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  • 1.
    Cebecauer, Matej
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Short-Term Traffic Prediction in Large-Scale Urban Networks2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.

  • 2.
    Cebecauer, Matej
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Gundlegård, David
    Department of Science and Technology,Linköping University.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Burghout, Wilco
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction2019In: TRB Annual Meeting Online, Washington DC, US, 2019, p. 1-20Conference paper (Refereed)
    Abstract [en]

    City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.

  • 3.
    Cebecauer, Matej
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Burghout, Wilco
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction2018In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, p. 1390-1395Conference paper (Refereed)
    Abstract [en]

    The paper explores the potential of spatiotemporal network partitioning for travel time prediction accuracy and computational costs in the context of large-scale urban road networks (including motorways/freeways, arterials and urban streets). Forecasting in this context is challenging due to the complexity, heterogeneity, noisy data, unexpected events and the size of the traffic network. The proposed spatio-temporal network partitioning methodology is versatile, and can be applied for any source of travel time data and multivariate travel time prediction method. A case study of Stockholm, Sweden considers a network exceeding 11,000 links and uses taxi probe data as the source of travel times data. To predict the travel times the Probabilistic Principal Component Analysis (PPCA) is used. Results show that the spatio-temporal network partitioning provides a more appropriate bias-variance tradeoff, and that prediction accuracy and computational costs are improved by considering the proper number of clusters towards robust large-scale travel time prediction.

  • 4. Koháni, M.
    et al.
    Czimmermann, P.
    Váňa, M.
    Cebecauer, Matej
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Buzna, L.
    Designing charging infrastructure for a fleet of electric vehicles operating in large urban areas2017In: ICORES 2017 - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems, SciTePress , 2017, p. 360-368Conference paper (Refereed)
    Abstract [en]

    Here, we propose a method to design a charging infrastructure for a fleet of electric vehicles such as a fleet of taxicabs, fleet of vans used in the city logistics or a fleet of shared vehicles, operating in large urban areas. Design of a charging infrastructure includes decisions about charging stations location and number of charging points at each station. It is assumed that the fleet is originally composed of vehicles equipped with an internal combustion engine, however, the operator is wishing to replace them with fully electric vehicles. To avoid an interaction with other electric vehicles it is required to design a private network of charging stations that will be specifically adapted to the operation of a fleet. It is often possible to use GPS traces of vehicles characterizing actual travel patterns of individual vehicles. First, to derive a suitable set of candidate locations from GPS data, we propose a practical procedure where the outcomes can be simply controlled by setting few parameter values. Second, we formulate a mathematical model that combines location and scheduling decisions to ensure that requirements of vehicles can be satisfied. We validate the applicability of our approach by applying it to the data characterizing a large taxicab fleet operating in the city of Stockholm. Our results indicate that this approach can be used to estimate the minimal requirements to set up the charging infrastructure. 

  • 5. Koháni, M.
    et al.
    Czimmermann, P.
    Váňa, M.
    Cebecauer, Matej
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Buzna, Ľ.
    Location-scheduling optimization problem to design private charging infrastructure for electric vehicles2018In: 6th International Conference on Operations Research and Enterprise Systems, ICORES 2017, Springer, 2018, p. 151-169Conference paper (Refereed)
    Abstract [en]

    We propose optimization model to design a charging infrastructure for a fleet of electric vehicles. Applicable examples include a fleet of vans used in the city logistics, a fleet of taxicabs or a fleet of shared vehicles operating in urban areas. Fleet operator is wishing to replace vehicles equipped with an internal combustion engine with fully electric vehicles. To eliminate interaction with other electric vehicles it is required to design a private network of charging stations that is specifically adjusted to the fleet operation. First, to derive a suitable set of candidate locations from GPS data, we propose a practical procedure where the outcomes can be simply controlled by setting few parameter values. Second, we formulate a mathematical model that combines location and scheduling decisions to ensure that requirements of vehicles can be satisfied. We validate the applicability of our approach by applying it to data characterizing a large taxicab fleet operating in the city of Stockholm. The model assumes that all vehicles posses complete information about all other vehicles. To study the role of available information, we evaluate the resulting designs considering the coordinated charging when vehicle drivers, for example, reveal to each other departure times, and the uncoordinated charging when vehicle drivers know only actual occupation of charging points. Our results indicate that this approach can be used to estimate the minimal requirements to set up the charging infrastructure.

  • 6.
    Langbroek, Joram H. M.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
    Cebecauer, Matej
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Malmsten, Jon
    Franklin, Joel P.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Susilo, Yusak O.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Georén, Peter
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. Solkompaniet Sverige AB, Vastbergavagen 4, S-12630 Hagersten, Sweden..
    Electric vehicle rental and electric vehicle adoption2019In: Research in Transportation Economics, ISSN 0739-8859, E-ISSN 1875-7979, Vol. 73, p. 72-82Article in journal (Refereed)
    Abstract [en]

    This case study describes the project Elbilsiandet (The Electric Vehicle Country) in Gotland, Sweden, where the island Gotland is made "ready for electric vehicles" by providing a network of charging infrastructure and electric vehicle rental during several summer seasons. The influence of the electric vehicle (EV) rental scheme on the process towards electric vehicle adoption is investigated using the Protection Motivation Theory (PMT) and the Transtheoretical Model of Change (TTM). Moreover, the travel patterns of electric rental cars are compared with those of conventional rental cars. The main results of this study are the following: Firstly, people renting an EV are on average closer to electric vehicle adoption than people renting a conventional vehicle. Secondly, people who rent an EV are at the time of rental associated with more positive attitudes towards EVs, have more knowledge about EVs and would feel more secure driving an EV. Thirdly, EV-rental does not seem to have a large additional effect on the stage-of-change towards EV-adoption of the participants. Lastly, the driving patterns of EVs do not seem to indicate serious limitations regarding driving distance, parking time and the destinations that have been visited, as compared to the driving patterns of conventional rental cars.

  • 7.
    Tympakianaki, Athina
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Koutsopoulos, Haris N.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering. Northeastern Univ, Dept Civil & Environm Engn, Boston, MA 02115 USA.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Cebecauer, Matej
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm2018In: Case Studies on Transport Policy, ISSN 2213-624X, E-ISSN 2213-6258, Vol. 6, no 2, p. 179-189Article in journal (Refereed)
    Abstract [en]

    The paper explores the utilization of heterogeneous data sources to analyze the multimodal impacts of transport network disruptions. A systematic data-driven approach is proposed for the analysis of impacts with respect to two aspects: (a) spatiotemporal network changes, and (b) multimodal effects. The feasibility and benefits of combining various data sources are demonstrated through a case study for a tunnel in Stockholm, Sweden which is often prone to closures. Several questions are addressed including the identification of impacted areas, and the evaluation of impacts on network performance, demand patterns and performance of the public transport system. The results indicate significant impact of tunnel closures on the network traffic conditions due to the redistribution of vehicles on alternative paths. Effects are also found on the performance of public transport. Analysis of the demand reveals redistribution of traffic during the tunnel closures, consistent with the observed impacts on network performance. Evidence for redistribution of travelers to public transport is observed as a potential effect of the closures. Better understanding of multimodal impacts of a disruption can assist authorities in their decision-making process to apply adequate traffic management policies.

1 - 7 of 7
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