![od matrix estimation transcad od matrix estimation transcad](https://d3i71xaburhd42.cloudfront.net/0a6c62fd0f6c493a0112d42b4027a29ae2f1ddaa/7-Figure3-1.png)
Palabras clave: Tramos críticos, vulnerabilidad, carreteras mexicanas. A partir de su aplicación a la red carretera de México, se presentan los resultados obtenidos, con la identificación de los tramos críticos donde la red es vulnerable y se demuestra que es factible aplicar la metodología con la información disponible en México. En seguida se muestra el método para calcular las afectaciones, mediante el uso de una interfaz desarrollada en TransCAD ®.
![od matrix estimation transcad od matrix estimation transcad](https://www.researchgate.net/profile/Jwc-Lint/publication/260601989/figure/tbl2/AS:668630631055376@1536425373482/Results-of-state-estimation-example_Q320.jpg)
Se discute la metodología a utilizar, de acuerdo con la propuesta de Scott y colaboradores y se presenta el modelo propio. Se pone énfasis en la metodología pero también se ofrecen los resultados de su aplicación, mediante el análisis de la afectación en el tiempo total de viaje en la red, incluyendo los efectos de cambios de ruta en atención a la capacidad vial. Por ello, en este artículo se propone un análisis para identificar los tramos críticos de la red carretera, de acuerdo con los flujos de carga. La posible interrupción de la red carretera mexicana cobra mayor importancia por la dependencia de la economía al transporte automotor. Key words: Critical segments, vulnerability, Mexican roads. Application of the method to the main road network in Mexico has identified critical segments where the network is vulnerable and it has been demonstrated that is feasible to apply the methodology with the information available in Mexico. Calculation of the effects includes an interface developed in TransCAD ®. The method uses the criterion 'effect on travel time', AT a, of Scott et al., but an original model is presented. The emphasis is on methodology, but the results of applying the method are also shown, by analysing the effect on total travel time within the network, including the effects of route changes in response to segment capacities. Therefore, this article presents an analysis to identify critical segments of the road network in terms of freight transport. The potential for disruption of the Mexican road network increases in importance as the economic dependence on road transport increases. Email: 2 July 2009.įinal version accepted: 15 February 2010. Email: División de Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Ciudad Universitaria, C.P. * Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Ciudad Universitaria, C.P. Luz Gradilla Hernández*, Ovidio González Gómez** Identificación de tramos críticos por vulnerabilidad para el traslado de mercancía en la red carretera pavimentada de México The research questions are primarily related to the conversion of stop level OD (stOD) to ztOD, transfer detection, and a few miscellaneous problems.Identification of critical segments by vulnerability for freight transport on the paved road network of Mexico The findings reveal many unanswered critical research questions which need to be addressed for ztOD estimation using smartcard data. Transfer detection algorithms distinguish between a transfer or an activity between two consecutive boarding and alighting. Estimation of unknowns includes boarding and alighting information estimation of passengers. The steps include processes of data cleansing, estimation of unknowns, transfer detection, validation of developed algorithms, and ultimately estimation of zone level transit OD (ztOD). The primary focus of this article is to critically analyse the current literature on essential steps involved in the tOD estimation process. One of the smart card data applications is the estimation of the public transit origin–destination matrix (tOD). During the last two decades, a tremendous amount of research has been done to employ this big data for various transport applications from transit planning to real-time operation and control.
![od matrix estimation transcad od matrix estimation transcad](https://www.researchgate.net/publication/331478240/figure/fig3/AS:1095901284175873@1638294635056/CNTME-model-This-model-is-fed-by-an-nn-L2AM-matrix-The-convolution-part-maps-the-L2AM_Q320.jpg)
In public transport, smartcards are primarily used for automatic fare collection purpose, which in turn generate massive data.