

  • 40.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Collecting travel diaries: Current state of the art, best practices, and future research directions2018In: Transport Survey Methods in the era of big data: facing the challenges, Elsevier, 2018, Vol. 32, p. 155-166Conference paper (Refereed)
    Abstract [en]

    The amount of useful information that can be extracted from travel diaries is matched by the difficulty of obtaining travel diaries in a modern era where the response rate to traditional travel diary collection methods has seen a decrease in most countries. Prompted by this, a body of research has been dedicated to study how travel diaries can be collected via new methods, namely location enabled devices such as smartphones, that have a higher penetration rate (in terms of device ownerships and user attachment) and are both easier and cheaper to manage compared to traditional data collection method, e.g. paper-and-pencil, phone, or web-based questionnaires. This paper offers an overview of the current state of travel diary collection, a potential future state and a practical checklist for travel diary collection case studies. A thorough discussion on different pros and cons of travel diary collection methods and efforts needed for the convergence of methods to collect travel diaries for all demographics are provided. The practical checklist to aid researchers to organise case studies is based on the authors' experience and it is meant to raise awareness of difficulties that can be encountered while collecting travel surveys with automated and semi-automated systems, and how to overcome them.

  • 41.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Susilo, Yusak Octavius
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    A series of three case studies on the semi-automation of activity travel diary generation using smarpthones2017In: Proceedings of TRB 2017 Annual Meeting, 2017Conference paper (Refereed)
    Abstract [en]

    The growing need of acquiring data that is useful for travel behaviour analysis led scientists topursue new ways of obtaining travel diaries from large groups of people. The most promising al-ternative to traditional (declarative) travel diary collection methods are those that rely on collectingtrajectories from individuals and then extract travel diary semantics from the trajectories. However,most studies report on routines specific to the post-processing of data, and seldom focus on datacollection. Even the few studies that deal explicitly with data collection describe the final state ofthe collection system, but do not go at the lengths that are required to describe the decision thatwere taken to bring the system to its current state. This leads to a considerable amount of work thatis needed for designing collection systems that are often undocumented, which impedes the reuseof the aforementioned systems. In light of the aforementioned problems, this paper presents a series of three case studies behind the continuous development of MEILI, a travel diary collection,annotation and automation system, in an effort to: 1) illustrate the utility of the developed systemto collect travel diaries, 2) identify how MEILI and other semi-automatic travel diaries collectionsystems can be improved, and 3) propose MEILI as an open source system that has the potentialof being improved into a widely available semi-automated travel diary collection system.

  • 42.
    Prelipcean, Adrian Corneliu
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
    Susilo, Yusak Octavius
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Future directions of research for automatic travel diary collection2018In: Proceedings of the 11th International conference on Transport Survey Methods, 2018Conference paper (Refereed)
    Abstract [en]

    The amount of useful information that can be extracted from travel diaries is matched by the difficulty of obtaining travel diariesin a modern era where the response rate to traditional travel diary collection methods has seen a decrease in most countries.Prompted by this, a body of research has been dedicated to study how travel diaries can be collected via new methods, namelylocation enabled devices such as smartphones, that have a higher penetration rate (in terms of device ownerships and userattachment) and are both easier and cheaper to manage compared to traditional data collection method, e.g. paper-and-pencil,phone, or web-based questionnaires. This paper offers an overview of the current state of travel diary collection, a potentialfuture state and a practical checklist for travel diary collection case studies. A thorough discussion on different pros and cons oftravel diary collection methods and efforts needed for the convergence of methods to collect travel diaries for all demographicsare provided. The practical checklist to aid researchers to organise case studies is based on the authors’ experience and it is meantto raise awareness of difficulties that can be encountered while collecting travel surveys with automated and semi-automatedsystems, and how to overcome them.

  • 43.
    Prelipcean, Adrian
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidófalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Susilo, Yusak
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Comparative framework for activity-travel diary collection systems2015In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, IEEE conference proceedings, 2015, p. 251-258Conference paper (Refereed)
    Abstract [en]

    The needs for cheaper and less intrusive ways to collect activity-travel diaries led scientist to pursue new technologies, e.g., positioning technologies like GPS. While a fully, reliable and widely accepted automatic activity-travel diary collection system is yet to be developed, scientists have presented systems that automate parts of an activity-travel diary collection. In the advent of automated systems, it is important to discuss how to analyse the potential of such systems and how to compare different activity-travel diary collection systems. To achieve this objective, this paper introduces a parallel survey design and a comparison framework for collection systems. The framework can be used as a development tool to optimise system design, to report and monitor progress of different system designs, to objectively weigh benefits in decision making, and to automate systematic analysis. In particular, the framework can be used as a comparison tool to reveal the qualitative difference in the data gathered using different collection systems. To achieve this, the framework defines: 1) a number of activity-travel diary measurement entities (trips and triplegs), entity attributes (e.g., trip purpose, origin / destination, etc.), 2) similarity functions between instances of the same entities, and 3) spatial and temporal quality indices to establish a notion of ground truth. The utility of the proposed framework is demonstrated by analysing the results of a trial survey where data is collected via two activity-travel collection systems: a web-based system (PP) and a smartphone-app-based system (MEILI). PP was collected for one day period and MEILI was used for one week period (with one day overlapping). The results show that half of the trips are captured by both systems, while each system roughly captures the same number of trips as the other. The strengths and weaknesses of MEILI are analysed using the framework on the entire week dataset.

  • 44.
    Susilo, Yusak Octavius
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.
    Prelipcean, Adrian Corneliu
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Allström, Andreas
    Sweco.
    Kristoffersson, Ida
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Widell, Jenny
    Sweco.
    Lessons from a trial of MEILI, a smartphone based semi-automatic activity-travel diary collector, in Stockholm city, SwedenManuscript (preprint) (Other academic)
    Abstract [en]

    This paper describes the lessons learned from the trial of MEILI, a smartphone based semi-automatic activity-travel diarycollection system, in Stockholm city, Sweden. The design of the system, together with state-of-the-art improvements of different elements of the tool, are presented before and after the trial to better illustrate the improvements based on the lessons learned. During the trial, both MEILI and a paper-based diary captured about 65% of the total number of detected trips, but only about half of the trips were captured by both systems. The unmatchable trips are partly due to different activity declaration and system specific destination specification, i.e., a verbose specification of address in the paper-and-pencil survey and a point of interest selection / declaration in MEILI. In terms of subjective appreciation, the user experiences vary greatly between the different participants in the pilot. Presumably, this is mainly due to different level of IT-knowledge of the respondents, but also due to the occasionally non-uniform behaviour of the location collection service caused by hardware and / or software difficulties. Based on these inputs, further web and support system improvements have been implemented for future trials.

  • 45.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Fast map matching, an algorithm integrating hidden Markov model with precomputation2017In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, p. 1-24Article in journal (Refereed)
    Abstract [en]

    Wide deployment of global positioning system (GPS) sensors has generated a large amount of data with numerous applications in transportation research. Due to the observation error, a map matching (MM) process is commonly performed to infer a path on a road network from a noisy GPS trajectory. The increasing data volume calls for the design of efficient and scalable MM algorithms. This article presents fast map matching (FMM), an algorithm integrating hidden Markov model with precomputation, and provides an open-source implementation. An upper bounded origin-destination table is precomputed to store all pairs of shortest paths within a certain length in the road network. As a benefit, repeated routing queries known as the bottleneck of MM are replaced with hash table search. Additionally, several degenerate cases and a problem of reverse movement are identified and addressed in FMM. Experiments on a large collection of real-world taxi trip trajectories demonstrate that FMM has achieved a considerable single-processor MM speed of 25,000–45,000 points/second varying with the output mode. Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge. Reverse movement in the result is also effectively reduced by applying a penalty.

  • 46.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Mining and visual exploration of closed contiguous sequential patterns in trajectories2018In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 32, no 7, p. 1282-1303Article in journal (Refereed)
    Abstract [en]

    Large collections of trajectories provide rich insight into movement patterns of the tracked objects. By map matching trajectories to a road network as sequences of road edge IDs, contiguous sequential patterns can be extracted as a certain number of objects traversing a specific path, which provides valuable information in travel demand modeling and transportation planning. Mining and visualization of such patterns still face challenges in efficiency, scalability, and visual cluttering of patterns. To address these challenges, this article firstly proposes a Bidirectional Pruning based Closed Contiguous Sequential pattern Mining (BP-CCSM) algorithm. By employing tree structures to create partitions of input sequences and candidate patterns, closeness can be checked efficiently by comparing nodes in a tree. Secondly, a system called Sequential Pattern Explorer for Trajectories (SPET) is built for spatial and temporal exploration of the mined patterns. Two types of maps are designed where a conventional traffic map gives an overview of the movement patterns and a dynamic offset map presents detailed information according to user-specified filters. Extensive experiments are performed in this article. BP-CCSM is compared with three other state-of-the-art algorithms on two datasets: a small public dataset containing clickstreams from an e-commerce and a large global positioning system dataset with more than 600,000 taxi trip trajectories. The results show that BP-CCSM considerably outperforms three other algorithms in terms of running time and memory consumption. Besides, SPET provides an efficient and convenient way to inspect spatial and temporal variations in closed contiguous sequential patterns from a large number of trajectories.

  • 47.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Gyözö, Gidofalvi
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Interactive Visual Exploration of Most LikelyMovements2016In: Geospatial data in a changing world selected papers of the 19th AGILE Conference on Geographic Information Science, Springer, 2016Conference paper (Refereed)
  • 48.
    Yuan, Zhengwu
    et al.
    Chongqing University of Posts and Telecommunications / College of Computer Science and Technology.
    Jiang, Yanli
    Chongqing University of Posts and Telecommunications / College of Computer Science and Technology.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Geographical and Temporal Similarity Measurement on Location-based Social Networks2013In: Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems / [ed] Chi-Yin Chow and Shashi Shekhar, New York, NY, USA: Association for Computing Machinery (ACM), 2013, p. 30-34Conference paper (Refereed)
    Abstract [en]

    Using "check-in" data gathered from location-based social networks, this paper proposes to measure the similarity of users by considering the geographical and the temporal aspect of their geographical and temporal aspects of their "check-ins". Temporal neighborhood is added to support the time dimension on the basis of the traditional DBSCAN clustering algorithm, which determines the similarity among users at different scales using the classical Vector Space Model (VSM) with vectors composed of the amount of visits in different cluster area. The spatio-temporal similarity of the user behaviors are obtained through overlapping the different weighted user similarity values. The experimental results show that the proposed approach is effective in measuring user similarity in location-based social networks.

  • 49.
    Zhu, Rui
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
    GPS-based Crowd Sourced Intelligent Traffic Information Hub2013In: Proceedings of the 26th International Cartographic Conference / [ed] Manfred F. Buchroithner, ICC International Cartographic Association , 2013, p. 669-670Conference paper (Refereed)
    Abstract [en]

    Congestion is a major problem in most metropolitan areas and given the increasing rate of urbanization it is likely to be an even more serious problem in the rapidly expanding mega cities. Some well know negative effects of congestion include: 1) the economic losses and quality of life degradation that result from the increased and unpredictable travel times, 2) the increased level of carbon footprint that vehicles idling in congestions leave behind, and 3) the increased number of traffic accidents that are direct results of the stress and fatigue of drivers that are stuck in congestion.

    One possible method to combat congestion is provide intelligent traffic management systems that can in a timely manner inform drivers about current or predicted traffic congestion that is relevant to them on their journeys. This this extent, the present paper proposes a scalable, grid-based intelligent traffic information hub that facilitates the manual definition and/or automatic detection of abnormal traffic condition events, e.g., accidents or congestion, and in advance informs drivers about events that will likely be relevant to them on their journey, thereby allowing the divers or their onboard navigation units to alter their paths as needed.

    The proposed system achieves the above described functionality through the following methodology. The system, without loss of generality, adopts a grid-based discretization of space, which by changing the resolution of the grid allows the system to scale in terms of it computation cost and the geographical level of detail of traffic information that it manages. The system derives traffic information from the continuous stream of grid-based position and speed reports that it receives from the vehicles. In particular, the system in an online fashion 1) summarizes Current (grid-based) Traffic Flow Statistics (CTFS), i.e., it records for each grid cell g from each neighboring grid cell n, the mean and standard deviation of the speeds of the vehicles that are currently located in g and have entered g from n; and 2) efficiently incorporates the CTFS into compressed Historical (grid-based) Traffic Flow Statistics (HTFS) using incremental statistics. Simultaneously, using a sliding window model, the system also 1) maintains the Recent (grid-based) Trajectories (RT) of the vehicles; 2) extracts Recent (gridbased) Mobility Statistics (RMS), i.e., it records for each destination grid cell d, for each neighboring grid cell n of g, and for each possible source grid cell s, the number of vehicles that (i) are currently in d, (ii) have entered d from n, and (iii) have a RT that has passed through s; and 3) efficiently incorporates the RMS into compressed Historical (grid-based) Mobility Statistics (HMS) using incremental statistics. To capture the temporal variability in traffic flow and mobility patterns at different scales, the system through temporal domain projections maintains day-of-week and hour-ofday based aggregations of HTFS and RMS. Then, the system classifies a grid cell g to be congested from the direction of a neighboring grid cell n if the current mean speed of vehicles that entered the grid cell g from the direction of n is below the normal according to the temporally relevant HFS. Finally, based on the temporally relevant HMS, the system sends out congestion notifications to vehicles that are likely to be effected in the future part of their journey by these congestions, i.e., the system sends out a congestion notification (g,n) to a vehicle v that is currently located in some grid cell s from which the likelihood of v moving to g through n within the prediction horizon is above a user-defined threshold.

    Extensive empirical evaluations on large sets of realistically simulated trajectories of vehicles illustrate that the above described methodology and its simple SQL-based implementation in a relational database system is scalable and effective. In particular, the execution time of- and the space used by the system scales linearly with the input size (number of concurrently moving vehicles) and the method’s mutually dependent parameters (grid resolution r and RT length l) that jointly define a spatio-temporal resolution. Within the area of a large city (40km by 40km), assuming a 60km/h average vehicle speed, the system, running on a single personal computer, can manage the described congestion detection and one-minute-ahead notification tasks within real-time requirements for 15 thousand and 2.5 million concurrently moving vehicles for spatio-temporal resolutions (r=62.5m, l=17) and (r=4km,l=2), respectively. Finally, the proposed method, for all spatio-temporal resolutions and prediction horizons, significantly outperforms in terms of notification accuracy the grid-based baseline method, which sends non-directional congestion notifications based on the recent linear movement tendencies of vehicles. 

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