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  • 1.
    Babicheva, Tatiana
    et al.
    VEDECOM, 77 Rue Chantiers, F-78000 Versailles, France..
    Burghout, Wilco
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering. VEDECOM, 77 Rue Chantiers, F-78000 Versailles, France..
    Andreasson, Ingmar
    LogistikCtr Goteborg AB, Osbergsgatan 4 A, S-42677 Vastra Frolunda, Sweden..
    Faul, Nadege
    VEDECOM, 77 Rue Chantiers, F-78000 Versailles, France..
    Empty vehicle redistribution and fleet size in autonomous taxi systems2019Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 13, nr 4, s. 677-682Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study investigates empty vehicle redistribution algorithms for personal rapid transit and autonomous taxi services. The focus is on passenger service and operator cost. A new redistribution algorithm is presented in this study: index-based redistribution (IBR). IBR is a proactive method, meaning it takes into account both current demand and anticipated future demand, in contrast to reactive methods, which act based on current demand only. From information on currently waiting for passengers, predicted near-future demand and projected arrival of vehicles, IBR calculates an index for each vehicle station, and redistribution is done based on this index. Seven different algorithm combinations are evaluated using a test case in Paris Saclay, France (20 stations and 100 vehicles). A combination of simple nearest neighbours and IBR is shown to be promising. Its results outperform the other methods tested in peak and off-peak demand, in terms of average and maximum passenger waiting times as well as station queue length. The effect of vehicle fleet size on generalised cost is analysed. Waiting times, mileage and fleet size are taken into account while assessing this generalised cost.

  • 2.
    Cebecauer, Matej
    et al.
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering.
    Jenelius, Erik
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering.
    Burghout, Wilco
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering.
    Integrated framework for real-time urban network travel time prediction on sparse probe data2018Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, nr 1, s. 66-74Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

  • 3.
    Elevant, Katarina
    KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.
    Trust-networks for changing driver behaviour during severe weather2013Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 7, nr 4, s. 415-424Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Studies on on-road behaviour imply that designing user-centred services is important for raising awareness about severe weather and adverse road conditions. Along with the developments of new communication technologies and practices, the research area of ITS is challenged to move on from traditional ways of collecting and distributing traffic weather information. This study presents two methods for potential improvements and personalisation of traffic weather information. The methods were demonstrated and evaluated by 440 respondents in Stockholm. Weather alerts were sent by SMS 12-48 h, up to a week, prior to the occurrence of severe weather events during 2008-2010. The service was personalised because of assumptions regarding perception and memory of weather, including user's recent observations. The second aspect of potential improvement was the introduction of a social network component, including user-generated local weather observations. The impact of the service was evaluated in a longitudinal study through a series of questionnaires on user behaviour and evaluation of the service. The combination of the two methods proved efficient as the amount of changed decisions was of considerable amplitude. A correlation between time of exposure and changed decisions implies that social components and interactivity may be a powerful tool in traffic weather services and ITS.

  • 4.
    Ma, Xiaoliang
    et al.
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Centra, Centrum för trafikforskning, CTR.
    Koutsopoulos, Harilaos
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transporter och samhällsekonomi (stängd 20110301), Trafik och Logistik (stängd 20110301).
    Estimation of the automatic vehicle identification based spatial travel time information collected in Stockholm2010Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 4, nr 4, s. 298-306Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To support the implementation of real-time traffic information systems in the Stockholm city area using automatic vehicle identification (AVI) data, a preliminary travel time analysis tool has been developed. The program can manage and analyse travel time measurements in a distributed database server where both online and historical traffic information are saved. Meanwhile, several existing travel time estimation algorithms are implemented in the travel time analysis program, and are evaluated using four months of AVI data collected in the urban streets and arterials of and near the Stockholm downtown area. The advantages and disadvantages of those algorithms are also analysed using the highly noisy travel time measurements collected under the urban context. In addition, the authors have also evaluated a common statistical median filtering approach and suggested some modifications for AVI data estimation. In general, all these algorithms have the potential to be applied for real daily travel time estimation and the statistical median filter with modifications has been suggested for historical travel time estimation in real application. Finally, the authors point out an essential problem in travel time estimation and suggest a direction that may have the potential to improve the online traffic information quality.

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