Abstract
A number of research papers have recently shown that the use of techniques based on the installation of vehicle identification devices allows us to address the observability problem of a traffic network in a much more efficient way than if it were done with traditional techniques. The use of such devices can lead to a better data set in terms of flows and therefore to a better definition of traffic flows, which is essential for traffic management in cities and regions. However, the current methodologies aimed at network modeling and data processing which are not fully adapted to the use of these devices in obtaining the necessary data for analyzing traffic and making network forecasts. This is because the essential variable in models which used data from plate scanning (as a particular case of AVI sensors) is composed of the route flows, while traditional methods are based on the observation of link and/or origin-destination flows. In this context, this paper proposes several practical contributions, in particular: (1) a traffic network design method aimed to use the plate scanning data to estimate traffic flows and (2) an algorithm for locating plate reader devices to reduce the effect of the uncertain knowledge of route enumeration. Next, using the well-known Nguyen-Dupuis network, a sensitivity analysis has been carried out to evaluate the influence of different parameters of the model on the final solution. These parameters are the considered routes, the degree of network simplification, and the available budget to install devices. Finally, the method has been applied to a real network.
Highlights
As is well known, estimating the origin-destination trip matrix, route flows, and link flows is essential to achieving efficient traffic management
Many authors have dealt with this problem, trying to estimate these traffic flows using either information from traditional sources such as traffic counts or information from more innovative sources such as mobile phones and GPS data (Huang et al [4], IbarraEspinosa et al [5], and Moreira-Matias et al [6]), Big Data (Toole et al [7] and Zin et al [8]), or automatic vehicle identification (AVI) data (Castillo et al [9], Fu et al [10], and Fedorov et al [11], among others)
A traffic network is a pair (N, A), where N is a set of nodes and A is a set of directed links connecting these nodes. e links represent the streets of a city, and the nodes typically represent the intersections of these streets
Summary
As is well known, estimating the origin-destination trip matrix, route flows, and link flows is essential to achieving efficient traffic management. Note that the solution for this problem is not unique in terms of links included in SL but constitutes a particular solution obtained through an optimization problem (see, for example, Castillo et al [36]) Taking advantage of this fact, this paper proposes a heuristic algorithm to find set SL that minimizes the RMARE obtained using equation (4) and that provides the best set of routes which are able to represent the traffic flow in the entire network. Trying to solve some of these problems, SanchezCambronero et al [31] proposed a model that allows to design a traffic network that minimizes the negative effects of the use of centroids and connectors by replacing them with “origin nodes” and “destination nodes” in such a way that all trip origins and destinations are assigned to these nodes of the network in accordance with the vehicle paths and the network shape
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