Developing a Trip Generation Model for South Tripoli Using Linear Regression Analysis: A Case Study of Residential Travel Behavi
DOI:
https://doi.org/10.65405/phywk519Keywords:
Trip generation; South Tripoli; linear regression; household travel behavior; transportation planning; socio-economic variables; Traffic Analysis Zones (TAZs); Libya.Abstract
South Tripoli has experienced substantial demographic, economic, and spatial transformations over the past decade, leading to significant increases in travel demand and pressure on its urban road network. Efficient transportation planning requires accurate estimation of household-level trip production, yet cities in Libya continue to lack locally calibrated trip generation models derived from empirical socio-economic data.
This study develops a trip generation model for South Tripoli using multiple linear regression analysis, relying on primary household survey data collected from residential zones in the southern suburban districts. A stratified random sampling technique was implemented across defined Traffic Analysis Zones (TAZs), yielding a final sample of 430 households. Household socio-economic characteristics—including family size, number of workers, income levels, vehicle ownership, age structure, and licensed drivers—were evaluated as predictors of total trip production and specific trip purposes. Results indicate that the overall trip generation model achieves a strong explanatory power, with an R² of 0.44, suggesting that approximately 44% of the variance in household trip production is explained by the socio-economic variables. The most influential predictors were: number of workers, number of licensed drivers, household size, and number of qualified/educated persons. These findings align with earlier studies in both developed and developing contexts, where economic activity and mobility capacity strongly correlate with trip frequency.
The study provides a locally calibrated and statistically validated framework for urban planners in Tripoli, enabling more reliable forecasting of travel demand and supporting future infrastructure investment decisions. It also recommends building nonlinear and machine learning–based extensions to improve predictive accuracy in evolving Libyan citie.
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