A data driven method for OD matrix estimation

Krishnakumari, Panchamy; Lint, Hans van; Djukic, Tamara; Cats, Oded

doi:10.1016/j.trpro.2019.05.009

Cited by 54 publications

(18 citation statements)

References 52 publications

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“…Extracting individual trips and flow data from the traffic link sensors is a non-trivial NP-Hard (non-deterministic polynomial-time hard) and time-dependent problem that requires high computation resources. It has been a subject of intense research for a long time and many approaches and models have been made [ 57 , 58 , 59 ]. But nevertheless, there are some direct ways of obtaining

information: (a) cloud route calculation services that receive directly drivers requests; (b) tracing individual vehicles in the network (using cameras and other mechanisms); and (c) using crowdsensing mechanisms by means of a dedicated app or an embedded measurement rootkit in an app.…”

Section: Mutraff Architecturementioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.

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Section: Mutraff Architecturementioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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“…In order to include the structure of the demand within the estimation framework, Ashok and Ben-Akiva (1993) formulated the KF in terms of deviations between the actual and the historical OD flows. The KF algorithm represents one of the most widely adopted solution framework for the online OD estimation problem (Barcelo and Montero, 2015;Zhang et al, 2017;Marzano et al, 2018;Krishnakumari et al, 2019;Liu et al, 2020). However, its application to the online OD estimation problem has several drawbacks.…”

Section: Kalman Filter and Non-linear Extensions For Oddementioning

confidence: 99%

Assignment Matrix Free Algorithms for On-line Estimation of Dynamic Origin-Destination Matrices

Castiglione

Cantelmo

Qurashi

et al. 2021

Front. Future Transp.

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Dynamic Traffic Assignment (DTA) models represent fundamental tools to forecast traffic flows on road networks, assessing the effects of traffic management and transport policies. As biased models lead to incorrect predictions, which can cause inaccurate evaluations and huge social costs, the calibration of DTA models is an established and active research field. When it comes to estimating Origin-Destination (OD) demand flows, perhaps the most important input for DTA models, one algorithm suggested to outperform all the others for real-time applications: the Kalman Filter (KF). This paper introduces a non-linear Kalman Filter framework for online dynamic OD estimation that reduces the number of variables and can easily incorporate heterogeneous data sources to better explain the non-linear relationship between traffic data and time-dependent OD-flows. Specifically, we propose a model that takes advantage of Principal Component Analysis (PCA) to capture spatial correlations between variables and better exploit the local nature of a specific KF recently proposed in literature, the Local Ensemble Transformed Kalman filter (LETKF). The main advantage of the LETKF is that the Kalman gain is not explicitly formulated which means that, differently from other approaches proposed in the literature, there is no need to compute the assignment matrix or its approximation. The paper shows that the LETKF can easily incorporate different data sources, such as traffic counts and link speeds. Additionally, thanks to the PCA, the model can identify local patterns within the data and better explain the correlation between variables and data. The effectiveness of the proposed methodology is demonstrated first through synthetic experiments where non-linear functions are used to benchmark the model in different conditions and then on the real-world network of Vitoria, Spain (2,884 nodes, 5,799 links) using the mesoscopic simulator Aimsun. Results show that the proposed method leads to better state estimation performances with respect to other Ensemble-based Kalman filters, providing improvements as high as 64% in terms of traffic data reproduction with a 17-fold problem dimensionality reduction.

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“…Origin-destination (OD) matrices reflect traffic demand patterns and play important roles in many applications over the entire traffic engineering, such as transportation planning and policy assessment, traffic operations, control, and management [1,2]. Driven by the increasing traffic congestion, urban rail transit has evolved by leaps and bones in the past decades [3].…”

Section: Introductionmentioning

confidence: 99%

Rail transit OD‐matrix completion via manifold regularized tensor factorisation

Dong

Ding

Tan

et al. 2021

IET Intelligent Trans Sys

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Urban rail transit has become an indispensable mode in major cities worldwide regarding the advantages of large capacity, high speed, punctuality, and environmental protection. Origin-destination (OD) matrix data is crucial to the organisation of rail train operation and management. Nevertheless, rail transit OD matrices are inevitably suffered from data loss problems due to the data transmission and acquisition failures. Tensor completion is a state-of-the-art method for missing data imputation. In this paper, a novel tensor completion method for OD-matrix completion is proposed. To this end, an OD-matrix tensor is established to represent OD information, and the similarity matrix of OD-matrix tensor for each dimension is extracted as a piece of auxiliary information expressing underlying multi-mode relationships of OD data. Finally, a manifold regularised tensor factorisation is applied to impute the missing OD data, in which the Graph Laplacians inferred from similarity weight matrices are used as regularisation priors on factorisation factors. The proposed model is applied to a case study of the metro line in Xi'an, China. The experimental results indicate that the proposed method outperforms baselines. It can accurately impute missing data within the OD matrices and work well even when the missing ratio is up to 80%.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A data driven method for OD matrix estimation

Cited by 54 publications

References 52 publications

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Assignment Matrix Free Algorithms for On-line Estimation of Dynamic Origin-Destination Matrices

Rail transit OD‐matrix completion via manifold regularized tensor factorisation

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