Evaluation of trade-offs between two data sources for the accurate estimation of origin–destination matrices

Gómez, Penélope; Menéndez, Mónica; Mérida-Casermeiro, Enrique

doi:10.1080/21680566.2015.1025892

Cited by 11 publications

(8 citation statements)

References 45 publications

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“…1a), in terms of the percentage of lanes containing the AVL data, C pt . Initially, we set = C {40%, 15%} Finally, for the purpose of collecting the trajectory data, each vehicle in the network was tracked with a simulation resolution of 1 Hz, during one hour of the evaluation time (same as in Gómez et al, 2015). A uniform traffic demand was loaded consecutively within an hour.…”

Section: Simulation Scenariosmentioning

confidence: 99%

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

Dakic

Menéndez

2018

Transportation Research Part C: Emerging Technologies

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The Macroscopic Fundamental Diagram (MFD) has been recognized as a powerful framework to develop network-wide control strategies. Recently, the concept has been extended to the threedimensional MFD, used to investigate traffic dynamics of multi-modal urban cities, where different transport modes compete for, and share the limited road infrastructure. In most cases, the macroscopic traffic variables are estimated using either loop detector data (LDD) or floating car data (FCD). Taking into account that none of these data sources might be available, in this study we propose novel estimation methods for the space-mean speed of cars based on: (i) the automatic vehicle location (AVL) data of public transport where no FCD is available; and (ii) the fused FCD and AVL data sources where both are available, but FCD is not complete. Both methods account for the network configuration layout and the configuration of the public transport system. The first method allows one to derive either uni-modal or bi-modal macroscopic fundamental relationships, even in the extreme cases where no LDD nor FCD exist. The second method does not require a priori knowledge about FCD penetration rates and can significantly improve the estimation accuracy of the macroscopic fundamental relationships. Using empirical data from the city of Zurich, we demonstrate the applicability and validate the accuracy of the proposed methods in real-life traffic scenarios, providing a cross-comparison with the existing estimation methods. Such empirical comparison is, to the best of our knowledge, the first of its kind. The findings show that the proposed AVL-based estimation method can provide a good approximation of the average speed of cars at the network level. On the other hand, by fusing the FCD and AVL data, especially in case of sparse FCD, it is possible to obtain a more representative outcome regarding the performance of multi-modal traffic.

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Section: Simulation Scenariosmentioning

confidence: 99%

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

Dakic

Menéndez

2018

Transportation Research Part C: Emerging Technologies

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“…The layout of the simulation is shown inFig.5. This simulation model is calibrated and validated based on empirical measurements on Nov 16,. 2014.…”

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confidence: 99%

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Yang

Menéndez

2019

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a convex optimization based algorithm for queue profile estimation in a connected vehicle environment, which can also be used for trajectory reconstruction, delay evaluation, etc. This algorithm generalizes the widelyadopted assumption of a linear back of queue (BoQ) curve to a piecewise linear BoQ curve to consider more practical scenarios. The piecewise linear BoQ curve is estimated via a convex optimization model, ensuring efficient computation. Moreover, this paper explicitly handles cases with low penetration rates and low sampling rates, as well as measurement noises. Additionally, the proposed methodology is extended to an urban arterial, reusing the estimated departure information from the upstream intersections to further improve the estimation accuracy. Finally, two online implementation approaches are presented to perform real-time queue estimation.The proposed methodology is tested with two datasets: the Lankershim dataset in the NGSIM project and the simulated dataset of Wehntalerstrasse, Zurich, Switzerland. Results show that the error is less than 1.5 cars in undersaturated scenarios and 5.2 cars in oversaturated scenarios if the penetration rates are larger than 0.1 and sampling rates are higher than 0.05s −1 . It is demonstrated that by considering a piecewise linear BoQ curve, the estimation accuracy can be improved by up to 16%. Incorporating flow successfully can also reduce the estimation error by up to 16%. Results further show that the proposed methodology is robust to measurement errors. It is finally shown that the proposed framework can be solved within a reasonable time (0.8s), which is sufficient for most real-time applications.

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“…GPS probe vehicles are equipped with GPS to collect data in real time ( 6 ). There are two main types of FCD in operation today: cellular FCD data, derived from cellular networks; and GPS-based FCD, derived from different types of device that are equipped with a GPS receiver ( 11 ). Mobile network data consist of two types ( 9 ): call detail records (CDR) including calls, text messages and sometimes Internet usage, named active events; and network records generated from an interaction between a device and the mobile network, named passive events.…”

Section: Review Of Related Workmentioning

confidence: 99%

On Heterogenous Sampling Rates in Origin–Destination Matrix Estimation Based on Trajectory Data and Link Counts

Leurent

Sun

Xie

2022

Transportation Research Record: Journal of the Transportation R

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Being a fundamental metric of the transportation network, the origin–destination (O-D) flow matrix is a critical input for various transportation models and studies. This paper deals with the estimation of an O-D matrix of trip flows based on two kinds of data: probe trajectory data and local traffic counts. A Bayesian assignment framework is developed for demonstrating the relationship between the link probe sampling rates and the fractional contributions from the sampling rates on different O-D pairs. The unknown O-D matrix is estimated by applying cross entropy minimization using a prior matrix from the probe trajectories, along with the Bayesian assignment rules on link sample rates as the constraints. The methodology was applied using floating car data and camera link flow counts for a numerical experiment. The results show that the method can achieve a robust estimation of O-D matrices, even using different prior matrices. The issue of the heterogeneous sampling rates can be well addressed with link count constraints, effectively correcting the unknown bias in the probe sampling. The case study using real data also proves the feasibility of mining observed trajectory data to obtain the assignment fractions and estimate the O-D matrix inversely, avoiding the conventional sophisticated process of traffic assignment modeling.

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Evaluation of trade-offs between two data sources for the accurate estimation of origin–destination matrices

Cited by 11 publications

References 45 publications

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

On Heterogenous Sampling Rates in Origin–Destination Matrix Estimation Based on Trajectory Data and Link Counts

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