scholarly journals Modeling Public Transport Users’ Trip Production in Hawassa City, Ethiopia

2021 ◽  
Author(s):  
Girma Gebre ◽  
Emer Tucay Quezon
Keyword(s):  
Public Transport ◽  
Trip Production

scholarly journals Modeling Public Transport Users’ Trip Production in Hawassa City, Ethiopia

2021 ◽  
Author(s):  
Girma Gebre ◽  
Emer Tucay Quezon
Keyword(s):  
Linear Regression ◽  
Public Transport ◽  
Traffic Management ◽  
Regression Models ◽  
Explanatory Power ◽  
Linear Regression Models ◽  
Household Level ◽  
Trip Purpose ◽  
Production Models ◽  
Trip Production

Today, overcrowded public transport demand, resulting in huge costs in an urban area. Similarly, there are a lot of people who use public transport in Hawassa city. This study aimed to develop public transport users' trip production models at the household level. Some socio-economic characteristics and trip detail of the public transport users were collected randomly from the different households through a questionnaire survey. The data gathered was fed into IBM SPSS package version 20 to develop linear regression models. The developed models are associated with trips for purpose and time intervals of trips made. The developed linear regression models, general trips, work trips, educational trips, and trips made before 8:00 AM and after 4:00 PM had good explanatory power. The value of explanatory power comprised of 0.656, 0.722, 0.549, 0.610 and 0.510. These values indicated the explanation power of the socio-economic characteristics on the trips made. It means the daily trips production was significantly affected by the number of working individuals, the different age brackets, cars and motorcycles, and the monthly income per household. The most frequent public transport users’ trips production regarding the trip purpose and time are work trips and occurred after 4:00 PM. This scenario represented a good model developed in this study. Hence, it is suggested that Hawassa city’s traffic management office use the developed models to predict the future trips demand to provide a proper scheme to avoid congestion during the peak hour of the day.

scholarly journals MODELING PUBLIC TRANSPORT USERS’ TRIP PRODUCTION IN HAWASSA CITY, ETHIOPIA

2021 ◽  
Vol 12 (2) ◽  
pp. 75-90
Author(s):  
Girma Gebre ◽  
Emer T. Quezon
Keyword(s):  
Linear Regression ◽  
Public Transport ◽  
Traffic Management ◽  
Regression Models ◽  
Explanatory Power ◽  
Linear Regression Models ◽  
Household Level ◽  
Trip Purpose ◽  
Production Models ◽  
Trip Production

Today, overcrowded public transport demand, resulting in huge costs in an urban area. Similarly, there are a lot of people who use public transport in Hawassa city. This study aimed to develop public transport users' trip production models at the household level. Some socio-economic characteristics and trip detail of the public transport users were collected randomly from the different households through a questionnaire survey. The data gathered was fed into IBM SPSS package version 20 to develop linear regression models. The developed models are associated with trips for purpose and time intervals of trips made. The developed linear regression models, general trips, work trips, educational trips, and trips made before 8:00 AM and after 4:00 PM had good explanatory power. The value of explanatory power comprised of 0.656, 0.722, 0.549, 0.610 and 0.510. These values indicated the explanation power of the socio-economic characteristics on the trips made. It means the daily trips production was significantly affected by the number of working individuals, the different age brackets, cars and motorcycles, and the monthly income per household. The most frequent public transport users’ trips production regarding the trip purpose and time are work trips and occurred after 4:00 PM. This scenario represented a good model developed in this study. Hence, it is suggested that Hawassa city’s traffic management office use the developed models to predict the future trips demand to provide a proper scheme to avoid congestion during the peak hour of the day.

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