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Guide to COST-BENEFIT ANALYSIS of investment projects - Ramiri

Guide to COST-BENEFIT ANALYSIS of investment projects - Ramiri

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Furthermore, travel demand depends on the locations <strong>of</strong> activities and families, and therefore trends in thedistribution <strong>of</strong> economic activities by sec<strong>to</strong>r and population should also be considered. Location patterns affect no<strong>to</strong>nly the distance travelled, but also the frequency <strong>of</strong> trips and thus the <strong>to</strong>tal demand. Accessibility is one fac<strong>to</strong>raffecting the choices <strong>of</strong> firms and families about where <strong>to</strong> locate, and as a consequence <strong>of</strong> these choices the ‘withproject’ and ‘without project’ demand may not be the same.The price <strong>of</strong> the service provided is not the only determinant <strong>of</strong> travel demand. The choice <strong>of</strong> how much travel <strong>to</strong>consume or how far <strong>to</strong> ship a good depends on the travel cost and the time spent in travelling. Elasticity <strong>to</strong> traveltime is a further determinant <strong>to</strong> be introduced in travel demand predictions. As for price elasticity, also in the case <strong>of</strong>travel time, direct and cross-elasticity are relevant. Demand for a specific mode <strong>of</strong> transport can be influenced by anincrease in the speed <strong>of</strong> that mode, but also by an increase/decrease in the speed <strong>of</strong> the competing mode(s).Demand characteristics, price, income and cross elasticity, value <strong>of</strong> time, value attributable <strong>to</strong> comfort for passengersand damage for freight will vary with the different segments <strong>of</strong> the market, as will the transport costs, type <strong>of</strong> servicedemanded etc. It is therefore extremely useful <strong>to</strong> disaggregate travel demand in<strong>to</strong> homogeneous segments. Thecharacteristics <strong>of</strong> the different type <strong>of</strong> commodities, the income group <strong>to</strong> which the individuals belong as well as thepurpose <strong>of</strong> the trip are important determinants in predicting travel demand 106 .106 Despite the considerable experience and the wide range <strong>of</strong> techniques available, forecasting transport demand remains a challenging task.Recent studies (Flyvberg et al., 2006) found considerable deviations between forecast and actual traffic volumes in more than 200 large-scaletransport <strong>projects</strong>. Forecast inaccuracy is <strong>of</strong>ten higher in rail than in road <strong>projects</strong>. This is not <strong>to</strong> say that road forecasts are always accurate; in fact,the rate <strong>of</strong> inaccuracy in road <strong>projects</strong> is significant, but it is more balanced between over- and underestimation. For rail travel the inaccuracies aresystematically higher and overestimates are the rule. Many fac<strong>to</strong>rs contribute <strong>to</strong> making rail travel forecasts less accurate than road travel forecasts:railway <strong>projects</strong> are, in general, bigger in size (but a study on aviation showed no correlation between size and demand forecast inaccuracy), andhave a longer implementation phase. However, overestimation <strong>of</strong> rail traffic seems <strong>to</strong> be linked <strong>to</strong> an overoptimistic expectation <strong>of</strong> modal shift.204

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