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Jenelius, Erik, DocentORCID iD iconorcid.org/0000-0002-4106-3126
Publications (10 of 79) Show all publications
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
Open this publication in new window or tab >>3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
2019 (English)In: TRB Annual Meeting Online, Washington DC, US, 2019, p. 1-20Conference paper, Published 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.

Place, publisher, year, edition, pages
Washington DC, US: , 2019
Keywords
3D speed map, short-term prediction, travel time prediction, traffic prediction, large-scale prediction, clustering, partitioning, spatio-temporal partitioning
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-250647 (URN)
Conference
Transportation research board annual meeting (TRB)
Note

QC 20190502

Available from: 2019-05-01 Created: 2019-05-01 Last updated: 2019-08-27Bibliographically approved
Ding-Mastera, J., Gao, S., Jenelius, E., Rahmani, M. & Ben-Akiva, M. (2019). A latent-class adaptive routing choice model in stochastic time-dependent networks. Transportation Research Part B: Methodological, 124, 1-17
Open this publication in new window or tab >>A latent-class adaptive routing choice model in stochastic time-dependent networks
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2019 (English)In: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 124, p. 1-17Article in journal (Refereed) Published
Abstract [en]

Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler's ability to look ahead in order to incorporate real-time information not yet available at the time of decision. A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) readings. A latent-class Policy Size Logit model is specified, with routing policy users who follow routing policies and path users who follow fixed paths. Two additional layers of latency in the measurement equation are accounted for: 1) the choice of a routing policy is latent and only its realized path on a given day can be observed; and 2) when GPS readings have relatively long gaps, the realized path cannot be uniquely identified, and the likelihood of observing vehicle traces with non-consecutive links is instead maximized. Routing policy choice set generation is based on the generalization of path choice set generation methods. The generated choice sets achieve 95% coverage for 100% overlap threshold after correcting GPS mistakes and breaking up trips with intermediate stops, and further achieve 100% coverage for 90% overlap threshold. Estimation results show that the routing policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice or routing policy choice model. This suggests that travelers are heterogeneous in terms of their ability and/or willingness to plan ahead and utilize real-time information, and an appropriate route choice model for uncertain networks should take into account the underlying stochastic travel times and structured traveler heterogeneity in terms of real-time information utilization.

Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-251687 (URN)10.1016/j.trb.2019.03.018 (DOI)000469906800001 ()2-s2.0-85064202392 (Scopus ID)
Funder
TrenOp, Transport Research Environment with Novel PerspectivesSwedish Transport Administration
Note

QC 20190529

Available from: 2019-05-19 Created: 2019-05-19 Last updated: 2019-06-25Bibliographically approved
Tympakianaki, A., Koutsopoulos, H. N. & Jenelius, E. (2019). Anatomy of tunnel congestion: Causes and implications for tunnel traffic management. Tunnelling and Underground Space Technology, 83, 498-508
Open this publication in new window or tab >>Anatomy of tunnel congestion: Causes and implications for tunnel traffic management
2019 (English)In: Tunnelling and Underground Space Technology, ISSN 0886-7798, E-ISSN 1878-4364, Vol. 83, p. 498-508Article in journal (Refereed) Published
Abstract [en]

Tunnel congestion is an important safety problem and is often dealt with using disruptive traffic management strategies, such as closures. The paper proposes an approach to identify the underlying causes of recurrent congestion in tunnels and tests the hypothesis that the cause may vary from day to day. It also suggests that the appropriate tunnel management strategy to deploy depends on the cause. Utilizing traffic sensor data the approach consists of: (i) cluster analysis of historical traffic data to identify distinct congestion patterns; (ii) in-depth analysis of the underlying demand patterns and associated bottlenecks; (iii) simulation to evaluate alternative strategies for each demand pattern; (iv) on-line classification analysis which is able to identify, in real time, the emerging congestion pattern, and inform the type of mitigation strategy to be implemented. The methodology is demonstrated for a congested tunnel in Stockholm, Sweden revealing two different spatio-temporal congestion patterns. The results show that, if the current strategy of closures is to be used, the timing should depend on the congestion pattern. However, metering is the most promising strategy. The on-line classification of the emerging congestion pattern is effective and can inform appropriate strategy proactively. The analysis emphasizes that the effectiveness of tunnel traffic management can be increased by identifying the causes of congestion on a given day.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Tunnel traffic management, Data-driven analysis, Clustering, Simulation
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-242252 (URN)10.1016/j.tust.2018.10.015 (DOI)000454963800043 ()2-s2.0-85056468137 (Scopus ID)
Note

QC 20190130

Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-01-30Bibliographically approved
Jenelius, E. (2019). Data-driven metro train crowding prediction based on real-time load data. IEEE transactions on intelligent transportation systems (Print)
Open this publication in new window or tab >>Data-driven metro train crowding prediction based on real-time load data
2019 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed) Epub ahead of print
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-258005 (URN)10.1109/TITS.2019.2914729 (DOI)
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20190917

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-17Bibliographically approved
Zhang, W., Jenelius, E. & Badia, H. (2019). Efficiency of semi-autonomous and fully autonomous bus services in trunk-and-branches networks. Journal of Advanced Transportation, Article ID 7648735.
Open this publication in new window or tab >>Efficiency of semi-autonomous and fully autonomous bus services in trunk-and-branches networks
2019 (English)In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, article id 7648735Article in journal (Refereed) Published
Abstract [en]

Automation technology is expected to change the public transport sector radically in the future. One rising issue is whether to embrace the intermediate stage of semi-autonomous buses or to wait until fully autonomous buses are available. This paper proposes a cost model of bus operations considering automation technology. The generalized cost, which is the sum of waiting, riding, operating, and capital cost, is modeled for conventional, semi-autonomous, and fully autonomous bus services on a generic trunk-and-branches network. Semi-autonomous buses achieve reduced unit operating cost through automated platooning on the corridor. The relative efficiency of the different services is studied under a range of scenarios for commercial speed, network structure, and demand distribution. Analytical and numerical results show that fully autonomous buses exhibit great potential through reduced operating and waiting costs even if the additional capital cost is high. The advantages of semi-autonomous buses are weaker and most prominent in networks with low demand along a long corridor such as interurban networks. For both automation levels a commercial speed comparable to conventional vehicles is crucial. The established criteria provide input to planners and operators for understanding the potential of automated bus services.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2019
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-237286 (URN)10.1155/2019/7648735 (DOI)000460891200001 ()2-s2.0-85062792951 (Scopus ID)
Note

QC 20181107

Available from: 2018-10-26 Created: 2018-10-26 Last updated: 2019-05-24Bibliographically approved
Laskaris, G., Cats, O., Jenelius, E., Rinaldi, M. & Viti, F. (2019). Multiline holding based control for lines merging to a shared transit corridor. Transportmetrica B: Transport Dynamics, 7(1), 1062-1095
Open this publication in new window or tab >>Multiline holding based control for lines merging to a shared transit corridor
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2019 (English)In: Transportmetrica B: Transport Dynamics, ISSN 2168-0566, Vol. 7, no 1, p. 1062-1095Article in journal (Refereed) Published
Abstract [en]

In transit corridors, multiple lines share a sequence of consecutive stops to provide higher joint frequency in higher demand areas. A key challenge is to coordinate the transition from single line to joint operation. A holding control strategy aimed at minimizing passenger travel times is introduced for lines merging into a shared corridor, accounting for the coordination of vehicle arrivals from the merging lines as well as the regularity of each line. The criterion is tested using an artificial network and a real-world network to analyze the impact of demand distribution and compare cooperative versus single line control. We illustrate how the real-time strategy yields overall passenger gains, depending on the composition of different user groups. Results are assessed based on operation and passenger performance indicators and show that coordination is achieved. When combined with joint control in the common part, the proposed approach achieves consistent network-wide travel time benefits.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
Line coordination; corridor management; fork line operations; holding control
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-251688 (URN)10.1080/21680566.2018.1548312 (DOI)000466354800001 ()
Funder
VinnovaTrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20190520

Available from: 2019-05-19 Created: 2019-05-19 Last updated: 2019-05-20Bibliographically approved
Cats, O. & Jenelius, E. (2018). Beyond a complete failure: The impact of partial capacity degradation on public transport network vulnerability. Transportmetrica B: Transport Dynamics, 6(2), 77-96
Open this publication in new window or tab >>Beyond a complete failure: The impact of partial capacity degradation on public transport network vulnerability
2018 (English)In: Transportmetrica B: Transport Dynamics, ISSN 2168-0566, Vol. 6, no 2, p. 77-96Article in journal (Refereed) Published
Abstract [en]

Disruptions in public transport networks (PTNs) often lead to partial capacity reductions rather than complete closures. This study aims to move beyond the vulnerability analysis of complete failures by investigating the impacts of a range of capacity reductions on PTN performance. The relation between network performance and the degradation of line or link capacities is investigated by establishing a vulnerability curve and related metrics. The analysis framework is applied to a full-scan analysis of planned temporary line-level capacity reductions and an analysis of unplanned link-level capacity reductions on the most central segments in the multi-modal rapid PTN of Stockholm, Sweden. The impacts of capacity reductions are assessed using a non-equilibrium dynamic public transport operations and assignment model. The nonlinear properties of on-board crowding, denied boarding, network effects and route choice result in non-trivial, generally convex, relations which carry implications on disruption planning and real-time management.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Network vulnerability, disruption, capacity, public transport
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-200542 (URN)10.1080/21680566.2016.1267596 (DOI)000425810200002 ()2-s2.0-85006124214 (Scopus ID)
Note

QC 20170130

Available from: 2017-01-30 Created: 2017-01-30 Last updated: 2018-08-21Bibliographically approved
Saadallah, A. (2018). BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. IEEE Transactions on Knowledge and Data Engineering
Open this publication in new window or tab >>BRIGHT - Drift-Aware Demand Predictions for Taxi Networks
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2018 (English)In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191Article in journal (Refereed) Published
Abstract [en]

Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China) and Stockholm (Sweden), as well as controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task. 

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-258021 (URN)10.1109/TKDE.2018.2883616 (DOI)2-s2.0-85058130950 (Scopus ID)
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20191028

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-28Bibliographically approved
Jenelius, E. (2018). Car-Specific Metro Train Crowding Prediction Based on Real-Time Load Data. 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. 78-83). IEEE
Open this publication in new window or tab >>Car-Specific Metro Train Crowding Prediction Based on Real-Time Load Data
2018 (English)In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, p. 78-83Conference paper, Published paper (Refereed)
Abstract [en]

The paper formulates the car-specific metro train crowding prediction problem based on real-time load data and evaluates the performance of several prediction methods (stepwise regression, lasso, and boosted tree ensembles). The problem is studied for multiple stations along a metro line in Stockholm, Sweden. Prediction accuracy is evaluated with respect to absolute passenger loads and predefined discrete crowding levels. When available, predictions with real-time load data significantly outperform historical averages, with accuracy improvements varying in magnitude across target stations and prediction horizons.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-244589 (URN)000457881300013 ()2-s2.0-85060472544 (Scopus ID)978-1-7281-0323-5 (ISBN)
Conference
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Note

QC 20190225

Available from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-08-20Bibliographically approved
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
Open this publication in new window or tab >>Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm
2018 (English)In: Case Studies on Transport Policy, ISSN 2213-624X, E-ISSN 2213-6258, Vol. 6, no 2, p. 179-189Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2018
Keywords
Transport system disruptions, Data-driven analysis
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-231201 (URN)10.1016/j.cstp.2018.05.003 (DOI)000434260300001 ()2-s2.0-85047071116 (Scopus ID)
Note

QC 20180629

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-11-23Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-4106-3126

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