Endre søk
Link to record
Permanent link

Direct link
BETA
Publikasjoner (7 av 7) Visa alla publikasjoner
Cebecauer, M., Gundlegård, D., Jenelius, E. & Burghout, W. (2019). 3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction. In: TRB Annual Meeting Online: . Paper presented at Transportation research board annual meeting (TRB) (pp. 1-20). Washington DC, US
Åpne denne publikasjonen i ny fane eller vindu >>3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
2019 (engelsk)Inngår i: TRB Annual Meeting Online, Washington DC, US, 2019, s. 1-20Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Washington DC, US: , 2019
Emneord
3D speed map, short-term prediction, travel time prediction, traffic prediction, large-scale prediction, clustering, partitioning, spatio-temporal partitioning
HSV kategori
Forskningsprogram
Transportvetenskap
Identifikatorer
urn:nbn:se:kth:diva-250647 (URN)
Konferanse
Transportation research board annual meeting (TRB)
Merknad

QC 20190502

Tilgjengelig fra: 2019-05-01 Laget: 2019-05-01 Sist oppdatert: 2019-08-27bibliografisk kontrollert
Langbroek, J. H. M., Cebecauer, M., Malmsten, J., Franklin, J. P., Susilo, Y. O. & Georén, P. (2019). Electric vehicle rental and electric vehicle adoption. Paper presented at 14th International Conference of the Network-on-European-Communications-and-Transport-Activities-Research (NECTAR), MAY 31-JUN 02, 2017, Madrid, Spain. Research in Transportation Economics, 73, 72-82
Åpne denne publikasjonen i ny fane eller vindu >>Electric vehicle rental and electric vehicle adoption
Vise andre…
2019 (engelsk)Inngår i: Research in Transportation Economics, ISSN 0739-8859, E-ISSN 1875-7979, Vol. 73, s. 72-82Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
Elsevier, 2019
Emneord
Electric vehicle adoption, Car rental, Transtheoretical model of change, Protection motivation theory, Driving patterns
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-255340 (URN)10.1016/j.retrec.2019.02.002 (DOI)000472704400008 ()2-s2.0-85061382286 (Scopus ID)
Konferanse
14th International Conference of the Network-on-European-Communications-and-Transport-Activities-Research (NECTAR), MAY 31-JUN 02, 2017, Madrid, Spain
Merknad

QC 20190807

Tilgjengelig fra: 2019-08-07 Laget: 2019-08-07 Sist oppdatert: 2019-08-07bibliografisk kontrollert
Cebecauer, M. (2019). Short-Term Traffic Prediction in Large-Scale Urban Networks. (Licentiate dissertation). Stockholm: KTH Royal Institute of Technology
Åpne denne publikasjonen i ny fane eller vindu >>Short-Term Traffic Prediction in Large-Scale Urban Networks
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2019. s. 21
Serie
TRITA-ABE-DLT ; 1915
Emneord
travel time prediction, short-term travel time prediction, traffic prediction, clustering, partitioning, spatio-temporal partitioning, large-scale prediction, PPCA, 3D speed map
HSV kategori
Forskningsprogram
Transportvetenskap
Identifikatorer
urn:nbn:se:kth:diva-250650 (URN)978-91-7873-224-1 (ISBN)
Presentation
2019-05-31, B2, Brinellvägen 23, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Merknad

QC 20190531

Tilgjengelig fra: 2019-05-02 Laget: 2019-05-01 Sist oppdatert: 2019-05-02bibliografisk kontrollert
Tympakianaki, A., Koutsopoulos, H. N., Jenelius, E. & Cebecauer, M. (2018). Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm. Case Studies on Transport Policy, 6(2), 179-189
Åpne denne publikasjonen i ny fane eller vindu >>Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm
2018 (engelsk)Inngår i: Case Studies on Transport Policy, ISSN 2213-624X, E-ISSN 2213-6258, Vol. 6, nr 2, s. 179-189Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
ELSEVIER SCIENCE BV, 2018
Emneord
Transport system disruptions, Data-driven analysis
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-231201 (URN)10.1016/j.cstp.2018.05.003 (DOI)000434260300001 ()2-s2.0-85047071116 (Scopus ID)
Merknad

QC 20180629

Tilgjengelig fra: 2018-06-29 Laget: 2018-06-29 Sist oppdatert: 2018-11-23bibliografisk kontrollert
Koháni, M., Czimmermann, P., Váňa, M., Cebecauer, M. & Buzna, Ľ. (2018). Location-scheduling optimization problem to design private charging infrastructure for electric vehicles. In: 6th International Conference on Operations Research and Enterprise Systems, ICORES 2017: . Paper presented at 6th ICORES 23 February 2017 through 25 February 2017 (pp. 151-169). Springer
Åpne denne publikasjonen i ny fane eller vindu >>Location-scheduling optimization problem to design private charging infrastructure for electric vehicles
Vise andre…
2018 (engelsk)Inngår i: 6th International Conference on Operations Research and Enterprise Systems, ICORES 2017, Springer, 2018, s. 151-169Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2018
Emneord
Charging infrastructure, Electric vehicles, GPS traces, Urban areas, Charging (batteries), Internal combustion engines, Location, Operations research, Optimization, Scheduling, Taxicabs, Trucks, Charging infrastructures, Complete information, Coordinated charging, Optimization modeling, Practical procedures, Scheduling optimization, Fleet operations
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-236417 (URN)10.1007/978-3-319-94767-9_8 (DOI)2-s2.0-85049696864 (Scopus ID)9783319947662 (ISBN)
Konferanse
6th ICORES 23 February 2017 through 25 February 2017
Merknad

QC 20181026

Tilgjengelig fra: 2018-10-26 Laget: 2018-10-26 Sist oppdatert: 2019-08-27bibliografisk kontrollert
Cebecauer, M., Jenelius, E. & Burghout, W. (2018). Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction. In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI (pp. 1390-1395). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction
2018 (engelsk)Inngår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, s. 1390-1395Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2018
Serie
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-244586 (URN)000457881301060 ()2-s2.0-85060452125 (Scopus ID)978-1-7281-0323-5 (ISBN)
Konferanse
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Merknad

QC 20190304

Tilgjengelig fra: 2019-03-04 Laget: 2019-03-04 Sist oppdatert: 2019-05-10bibliografisk kontrollert
Koháni, M., Czimmermann, P., Váňa, M., Cebecauer, M. & Buzna, L. (2017). Designing charging infrastructure for a fleet of electric vehicles operating in large urban areas. In: ICORES 2017 - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems: . Paper presented at 6th International Conference on Operations Research and Enterprise Systems, ICORES 2017, 23 February 2017 through 25 February 2017 (pp. 360-368). SciTePress
Åpne denne publikasjonen i ny fane eller vindu >>Designing charging infrastructure for a fleet of electric vehicles operating in large urban areas
Vise andre…
2017 (engelsk)Inngår i: ICORES 2017 - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems, SciTePress , 2017, s. 360-368Konferansepaper, Publicerat paper (Fagfellevurdert)
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. 

sted, utgiver, år, opplag, sider
SciTePress, 2017
Emneord
Charging Infrastructure, Electric Vehicles, GPS Traces, Urban Areas, Charging (batteries), Design, Internal combustion engines, Location, Operations research, Taxicabs, Trucks, Candidate locations, Charging infrastructures, Charging station, Data characterizing, Practical procedures, Scheduling decisions, Fleet operations
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-236842 (URN)000413254200039 ()2-s2.0-85048747635 (Scopus ID)9789897582189 (ISBN)
Konferanse
6th International Conference on Operations Research and Enterprise Systems, ICORES 2017, 23 February 2017 through 25 February 2017
Merknad

QC 20181221

Tilgjengelig fra: 2018-12-21 Laget: 2018-12-21 Sist oppdatert: 2019-05-10bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8499-0843