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Public transportation services often face challenges in middle- and low-income nations from both public acceptance and economic constraints. Jordan is classified as a middle-income country with a population of over 10 million, 4 million of whom live in Amman, the capital. The development and operation of a bus rapid transit (BRT) system in Amman city was recently proposed. The BRT project is anticipated to offer a solution to the city’s escalating congestion problem. This study’s objective is to conduct a “before” analysis to identify the variables that affect the willingness of people to use the Amman BRT system. The socioeconomic characteristics and travel habits of individuals were used to model the willingness to use BRT. An online survey was distributed to Amman residents and 238 valid responses were returned. Two popular techniques were utilized: binary logistic regression and Bayesian networks. Ten models were developed: one binary logistic model and nine Bayesian network models. The results of these models were compared based on accuracy, sensitivity, specificity, area under the Receiver Operating Characteristic (ROC) curve, complexity, and number of selected variables. It was found that Bayesian networks were more effective in modeling willingness to use BRT. Willingness to use BRT was shown to be higher among households without cars, youths, females, and university students, and if there were fewer transfers along the route. It became clear that introducing a new public transportation system is well appreciated, particularly in areas with low income, insufficient existing public transportation services, and where driving a car is the norm.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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