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Developing an Artificial Neural Network Mode-Choice Model for Sharjah University City, UAE

The main goal of this study is to develop an Artificial Neural Network (ANN) mode-choice model for Sharjah University City (SUC) in the United Arab Emirates. A travel survey was conducted in 2018/2019 to collect travel preferences among travelers onto SUC. Over 4,200 respondents indicated their preferences as they traveled to the campus. The multilayer perceptron (MLP) neural network models were trained by a backpropagation algorithm to predict the mode choice. The following eight input variables were utilized: Trip duration, Trip distance, Car-ownership, Gender, Carsharing, Citizenship, Academic status, and the number of cars per household. The output variable was the chosen mode of transportation, and the following five modes were considered: car, taxi, university of Sharjah shuttle, SUC public bus, and walking. The best architecture of the ANN model was decided to have a 1-Hidden Layer with 5-Neurons. This was achieved using SPSS after trying over 150 explorative models. The main finding of this study is that the ANN model achieved high accuracy of 95%. Using the ANN model, a variable importance analysis was conducted, revealing that the most powerful predictors of the ANN mode choice model for SUC are Trip duration, Car-ownership, and Trip distance. Therefore, we recommend adopting ANN models for mode-choice instead of the classical models. ANN proved to be highly accurate, less dependent on any statistical assumptions, and can handle even nonlinear data. Several recommendations were presented for future research as well as for professionals.