scholarly journals Repurposing existing traffic data sources for COVID-19 crisis management

Author(s):  
Casper Van Gheluwe ◽  
Angel J. Lopez ◽  
Ivana Semanjski ◽  
Sidharta Gautama
Author(s):  
Matthew Fullerton ◽  
Andreas Wenger ◽  
Mathias Baur ◽  
Florian Schimandl ◽  
Jonas Lüßmann ◽  
...  

2021 ◽  
Vol 2 ◽  
Author(s):  
Marisdea Castiglione ◽  
Guido Cantelmo ◽  
Moeid Qurashi ◽  
Marialisa Nigro ◽  
Constantinos Antoniou

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.


2016 ◽  
Vol 14 (2) ◽  
pp. 165-178 ◽  
Author(s):  
Andy Chow

Purpose This paper aims to present collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach for assessing performance of urban transport network facilities. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. This paper contributes to analysis and management of urban transport facilities. Design/methodology/approach In this paper, the data fusion algorithm are developed by using a kernel-based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space-time resolution, with different level of accuracy and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. Findings The results show that the proposed algorithm is able to reconstruct accurately the underlying traffic flow pattern on transport network facilities with ordinary data sources on both virtual and real-world test beds. The data sources considered herein include loop detectors, cameras and GPS devices. The proposed data fusion algorithm does not require assumption and calibration of any underlying model. It is easy to implement and compute through advanced technique such as parallel computing. Originality/value The presented study is among the first utilizing and integrating heterogeneous urban traffic data from a major city like London. Unlike many other existing studies, the proposed method is data driven and does not require any assumption of underlying model. The formulation of the data fusion algorithm also allows it to be parallelized for large-scale applications. The study contributes to the application of Big Data analytics to infrastructure management.


2002 ◽  
Vol 30 (3) ◽  
pp. 466-474

In In re Pharmatrak, Inc. Privacy Litigation, website users brought suit claiming that major pharmaceutical corporations and a web monitoring company violated three federal statutes protecting electronic communications and data by collecting web traffic data and personal information about website users. On August 13,2002, the District Court of Massachusetts dismissed these allegations, holding that the defendants were parties to the communications and thus exempted under the statutory language.The court also found that plaintiffs had not suffered an amount of damages required to sustain private action.


2008 ◽  
Author(s):  
Glenn E. Meyer ◽  
Carolyn B. Becker ◽  
Melissa M. Graham ◽  
John S. Price ◽  
Ashley Arsena ◽  
...  

2014 ◽  
Author(s):  
A. Calvo ◽  
M. Moreno ◽  
A. Ruiz-Sancho ◽  
M. Rapado-Castro ◽  
C. Moreno ◽  
...  

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