Abstract
This paper models an intercity travel demand which uses the principal structural variables that have been identified in the literature. This research develops an urban public travel demand model for 19 directional O-D city-pair network originating from Owerri Urban. Using revealed preference data from the period of 2014 to 2016 which was filtered into 207 observations operated by the 16 transport companies in Owerri, Imo State. The empirical analysis explicitly modelled the pattern of correlations among service variables by a log-linear model using OLS. The estimates yield better demand elasticities than those of direct linear models. Other empirical findings include that; overall fares elasticities are low, so that increases in fare levels will almost always lead to increases in revenue. However, it is possible for any would-be operator determine the expected patronage on any route of operation using the derived models from this paper. Thus this paper has come up with empirical model to assess the viability of intercity passenger transport operation in Nigeria. However, it will also help the operators in business to do a sensitivity analysis based of changes in the intercity passenger travel markets in Nigeria.
Highlights
Intercity travel is the travel between cities or other points of interest that are separated by some significant distance
An empirical model to assess the viability of intercity passenger transport operation in Nigeria was formulated
This study describes a demand model based on these structural factors
Summary
Intercity travel is the travel between cities or other points of interest that are separated by some significant distance. Current understanding of the demand for public transport service fails to address several significant questions: (1) what is the relative importance of causal factors (such as, trip frequency, route distance, journey time, and fare) in determining demand among routes? Studies in intercity travel demand literature usually include cost and frequency as causal factors, other factorssuch as journey time and capacity- are seldom investigated (Camagni et al, 2002). Specifying these additional causal factors allows predictions of demand response to changes in these factors, and affects the estimated effects of cost and frequency of trip
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