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Researchers had given the issue of soil collapsibility a substantial amount of attention, as well as international specifications in order to choose the most appropriate method to estimate soil collapse. these identification methods can be divided into regional, laboratory, and field methods. researchers declared that regional methods might give a...
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... Because ALPR data and actual link vehicle counts (observation data in the time interval ) are not available, the real OD matrices were assigned to the network to capture detected flows of partial paths and links for each time interval. Yazdi and Shafahi (2018) developed a greedy heuristic algorithm to locate ALPR cameras on the network. They improved Vasko's method (Vasko et al. 2016) for solving vehicle identification sensor locations for large-scale networks. ...
The precise estimation of time-varying demand matrices using traffic data is an essential step for planning, scheduling, and evaluating advanced traffic management systems (ATMS). This paper presents an innovative method (based on the least squares approach) to deal with the inherent complexities in estimating the dynamic characteristics of changing demand flow over time and considering congestion conditions. The time-dependent Origin-Destination (OD) demand matrices of the network are estimated by exploitation of the received partial paths data from an automated vehicle identification (AVI) system, and vehicle counts data from loop detectors on a subset of the links. A traffic assignment approach based on partial paths is embedded into the measurement equations of the least squares model. For all time intervals, the relation between the variable aspects of congestion (the temporal and spatial distribution of the OD traffic flows) is established by their variance-covariance matrices. The LSQR algorithm, an iterative algorithm that is logically equivalent to the conjugate gradient method, is employed for solving the proposed least squares problem. Numerical examples performed on three different approaches (only link counts data, only partial path flows data, and both of them) show that using the variance-covariance matrices is more precise for estimating time-dependent OD matrices. The Sioux Falls network is presented to examine the solution algorithm’s effectiveness and the model’s main ideas. This paper reports the features of the discussed model based on synthetic data as proof of concept that using partial path flows significantly improves the results for solving time-dependent OD matrices estimation problems.
... In this study, the stopping criteria of the algorithm were considered as 1000 iterations. LPR sensors are located in the network based on a greedy heuristic algorithm developed by Talebian Yazdi (2018). She developed Vasko's greedy-like heuristic algorithm (Vasko et al. 2016) to solve Hadavi and Shafahi's (2016) location model for large-scale networks. ...
After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.
Updating the urban origin-destination demand matrix using data from different sources in networks is essential in transportation planning, traffic management, and operations. This study presented a three-level model to update the origin-destination matrix of large-scale networks using combined data from vehicle counts, such as loop detectors on links as well as automated vehicle identification sensors, such as license plate recognition cameras on partial paths. The first level minimized the distance between the estimated and observed flows of partial paths, whereas the second level minimized the distance between vehicle counts and estimated flows of links. The lowest level found user equilibrium flow patterns for demand tables. To solve the model, an innovative method was offered. The basic ideas of the method included expanding a primary origin-destination matrix with a travel growth factor obtained from comparing estimated and observed link flows, and then adjusting path flows based on percentage differences in the estimated versus observed link or partial path flows. The estimated origin-destination matrices were reassigned under a user equilibrium principle, and the path flows were readjusted. The model was tested to estimate the origin-destination matrix of the large-scale network of Mashhad City with 2 million population, 163 traffic zones, and 2093 links. The estimation quality was evaluated using different error indicator measures. In conclusion, the results showed a significant accuracy to reconstruct the observed flows.
