Agent-Based Modeling for Evaluation of Transportation Mode Selection in the State of Guanajuato, Mexico

One of the negative consequences of the industrialization of Mexico favored by the North American Free Trade Agreement (NAFTA), is the emergence of huge industrial corridors associated with the demand for mobility by commuters who move to their workplace. The demand produces mobility patterns that have a serious impact on air pollution in five cities in the state of Guanajuato that, despite being medium in size, outnumber Mexico City in pollution. The objective of this work is to model a data-driven agent based on the beliefs-desires-intentions model, to predict the selection of transport modes using a J48 decision tree algorithm that was designed from data from the 2015 national census (INEGI). The method is mode based l agent programmed in Net logo. The results show that: it is possible to predict the demand of transport considering the: gender, level of education, transfer times and age in the five cities of Guanajuato, in a horizon of three years. With changes in public policies related to mobility and changes in transportation patterns, air pollution would be reduced. The proposed model could be used to support public policies that improve mobility and positively impact air quality in five cities in the state of Guanajuato.