Compressive origin-destination estimation

Sanandaji, Borhan M.; Varaiya, Pravin

doi:10.1179/1942787515y.0000000018

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…This is opposite to the solution evaluated using entropy maximization, which tries to achieve a solution as uniform as possible to minimize the errors. The sparsity as a regularizer has been used before for highway network OD estimation and has found promising results ( 17, 21–24 ). For example, ( 17 ) leverages sparsity in highway OD matrices to estimate a set of suitable traffic analysis zones (TAZs) and uses those zones to evaluate an OD matrix.…”

Section: Methodsmentioning

confidence: 99%

Transit Route Origin–Destination Matrix Estimation using Compressed Sensing

Kumar

Khani

Davis

2019

Transportation Research Record: Journal of the Transportation R

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The development of an origin–destination (OD) demand matrix is crucial for transit planning. With the help of automated data, it is possible to estimate a stop-level OD matrix. We propose a novel method for estimating transit route OD matrix using automatic passenger count (APC) data. The method uses [Formula: see text] norm regularizer, which leverages the sparsity in the actual OD matrix. The technique is popularly known as compressed sensing (CS). We also discuss the mathematical properties of the proposed optimization program and the complexity of solving it. We used simulation to assess the accuracy and efficiency of the method and found that the proposed method is able to recover the actual matrix within small errors. With increased sparsity in the actual OD matrix, the solution gets closer to the actual value of the matrix. The method was found to perform more efficiently even for different demand patterns. We also present a real numerical example of OD estimation of the A Line Bus Rapid Transit (BRT) route in Twin Cities, MN.

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Section: Methodsmentioning

confidence: 99%

Transit Route Origin–Destination Matrix Estimation using Compressed Sensing

Kumar

Khani

Davis

2019

Transportation Research Record: Journal of the Transportation R

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“…In the proposed model, the assumption of sparse O-D matrix is represented by the L1 regularisation, because minimisation of the L1 regularisation term induces a sparse solution. Previously researchers have used L1 regularisation to account for network anomalies [50][51][52], the impact of path flow sparsity in the O-D estimation problem was also explored [53]. However, few researches have considered that the O-D demands should be nonnegative in the regularisation problem, which is an important constraint in the O-D estimation process.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have focused on the Lagrangian dual of the for L1 regularisation, which regards the problem as an example of the basis pursuit principle. The advantage is to avoid tuning weight parameters for the regularisation term, with a compromise of spending more time on the optimisation procedure [53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Estimation of sparse O–D matrix accounting for demand volatility

Wen

Gardner

Waller

et al. 2018

IET Intelligent Transport Systems

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A critical issue in origin-destination (O-D) demand estimation is under-determination: the number of O-D pairs to be estimated is often much greater than the number of monitored links. In real world, some centroids tend to be more popular than others, and only few trips are made for intro-zonal travel. Consequently, a large portion of trips will be made for a small portion of O-D pairs, meaning many O-D pairs have only a few or even zero trips. Mathematically, this implies that the O-D matrix is sparse. Also, the correlation between link flows is often neglected in the O-D estimation problem, which can be obtained from day-to-day loop detector count data. Thus, sparsity regularisation is combined with link flow correlation to provide additional inputs for the O-D estimation process to mitigate the issue of under-determination and thereby improve estimation quality. In addition, a novel strategic user equilibrium model is implemented to provide route choice of users for the O-D estimation problem, which explicitly accounts for demand and link flow volatility. The model is formulated as a convex generalised least squares problem with regularisation, the usefulness of sparsity assumption, and link flow correlation is presented in the numerical analysis.

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Optimal Traffic Sensor Location for Origin–Destination Estimation Using a Compressed Sensing Framework

Wen

2017

IEEE Trans. Intell. Transport. Syst.

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Compressive origin-destination estimation

Cited by 3 publications

References 38 publications

Transit Route Origin–Destination Matrix Estimation using Compressed Sensing

Transit Route Origin–Destination Matrix Estimation using Compressed Sensing

Estimation of sparse O–D matrix accounting for demand volatility

Optimal Traffic Sensor Location for Origin–Destination Estimation Using a Compressed Sensing Framework

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