Abstract
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.
Highlights
Transportation is vital to people, goods, and services to transport from a place to another under a desirable condition
After feeding the collected data of the explained and the explanatory variables into the SPSS package, trip time models are developed for public transport users' trips made before 8:00 AM, between 8:00 AM to 9:00 AM, 9:00 AM to 12:00 PM, 12:00 AM to 4:00 PM, and after 4:00 PM through linear regression analysis
The value of mean square error (MSE) and root mean square error (RMSE) directed that the model could predict the number of trips made before 8:00 AM per household in Hawasss city with the average squared error of 14.51 and average deviation of 3.81 trips from the actual number of trips, respectively, since the model is said to be a good model
Summary
Transportation is vital to people, goods, and services to transport from a place to another under a desirable condition. Transportation planning is needed to estimate the travel demand behavior that will face in the future This is because the travel demand behavior of the people is affected by different factors which are making the trips dynamic through time [1]. The most commonly used transportation planning method from the 1950s until today was the four analytical steps method This method had four sub-models describing trip generation, trip distribution, modal split, and route assignment [2]. Trip generation means predicting and determining the volume of trips produced by and attracted to a geographical district [3]. Those volumes of trips depend on various factors. The basic concept remains the same, whatever the type of trip we are studying [5]
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