In densely populated urban areas, predicting the post-earthquake performance of a transport network is a particularly challenging task that requires the integration of modeled structural seismic response, damage scenarios, and resulting traffic behavior. Previous approaches assessing the vulnerability and performance of networks after earthquakes have not succeeded in capturing and estimating the interdependencies between seismic risk parameters and key traffic behavior variables. This paper presents a methodology, based on data analysis and optimization, where the dynamic traffic modeling and probabilistic seismic hazard assessment are coupled, to link and characterize key network performance variables after extreme earthquakes and establish a multivariable seismic performance measure. The methodology is used to study the transport network in the southern part of Mexico City for a set of scenarios. The seismic environment is established through uniform hazard spectra derived for firm soil. Damage to structures is estimated considering site response and using fragility functions. Dynamic traffic modeling is developed to simulate damage-induced road closures and resulting in traffic variations. Post-earthquake network performance is evaluated through data envelopment analyses, obtaining sets of seismic performance boundaries, and seismic performance maps. The methodology offers a quantitative tool with applications in the planning of urban areas that are sustainable and seismic resilient.

Efficiency estimation of transit transfer stations (TTS) in densely populated urban areas is a mandatory step to define proper sustainable public transportation networks. However, due to the lack of data and the complexity of the operation patters, efficiency assessment is a cumbersome task in practice. This paper presents a novel approach to establish relative efficiencies in order to optimize TTS operation and design. Using the Data Envelopment Analysis method (DEA) three key efficiency dimensions are studied, which corresponds to the pillars of sustainability: technical, social and environmental. Efficiencies were computed using a database generated including 39 TTS located in Mexico City Metropolitan Area. Results show that none of the stations analysed are completely efficient considering the three dimensions simultaneously. Technically efficient TTS are those attending the highest number of passengers in a smallest given area. Social efficient TTS are those that provide enough services, such as automatization, multiple transport modes, and low transfer time, leading to high levels of users satisfaction. Finally, environmental efficient TTS correspond to those registering low values of air polluting emissions per passenger mainly due to the presence of non-oil fuel transport modes. In developing countries in Latin American and Asia, the application of this kind of models represent an excellent tool for the computation and understanding of TTS operations