Trip generation modeling for forecasting travel demand of rapidly expanding Cities. A case of Lira City.

Abstract

Transportation planning which involves trip generation, trip distribution, modal split, and network assignment processes is very important in urban areas for matching travel demand and transport facilities. Most transportation studies have been undertaken in developed countries but few or none in developing countries due to budget constraints. Direct transferability of existing trip generation models from the developed world to the developing countries is inappropriate as it yields poor results. This caused a need for a local trip generation model for a newly created Lira City located in Northern Uganda. The study used a linear regression technique to develop a trip generation model for forecasting the travel demand of rapidly expanding cities - the case of Lira City. The study collected primary data from 30 traffic analysis zones (TAZs) through household surveys and questionnaires. Household demographics, socio-economic attributes, and trip purposes were used to determine the number of trips generated daily in Lira City. A total of 1,813 households was used for analysis. The study revealed that out of 11,494 trips made daily, 32.3%, 29% 28.1%, and 10.6% were for school, shopping, work, and recreation purposes respectively. The household size, number of persons schooling, and male heads of households highly influenced trip production. The long trip production model had a coefficient of determination R2 value of 0.74 and root mean square error (RMSE) of 0.93. The R2 value is used to determine the goodness of fit. An R2 value of 0.5 is regarded as the standard. An R2 value below 0.5 is bad, and one above 0.5 is good. The higher it is, the better. To minimize costs for data collection, the researcher developed a short trip production model as a function of variables from the Uganda Bureau of Statistics, and had a R2 value of 0.73 and RMSE of 0.94. Both models were acceptable for predicting the daily number of trips. The long-trip production model slightly performed better since its RMSE was closer to zero than the short-trip production model. Lira City has limited resources for collecting more household data than what the Uganda Bureau of Statistics provides, such as household income and, the number of car and driving licenses owned; therefore, a short trip production model can be used to estimate the number of trips generated. The trip attraction model was developed as a function of the number of employment opportunities, floor area, number of cars owned, and parking area and had an R2 value of 0.56. It is recommended that Lira City prioritizes the planning of transport infrastructure in Anyomorem, Bazza, Boke, Ongica, and Railway quarters as trip generation rates are high in these zones. Under budget constraints, Lira City should use household data from the Uganda Bureau of Statistics on the short trip generation model. Lira City should partner with the private sector to establish good education institutions and commercial services in every administrative unit/zone to minimize home-based trips attracted to the central business city.