Predictive modeling is the key fundamental method to study passengers’ behavior in transportation research. One of the limited studied topic is modeling of public transport usage frequency, which can be used to estimate present and future demand and users’ trend toward public transport services. The artificial intelligence and machine learning methods are promising to be better substitute to statistical techniques. No doubt, traditionally been used econometrics models are better for causal relationship studies among variables, but they made rigid assumptions and unable to recognize the pattern in data. This paper aims to build a predictive model to solve passengers’ classification, and public transport usage frequency using socio-demographic survey data. The supervised machine learning algorithm, K-Nearest Neighbor (KNN) applied to build a predictive model, which is the better machine learning method for dealing with small datasets, because of its ability of having less parameter tuning. Survey data has been used to train and validate the model performance, which is able to predict public transport usage frequency of future users of public transport. This model can practically be used by public transport agencies and relevant government organizations to predict the public transport demand for new commuters before introducing any new transportation projects.

The purpose of this study is to recognize the key issues in engineering construction management (ECM) under OBOR in Pakistan, to assess their impact on success of ECM projects and to draw suggestions about solutions to these issues. Three major issues named as "time-related issues, budget-related issues and employee-related issues" are recognized in ECM projects and then their impact on success of ECM projects is assessed. Data were collected from 202 CPEC officials through structured questionnaire and analysis was run to check the hypotheses of current study. The findings revealed that there is significant negative impact of employee-related issues, budget-related issues and time-related issues on the ECM project success as these issues create different managerial problems in projects, which causes delay the completion of projects and hampers the success of projects. The solution of these problems is provided in current study. This study is first of its type that has identified and analyzed these three key issues in ECM projects under OBOR in Pakistan in terms of their effect on success of those projects. The current study has noteworthy implications in theoretical and practical terms.