2016
DOI: 10.1016/j.trc.2015.08.009
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Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation

Abstract: Estimation/updating of origin-destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in online contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks-except from closed… Show more

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Cited by 94 publications
(51 citation statements)
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“…Computational experiments were conducted with urban networks of various sizes, see [4] for details. For the sake of completeness we report here those for the mediumsize network from the city of Vitoria (Spain), depicted in Figure 3, including 57 centroids, 3249 OD pairs, 2800 intersections and a modeled network of about 600 km.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Computational experiments were conducted with urban networks of various sizes, see [4] for details. For the sake of completeness we report here those for the mediumsize network from the city of Vitoria (Spain), depicted in Figure 3, including 57 centroids, 3249 OD pairs, 2800 intersections and a modeled network of about 600 km.…”
Section: Resultsmentioning
confidence: 99%
“…Notably, the AVI sensors are deployed following a layout strategy, whose details can be found in [9], to optimize the capturing of Bluetooth equipped vehicles and provide effective travel time measurements between AVI sensors. Various methods were tested in the referenced paper [4] depending on different values for the design factors, such as initial a priori OD estimates, and penetration rates of Bluetooth equipped vehicles. Results for the modified Kalman filtering approach (denoted Method 5 in [4]), assuming 100% Bluetooth penetration rate and prior OD scenarios D7 (low-demand), D8 (medium demand) and D9 (highdemand), are presented in Table 1 for selected goodness of fit measures: normalized root mean squared error (NRMSE), Theil's U coefficient and R 2 .…”
Section: Resultsmentioning
confidence: 99%
“…From a methodological point of view, the proposed approach relaxes the strong limitation of having a good starting demand matrix. As reported in [15], the capability of the DODE solution algorithm to correct the biases within the temporal and spatial structure of the demand is a strict requirement for having robust results. Mobile phone data is shown to improve the performances of the Single-Step.…”
Section: Discussionmentioning
confidence: 99%
“…First, as pointed out by Antoniou et al [15], the starting matrix is still a key input for all state-of-the-art DODE models. The first step of this formulation focuses on improving the historical demand matrix by performing a broad evaluation of the solution space and estimating a "better" updated seed matrix to be used in the second step.…”
Section: The Demand Estimation Problemmentioning
confidence: 99%
“…The research effort was directed mainly to improve the efficiency and the effectiveness of the solution methods, by following different research lines [4]: (i) introducing some approximation to the optimization method to reduce the computational effort; (ii) including more variables exploiting all available sources of information on traffic performances; (iii) assuming some simplification into the traffic assignment model.…”
Section: Introductionmentioning
confidence: 99%