<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method

MoE Key Laboratory of Complex System Analysis and Management Decision

Transport technic and technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Predictive modeling is the key fundamental method to study passengers’ behavior in transportation research. One of the limited studied topic is modeling of...

Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method

Pubblicato online: 30 dic 2019

Pagine: 1 - 7

DOI: https://doi.org/10.2478/ttt-2019-0005

Parole chiave
machine learning, modeling, public transport, socio-demographic status, Hyderabad

© 2019 Nabeel Shakeel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method

Pubblicato online: 30 dic 2019

Pagine: 1 - 7

DOI: https://doi.org/10.2478/ttt-2019-0005

Parole chiavemachine learning, modeling, public transport, socio-demographic status, Hyderabad

© 2019 Nabeel Shakeel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
machine learning, modeling, public transport, socio-demographic status, Hyderabad