A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system
This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.
Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system
This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.
Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system
2016
Article (Journal)
English
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