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Cheng, Y. (author), Krishnakumari, P.K. (author)
To analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily...
journal article 2024
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van Schaik, L. (author), Duives, D.C. (author), Hoogendoorn-Lanser, S. (author), Hoekstra, Jan Willem (author), Daamen, W. (author), Gavriilidou, A. (author), Krishnakumari, P.K. (author), Rinaldi, M. (author), Hoogendoorn, S.P. (author)
Physical distancing has been an important asset in limiting the SARS-CoV-2 virus spread during the COVID-19 pandemic. This study aims to assess compliance with physical distancing and to evaluate the combination of observed and self-reported data used. This research shows that it is difficult to operationalize new rules, that context affects...
journal article 2024
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Wang, Z. (author), Krishnakumari, P.K. (author), Anupam, K. (author), van Lint, J.W.C. (author), Erkens, S. (author)
The relationship between real-world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to...
journal article 2024
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Xu, Y. (author), Krishnakumari, P.K. (author), Yorke-Smith, N. (author), Hoogendoorn, S.P. (author)
COVID-19 significantly influenced travel behaviours and public attitudes towards public transport. Various studies have illustrated complicated factors related to long-term travel behaviour, indicating difficulty in understanding and predicting post-pandemic long-term travel behaviour via traditional methods. In these complex circumstances,...
conference paper 2023
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Zhang, Jinlei (author), Chen, Yijie (author), Krishnakumari, P.K. (author), Jin, Guangyin (author), Wang, Chengcheng (author), Yang, Lixing (author)
Accurate and reliable short- term passenger flow prediction can support operations and decision-making of the URT system from multiple perspectives. In this paper, we propose a URT multi- step short- term passenger flow prediction model at the network level based on a Transformer-based LSTM network, Depth-wise Attention Block, and CNN network...
journal article 2023
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Wang, Z. (author), Pel, A.J. (author), Verma, T. (author), Krishnakumari, P.K. (author), van Brakel, Peter (author), van Oort, N. (author)
Predictions on public transport ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. At an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer lag...
conference paper 2022
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Wang, Z. (author), Pel, A.J. (author), Verma, T. (author), Krishnakumari, P.K. (author), van Brakel, Peter (author), van Oort, N. (author)
Predictions on Public Transport (PT) ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. On an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer...
journal article 2022
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Wang, Z. (author), Krishnakumari, P.K. (author), Anupam, K. (author), van Lint, J.W.C. (author), Erkens, S. (author)
Understanding the relationship between pavement raveling and traffic characteristics is important to pavement management and maintenance planning. In this work, we propose a framework to empirically quantify this relationship. It consists of an alignment method to tackle the inconsistent spatial-temporal scales of the raveling and traffic...
conference paper 2022
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Hoogendoorn, S.P. (author), Daamen, W. (author), Yuan, Y. (author), Krishnakumari, P.K. (author)
In dit artikel worden de effecten van COVID-19 op de (relevante onderdelen van de) aanbodkant van het mobiliteitssysteem beschreven. Gezien de verwachte impact ligt de focus op voetgangersstromen, fietsstromen, het gebruik van deeldiensten en openbaar vervoer voertuigen. We kijken naar het effect op de capaciteit, via gemeten of theoretisch...
journal article 2021
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Krishnakumari, P.K. (author)
Cities are complex, dynamic and ever-evolving. We need to understand how these cities work in order to predict, control or optimize their operations. We have identified some open issues related to network and data complexity that need to be solved to build feasible methods for these purposes. To this end, we first build multiscale graphs...
doctoral thesis 2020
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van Lint, J.W.C. (author), Nguyen, T.T. (author), Krishnakumari, P.K. (author), Calvert, S.C. (author), Schuurman, Henk (author), Schreuder, Marco (author)
Is it possible to use just aggregate carriageway data for the evaluation of congestion warning systems (CWS) in large networks—or any system affecting traffic safety for that matter? In this paper, two hypotheses related to this question are tested. The first hypothesis is that it can be done by comparing large-scale congestion patterns on...
journal article 2020
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Krishnakumari, P.K. (author), Cats, O. (author), van Lint, J.W.C. (author)
The biggest challenge of analysing network traffic dynamics of large-scale networks is its complexity and pattern interpretability. In this work, we present a new computationally efficient method, inspired by human vision, to reduce the dimensions of a large-scale network and describe the traffic conditions with a compact, scalable and...
journal article 2020
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Cats, O. (author), Krishnakumari, P.K. (author)
In large-scale urban agglomerations, heavy rail in the form of metro and commuter train serves as the backbone of the metropolitan public transport network. The objective of this paper is to investigate whether networks with strikingly different structure and development pattern exhibit different robustness properties in the event of random...
journal article 2020
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Krishnakumari, P.K. (author), Cats, O. (author), van Lint, J.W.C. (author)
Smart card data enables the estimation of passenger delays throughout the public transit network. However, this delay is measured per passenger trajectory and not per network component. The implication is that it is currently not possible to identify the contribution of individual system components – stations and track segments – to overall...
journal article 2020
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Krishnakumari, P.K. (author), van Lint, J.W.C. (author), Djukic, T. (author), Cats, O. (author)
The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic...
journal article 2020
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Nguyen, T.T. (author), Krishnakumari, P.K. (author), Calvert, S.C. (author), Vu, Hai L. (author), van Lint, J.W.C. (author)
Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and...
journal article 2019
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Cats, O. (author), Krishnakumari, P.K. (author), Tundulyasaree, Krissada (author)
In large-scale urban agglomerations, heavy rail in the form of metro and commuter train serves as the backbone of the metropolitan public transport network. Transport systems are subject to recurrent disruptions that may result in severe consequences for network performance and society at large. The objective of this paper is to compare the...
conference paper 2019
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Liu, T.L.K. (author), Krishnakumari, P.K. (author), Cats, O. (author)
On-demand transport has become a common mode of transport with ride-sourcing companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in prevailing demand patterns can be used by service providers to better anticipate future demand distribution and thus support demand-Anticipatory fleet management strategies. To...
conference paper 2019
document
Krishnakumari, P.K. (author), Cats, O. (author), van Lint, J.W.C. (author)
Graphs at different scales are essential tools for many transportation applications. Notwithstanding their relevance, these graphs are created and maintained manually for most applications, in both research and practice. In this paper, we develop a heuristic method for automatically generating multiscale graph representations without...
journal article 2019
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Krishnakumari, P.K. (author), Perotti, Alan (author), Pinto, Viviana (author), Cats, O. (author), van Lint, J.W.C. (author)
Large-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. One of the key issues is how to integrate spatial and temporal analyses efficiently. Deep Learning is gaining momentum as a go-to approach for artificial vision, and transfer learning approaches allow...
conference paper 2018
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