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  • 1. Krasemann, Johanna Törnquist
    Greedy algorithm for railway traffic re-scheduling during disturbances: a Swedish case2010In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 4, no 4Article in journal (Refereed)
    Abstract [en]

    The positive trend of increased use of railway transportation in Europe has resulted in an increased sensitivity to and occurrence of traffic disturbances. In addition to the need for extensions of the infrastructure, the need to effectively limit and predict the effects of disturbances becomes apparent. The kernel of the disturbance management problem is to revise the original timetable in line with the new conditions and decide where, when and how trains should overtake or meet to minimise the negative effect of the disturbance. In previous research, the author has designed optimisation-based approach for rescheduling, which seems promising, but for some scenarios it is difficult to find good solutions within seconds. Also, more detailed constraints will have to be included, which makes the problem even more complex and difficult to solve. Therefore the author developed a greedy algorithm that effectively delivers good solutions within the permitted time. To quickly retrieve a feasible solution, the algorithm performs a depth-first search using an evaluation function to prioritise when conflicts arise and then branches according to a set of criteria. A performance analysis of the algorithm was carried out using simulated experiments showing its strengths and weaknesses.

  • 2.
    Mbiydzenyuy, Gideon
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Persson, Jan A.
    Davidsson, Paul
    Exploring synergy relationships between telematic services and functionalities using cluster analysis2015In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 9, no 4, p. 366-374Article in journal (Refereed)
    Abstract [en]

    A method for assessing potential synergies among different sets of transport telematic services (TTSs) is suggested. An Intelligent Transport System enhances transport by delivering one or more TTSs. The ability to deliver multiple TTSs to address a wide range of stakeholder needs is gaining momentum, not only from a marketing perspective but also from a technological perspective. The total cost of TTSs can be reduced if they share functionalities (i.e., sub-services provided by telematic systems). We show how this synergy can be assessed with the help of clustering methods. Knowledge about possible synergies of functionalities is useful in the (re)design and eventual deployment of TTSs, especially when the underlying telematic systems are able to support multiple TTSs. To adapt the clustering method for this purpose, we suggest a mathematical formulation of synergy among functionalities of TTSs. By applying the method to a set of 32 TTSs, we obtain a cluster formation of these TTSs according to their synergy measures. Overall, the results suggest that the joint implementation of TTSs targeted toward some problem domains can lead to significant cost savings, for example, Road User Charging, Infrastructure Repair and Maintenance, and Information on the Transport of extra large goods for the management of road transport infrastructure. © The Institution of Engineering and Technology 2015.

  • 3.
    Mbiydzenyuy, Gideon
    et al.
    Blekinge Institute of Technology, School of Computing.
    Persson, Jan A.
    Blekinge Institute of Technology, School of Computing.
    Davidsson, Paul
    Blekinge Institute of Technology, School of Computing.
    Clemedtson, Per Ola
    Method for quantitative valuation of road freight transport telematic services2012In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 6, no 4, p. 388-396Article in journal (Refereed)
    Abstract [en]

    This study describes transport telematic services (TTSs) for road-based heavy goods vehicle (HGV) transport and suggests a method for assessing the societal value of different TTSs. For decision making related to the selection of services to promote by potential investors, for example, governmental organisations and service providers, quantified service value can simplify the decision process by enabling comparison between TTSs. Moreover, these values can serve as inputs to quantitative analysis of service architectural system designs. The authors suggest a method for assessing the societal values of TTSs using potential saving indicators (PSIs), estimated in the context of Swedish HGV freight transport. To illustrate the proposed method, 32 services are analysed, and their societal values were quantified and compared for the Swedish HGV market. Results based on estimated values of PSIs and potential percentage savings indicate the following HGV-based TTSs to be of high societal potential: transport resource optimisation, dynamic traffic information, navigation, road hindrance warning, theft alarm and recovery, accident warning information, intelligent speed adaptation, eCall, en-route driver information, transport order handling, road user charging and sensitive goods monitoring.

  • 4.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Khatibi, Siamak
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Robust Visual Odometry Estimation of Road Vehicle from Dominant Surfaces for Large Scale Mapping2015In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 9, no 3, p. 314-322Article in journal (Refereed)
    Abstract [en]

    Every urban environment contains a rich set of dominant surfaces which can provide a solid foundation for visual odometry estimation. In this work visual odometry is robustly estimated by computing the motion of camera mounted on a vehicle. The proposed method first identifies a planar region and dynamically estimates the plane parameters. The candidate region and estimated plane parameters are then tracked in the subsequent images and an incremental update of the visual odometry is obtained. The proposed method is evaluated on a navigation dataset of stereo images taken by a car mounted camera that is driven in a large urban environment. The consistency and resilience of the method has also been evaluated on an indoor robot dataset. The results suggest that the proposed visual odometry estimation can robustly recover the motion by tracking a dominant planar surface in the Manhattan environment. In addition to motion estimation solution a set of strategies are discussed for mitigating the problematic factors arising from the unpredictable nature of the environment. The analyses of the results as well as dynamic environmental strategies indicate a strong potential of the method for being part of an autonomous or semi-autonomous system.

  • 5.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Cheng, Wei
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Goswami, Prashant
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Bai, Guohua
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours2018In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 1, p. 41-48Article in journal (Refereed)
    Abstract [en]

    Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting.However, kNN parameters self-adjustment has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training. We used realworld data with more than one-year traffic records to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods with regards to accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

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