Identification and Evaluation of Freight Demand Factors (2012) / Chapter Skim
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From page 34... ...
31 3. Modeling Transportation Demand – Analyzing the Results As detailed in the previous section, the research team began this analysis by first reviewing the historical literature and various recent studies on "What influences transportation demand?
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32 Table 2 – Potential Cross-Correlation of Independent Variables Re al G D P in 2 00 5 Ch ai ne d $ Re al G D P in C ha in ed 2 00 5$ / Ca pi ta Re al P er so na l C on su m pt io n Ch ai ne d 20 05 $ Re al In co m e in C ha in ed 2 00 5$ / Ca pi ta To ta l H ou si ng S ta rt s In du st ri al P ro du ct io n In de x In du st ri al M an uf ac tu ri ng In de x Pu rc ha si ng M an ag er s In de x Tr ad e W ei gh te d Fo re ig n Ex ch an ge In de x (B ro ad T ra di ng P ar tn er s)
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33 Results of this work include the statistical models that explain freight demand based on independent economic and demographic variables. A challenge in this research has been the high correlations between the independent variables themselves -- generally referred to as multicollinearity.
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34 Japanese-manufactured automobiles, many new consumer electronics manufactured in Asia became affordable to a majority of American consumers. Between 1986 and 1996, with the exception of a moderate recession in 1990-1991, the U.S.
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35 detail below) , PCA was used to compensate for the high degree of multicollinearity among the remaining independent variables.
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36 Table 3 - Correlation Ranks of Candidate Demand Factors Absolute Correlation Matrix Rail Tons Rail Ton- Miles Rail Train- Miles Rail Car- Miles Rail Rev Ton-Miles Annual Truck TonMiles Truck VMT Water Tons Water TonMiles Real GDP 6 4 6 6 3 3 3 21 4 Real GDP per Capita 8 6 8 5 6 2 2 17 7 Real Personal Consumption 7 7 9 7 7 8 8 16 3 Real Income Per Capita 9 8 11 8 8 7 7 19 6 Total Housing Starts 16 16 14 15 16 16 16 4 19 Industrial Production Index 4 1 4 3 1 4 4 20 2 Industrial Manufacturing Index 3 2 2 2 2 5 5 22 1 Purchasing Managers' Index 17 17 17 18 17 18 18 11 21 Trade Wt. Broad Cur.
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37 Correlations by mode are presented in a series of tables (see Appendix C) that take a more detailed look at the candidate variables that influence freight transportation demand.
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38 influence. As imports and exports often travel long-distances to get from/to seaports, trade has become an important explainer of railroad ton-miles.
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39 As the economy shifts – for example, as agricultural exports contribute more to the U.S. balance of trade – additional independent variables could be tested to determine if they correlate to national demand for freight transportation.
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40 Table 4 - Lag Correlations in Comparison with Prior Year Demand Measures Rail Tons Rail Ton Miles Rail Revenue Ton Miles Rail Train Miles Rail Car Miles Truck Ton Miles Truck Vehicle Miles Water Tons Water Ton Miles Candidate Independent Variables Real GDP yes yes yes no no no no no yes Real GDP per Capita yes yes yes no no no no yes yes Real Personal Consumption yes yes yes no no no no no no Real Income Per Capita yes yes yes no no no no no no Total Housing Starts yes yes yes yes yes yes yes no yes Industrial Production Index yes no no no no no no yes yes Industrial Manufacturing Index yes no no no no no no yes yes Purchasing Managers' Index yes yes yes yes yes yes yes yes yes Trade Wt. Broad Cur.
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41 models were constructed based on pr ior knowledge of freight demand trends, the correlation between dependent variables and independent variables, and statistical fitness diagnostics. The regression models tested included candidate demand factors that were believed to be important determinants of demand (through the foregoing statistical analysis as well as theoretical motivations)
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42 Table 5 – Interpretation of models using natural logarithm transformations Model Type (Dependent-Independent) Representation Interpretation Log-Actual LN(Y)
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43 Correlated Error Corrections Another concern in regression analysis is to correct for correlation among error terms from different time periods, also known as serial correlation. Serial correlation violates a fundamental regression assumption that error terms are uncorrelated.
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44 TABLE 6 - Potential Factor Weights (Log Actual Models) of Independent Variables (generated as a result of multiple regression models)
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45 Tables in Appendix D, Regression Analysis Results and Diagnostics, present the results of the regression analysis. T he appendix contains 18 tables, two for each of the nine dependent measures of freight demand.
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46 Table D-5 shows regression results for five different ways to model rail train miles. Again, the regressions fit the past very well as can be seen by the very high R2 Table D-7 shows regression results for models that predict rail car-miles.
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47 Moreover, only a limited number of heavy, low-value, high-volume commodities move via barge. Over two decades of general economic growth in the U.S., waterway transportation demand actually decreased.
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48 Principal Component Analysis Both the literature review and the analysis confirm that one of the primary challenges of researching the multiple economic, demographic, and other factors that influence freight transportation demand is that they are often similar to one another. Imports grow with exports as trade grows.
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49 factors within each category that are highly correlated could be combined. Groups of variables were developed that measured employment, consumption, production, commodity prices and foreign exchange.
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50 90% of the variance of each dependent variable (R2 Table E-14 provides the results for rail tons regressed on principal components and Table E-15 shows the associated diagnostics. The regressions accurately fit historical values as suggested by the very high R >0.90)
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51 bias model estimates and associated standard errors are reduced. Compared to other models, the apparent impact of NAFTA is far greater at 9.7%.
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52 Reliability and Representative Tests In order to evaluate the reliability and the ability of the models to predict actual freight demand, actual data was compared against the various model predictions of past periods in a technique known as backcasting. Whereas forecasting is a prediction of future levels of freight demand, backcasting looks at how well can the selected model predict historical values and informs researchers on the actual fitness of the model assuming that underlying economic relationships do not drastically change.
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53 Rail revenue ton-miles were estimated using the following function: Rail revenue ton-miles = f(PCA commodity component, PCA production component, PCA consumption component, NAFTA indicator, autoregressive correction) Data was available at the quarterly level between 1990 and 2009 Q3, which meant that the specified model could be estimated on 77 periods.
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54 Truck ton-miles, using the PCA model, were estimated via the following: Truck ton-miles = f(PCA commodity component, PCA consumption component, NAFTA indicator, autoregressive corrections for one and two periods) Figure 10 below plots the observed number of truck ton-miles as well as the base model and the randomly sampled model.
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From page 58... ...
55 100,000 120,000 140,000 160,000 180,000 200,000 220,000 240,000 1980 1985 1990 1995 2000 2005 Base (MAPE = 2.36) Sample (MAPE = 2.42)
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