In the regression, different functional forms are tested, including linear, double-log, and semi-log models, and the best result is obtained from the double logarithmic models. Zamparini and Reggiani (
2007a) also use this specification in a regression analysis of European and American VFTTS studies and it has also been the preferred specification in many meta-analyses of the passenger VTTS. Thus, the coefficients of the quantitative variables will be interpreted as elasticities of the VFTTS to changes in the independent variable (notably GDP per capita). The results of the meta-model are estimated first by OLS for all the variables and reported in Table
4. Then, the WLS model is compared to the OLS model that includes only the significant variables, which can suffer from heteroscedasticity and this will be tested using the Breusch-Pagan test. The results of the WLS model are reported in Table
5 in the results section, noting that the number of observations is smaller (
n = 87) than the total number of observations in Table
4 because some studies did not report the number of observations in their studies. Thus, these studies were removed from the model. Furthermore, OLS and random effect models were estimated for the significant variables also and reported in Table
6. Concerning panel models, both the fixed and random-effects models were estimated; however, the random-effects model produced the best results and it is therefore reported here. This selection depends on the Hausman test, which is based on the differences between the estimators of the two models (Hausman
1978). Therefore, a higher value of the Hausman test indicates that the random-effects model is more appropriate than the fixed-effects model.
Table 4
OLS model for all variables (2017 prices, $ per-tonne/ hours)
Constant | − 6.54 | 0.001 | |
LOGGDP | 0.68 | 0.000 | 0.68 |
Carriers | 1.35 | 0.002 | + 286% |
Rail | − 1.77 | 0.000 | − 83% |
Sea | − 2.08 | 0.004 | − 88% |
Air | 2.68 | 0.002 | + 1359% |
Inland | − 2.03 | 0.002 | − 87% |
Non-efficient | − 1.02 | 0.029 | − 64% |
Attributes | − 0.19 | 0.795 | − 17% |
Non-SP | 0.78 | 0.377 | + 118% |
Non-CAPI | 0.01 | 0.622 | + 1% |
Pre2000 | − 0.32 | 0.394 | − 27% |
Advanced | − 0.44 | 0.273 | − 36% |
Unpublished | 0.16 | 0.724 | + 17% |
Model fit | N = 106 | R2 = 0.58 | |
Table 5
OLS and WLS model (2017 prices, $ per-tonne/ hours)
Constant | − 7.53 | 0.000 | − 7.21 | 0.005 |
LOGGDP | 0.82 | 0.000 | 0.77 | 0.002 |
Carriers | 1.64 | 0.000 | 1.25 | 0.016 |
Rail | − 1.74 | 0.000 | − 3.75 | 0.000 |
Air | 3.11 | 0.001 | 1.79 | 0.000 |
Sea | − 2.66 | 0.004 | − 3.92 | 0.001 |
Inland | − 2.27 | 0.010 | − 2.72 | 0.000 |
Model fit | N = 87 | R2 = 0.56 | N = 87 | R2 = 0.77 |
Breusch-Pagan test | Prob = 0.0003 | | Prob = 0.0000 | |
Table 6
OLS, fixed-effects and random-effects models (2017 prices, $ per-tonne/ hours)
Constant | − 7.06 | 0.000 | − 15.6 | 0.059 | − 8.03 | 0.001 | |
LOGGDP | 0.67 | 0.000 | 1.49 | 0.063 | 0.80 | 0.001 | 0.80 |
Carriers | 1.57 | 0.000 | 2.08 | 0.000 | 1.88 | 0.000 | + 555% |
Rail | − 1.78 | 0.000 | − 1.78 | 0.000 | − 1.77 | 0.000 | − 0.83% |
Sea | − 2.00 | 0.004 | − 2.29 | 0.001 | − 2.13 | 0.000 | − 0.88% |
Air | 2.26 | 0.005 | 2.71 | 0.000 | 2.67 | 0.000 | + 858% |
Inland | − 2.08 | 0.001 | − 3.39 | 0.000 | − 2.89 | 0.000 | − 0.94% |
Country variance | Na | Na | 0.69 | Na | 0.53 | Na | |
Model fit | N = 106 | R2 = 0.51 | N = 106 | R2 = 0.51 | N = 106 | R2 = 0.51 | |
Estimation discussion
The results of the meta-model including all variables are reported in Table
4. The model is estimated by the OLS, where the natural logarithm of the VFTTS is the dependent variable. The independent variables are included in the model based on the effect of each variable in previous literature.
The first variable is the natural logarithm of the GDP per capita, which has a positive sign as expected. This effect was confirmed in the previous VFTTS meta-analysis because higher GDP per capita for a country leads firms to maintain their time and competitive power by using faster deliveries (Zamparini and Reggiani
2007a). However, the income variable in this dataset is expressed by the country’s GDP per capita, which approximates the wage rate and price level in the country. In addition, transport mode indicators are considered in the model to demonstrate the impact of each mode on the VFTTS. It is expected that each transport mode will have a different VFTTS, particularly a high value for the road mode (and for air) is expected; 47 of the observations are related to road haulage. The coefficients for rail, sea, air and inland modes are compared to the road mode, which is the reference category.
The second set of indicator variables is the respondent in the survey. Investigating the difference between the decision makers in freight transportation is important, because in freight transportation, the valuation of travel time depends on who makes the decision (De Jong
2008). Therefore, the VFTTS will differ between decision makers, and it is expected that shippers might have lower values than carriers. A time indicator variable is also tested in the model to observe the changes in VFTTS over time. The expectation is that studies before 2000 might have higher values than more recent studies. One reason might be that most countries had an increase in their GDP before the year 2000 (Shires and De Jong
2009), but this should be picked up by GDP per capita. The number of attributes in each experiment is included in the model to investigate whether using attributes besides time and cost might result in higher values (Shams et al.
2017b,
a). The data type SP or RP is included in the model to determine whether the effect of these data types on the VFTTS is significant (Wardman et al.
2016).
In the model, the estimation method and experimental design were taken into consideration as they were widely applied across VFTTS studies. Because there are different methods and designs, two categories are identified to limit the number of indicators. The general expectation is that the estimation models and the experiment design might not have a direct effect on the VFTTS, as this results was also obtained in the last passenger VTTS meta-analysis (Wardman et al.
2016). It can be seen from the model reported above in Table
4 that there is a variation in the VFTTS, and the model explains around 58% of this variation. However, such variables as the data type (e.g. SP or RP), time variable, number of attributes, survey method, publication type and estimation model used in the individual studies, were found to be not significant in the model. As with Zamparini and Reggiani’s (
2007a) meta-analysis of the VFTTS, a substantial number of significant coefficients were obtained. Removal of all non-significant variables from the meta-model is applied in the next models. A detailed interpretation of the results will be undertaken for the meta-models with significant variables that influence the VFTTS.
The following meta-models regress the VFTTS on the significant explanatory variables from the previous model, and these are GDP per capita in dollars and, a set of indicator variables, such as the survey respondent (e.g. shippers or carriers) and transport mode. The experimental design indicator has a significant coefficient but was removed from the next models. This is because using different experimental designs (e.g. D-efficient design) should have an effect on the precision of the results with (a lower standard error), but not on the VFTTS estimate itself (Hess and Rose
2009; Bliemer and Rose
2011).
The model is estimated first by OLS, and the WLS model for studies reporting the number of observations. Because the estimated VFTTS might vary considerably in precision, there might be heteroscedasticity problems. Therefore, estimating the meta-model by OLS solely can lead to biased estimates of the coefficients. To solve this issue, the WLS model has been applied to weigh each VFTTS with a measure of its precision. Unfortunately, most VFTTS studies in the dataset did not report the standard errors, which are needed to compute the inverse variance weight. Therefore, we use the number of observations of a survey as a proxy of precision of the VFTTS in the analysis.
Table
5 shows the results of the OLS and WLS models and the results indicate that the estimated parameters from the weighted model are similar in sign and significance level to the OLS estimates; however, the magnitude of the transport mode indicator variables are noticeably different. In addition, a Breusch-Pagan test indicates that both the WLS and OLS model suffer from heteroscedasticity and therefore OLS with robust standard error is preferred compare to WLS model. This might be because of using the number of observations as analytical weights instead of using more conventional inverse variance weighting.
Relying on the OLS model only could affect the precision of the results due to the correlation between observations from the same country or study. Therefore, the meta-model is estimated by the random-effects model and compared to the OLS for all the observations. It can be seen from Table
6 below that there is variation in the VFTTS, and the model explains approximately 51% of this variation. The other 49% of the variation will depend on other characteristics not included in the model.
First, note that the signs of the coefficients across all models are similar with a slightly higher magnitude of all the coefficients in the random-effects model when compared to the OLS model. The Hausman test is used to compare the two estimators derived from the fixed and random-effects models, indicating better performance of the random-effects model. The results of the test gave a p-value of 0.6901 (χ2 = 3.90), and the null hypothesis cannot be rejected. Therefore, the preferred model is the random-effects model. It accounts for the nature of the dataset that contains multiple values from the same country. From this point onward, the results produced by this model will be discussed.
A strong relationship between the value of travel time and income has been reported in the literature (Waters
1994). The income variable, which is a key influential variable, is estimated and has a positive relationship to the VFTTS. The estimated elasticity is close to unity and implies that a 1% increase in country income is associated with a 0.80% increase in the VFTTS, which is significantly different from zero at the 5% level. This is slightly higher than the income elasticity of 0.68 estimated in the previous VFTTS meta-analysis (Zamparini and Reggiani
2007a). The role of income is definitely an important issue for administrators when developing transport infrastructure and project evolution (Waters
1994). Because an income elasticity of one for the VFTTS is often used in practice, we need to test this elasticity (Fosgerau
2005). The assumption of an income elasticity equal to one was tested and found to be not significantly different from unity with
p-value = 0.416 at the 5% significance level.
The first set of indicators included in the model distinguishes between different respondents of the survey—whether shipper or carrier. The carrier variable has a significant coefficient and a higher value than that of the shipper variable, which is the base category. This is expected because each respondent makes a different decision and has a different valuation of time (De Jong et al.
2014). The VFTTS will vary depending on the decision maker, and it is expected that shippers, who consider cargo costs, might have a lower travel time value than carriers, who consider transport costs, when they make trade-offs between time and cost (Halse et al.
2010; Shams et al.
2017b,
a).
Furthermore, the transport mode indicators have a significant coefficient for all four indicators included in the model. As expected, the air mode has a higher value with an impact 14 times larger than the road omitted category, while the remaining indicators have lower values. The rail, sea and inland coefficients have a similar impact with approximately 83%, 88% and 94% lower values than the road category, respectively. This is in line with De Jong et al. (
2014), who found that the VFTTS for rail, sea and inland have lower values than the VFTTS for the road mode, with roughly the same percentage. The results of the transport mode indicators have a similar coefficient sign as in passenger transport, with higher values for the air mode (Zamparini and Reggiani
2007b). The higher value for the air mode might be because the shipments usually contain high-value and time-sensitive products (Alkaabi and Debbage
2011). As the air variable in the random-effects model has a large influence, we tested and estimated the model without air observations. However, the model that includes an air variable provides a better fitting model, whereas other transport mode coefficients in both models were similar. Therefore, the random-effects model, reported in Table
6, will be used for calculating the implied VFTTS for various countries.
Application of the estimated meta-model
The estimated meta-model can yield an implied VFTTS for different countries, particularly those with no evidence or official values. The main purpose of estimating the implied values is to obtain the VFTTS for each country by different transport modes and types of respondents. The VFTTSs based on the meta-model will be compared with those in the literature to verify that the values are within the range of the reported VFTTS. Therefore, this comparison provides more confidence in the meta-model in predicting VFTTS for each country around the world. Furthermore, the estimated meta-model proposes a provisional value for countries without a VFTTS to be used in their transport projects.
Table
7 presents the VFTTS for different transport modes and refers to carriers or shippers in various countries. The selection of countries based on the country’s GDP. The countries in the world with the biggest total GDP are included. In application, the random-effects model estimation, reported in Table
6, was used. The GDP per capita in dollars of each country was ascertained from the 2017 statistical records of the World Bank statistics. The variance in countries was set to zero, and each coefficient in the model was used to calculate specific mode values. This gives specific VFTTS for each country segmenting by five transport modes and two types of respondents.
Table 7
Implied values of travel time in freight transport ($ per-tonne/ hour, 2017 incomes and prices)
Argentina | 14,592 | 4.27 | 0.73 | 61.59 | 0.51 | 0.24 | 0.65 | 0.11 | 9.40 | 0.08 | 0.04 |
Australia | 54,094 | 12.17 | 2.07 | 175.70 | 1.45 | 0.68 | 1.86 | 0.32 | 26.81 | 0.22 | 0.10 |
Austria | 47,381 | 10.94 | 1.86 | 158.03 | 1.30 | 0.61 | 1.67 | 0.28 | 24.11 | 0.20 | 0.09 |
Bangladesh | 1564 | 0.71 | 0.12 | 10.32 | 0.08 | 0.04 | 0.11 | 0.02 | 1.57 | 0.01 | 0.01 |
Belgium | 43,507 | 10.22 | 1.74 | 147.61 | 1.21 | 0.57 | 1.56 | 0.27 | 22.52 | 0.19 | 0.09 |
Brazil | 9881 | 3.12 | 0.53 | 45.09 | 0.37 | 0.18 | 0.48 | 0.08 | 6.88 | 0.06 | 0.03 |
Canada | 45,070 | 10.51 | 1.79 | 151.83 | 1.25 | 0.59 | 1.60 | 0.27 | 23.17 | 0.19 | 0.09 |
Chile | 15,037 | 4.37 | 0.74 | 63.09 | 0.52 | 0.25 | 0.67 | 0.11 | 9.63 | 0.08 | 0.04 |
China | 8759 | 2.84 | 0.48 | 40.95 | 0.34 | 0.16 | 0.43 | 0.07 | 6.25 | 0.05 | 0.02 |
Colombia | 6376 | 2.20 | 0.37 | 31.76 | 0.26 | 0.12 | 0.34 | 0.06 | 4.85 | 0.04 | 0.02 |
Czech Republic | 0380 | 5.57 | 0.95 | 80.47 | 0.66 | 0.31 | 0.85 | 0.14 | 12.28 | 0.10 | 0.05 |
Denmark | 57,219 | 12.73 | 2.17 | 183.77 | 1.51 | 0.71 | 1.94 | 0.33 | 28.04 | 0.23 | 0.11 |
Finland | 45,810 | 10.65 | 1.81 | 153.82 | 1.27 | 0.60 | 1.63 | 0.28 | 23.47 | 0.19 | 0.09 |
France | 38,679 | 9.30 | 1.58 | 134.35 | 1.11 | 0.52 | 1.42 | 0.24 | 20.50 | 0.17 | 0.08 |
Germany | 44,681 | 10.44 | 1.78 | 150.78 | 1.24 | 0.59 | 1.59 | 0.27 | 23.01 | 0.19 | 0.09 |
Hong Kong | 6221 | 10.73 | 1.83 | 154.93 | 1.27 | 0.60 | 1.64 | 0.28 | 23.64 | 0.19 | 0.09 |
India | 1981 | 0.86 | 0.15 | 12.47 | 0.10 | 0.05 | 0.13 | 0.02 | 1.90 | 0.02 | 0.01 |
Indonesia | 3837 | 1.47 | 0.25 | 21.16 | 0.17 | 0.08 | 0.22 | 0.04 | 3.23 | 0.03 | 0.01 |
Ireland | 69,650 | 14.89 | 2.54 | 215.08 | 1.77 | 0.84 | 2.27 | 0.39 | 32.82 | 0.27 | 0.13 |
Israel | 40,544 | 9.66 | 1.65 | 139.51 | 1.15 | 0.54 | 1.47 | 0.25 | 21.29 | 0.18 | 0.08 |
Italy | 32,155 | 8.03 | 1.37 | 115.89 | 0.95 | 0.45 | 1.22 | 0.21 | 17.68 | 0.15 | 0.07 |
Japan | 38,332 | 9.24 | 1.57 | 133.38 | 1.10 | 0.52 | 1.41 | 0.24 | 20.35 | 0.17 | 0.08 |
Malaysia | 10,117 | 3.18 | 0.54 | 45.95 | 0.38 | 0.18 | 0.49 | 0.08 | 7.01 | 0.06 | 0.03 |
Korea | 29,743 | 7.54 | 1.28 | 108.88 | 0.90 | 0.42 | 1.15 | 0.20 | 16.61 | 0.14 | 0.06 |
Mexico | 9281 | 2.97 | 0.51 | 42.89 | 0.35 | 0.17 | 0.45 | 0.08 | 6.54 | 0.05 | 0.03 |
Netherlands | 48,555 | 11.16 | 1.90 | 161.15 | 1.33 | 0.63 | 1.70 | 0.29 | 24.59 | 0.20 | 0.10 |
Nigeria | 1969 | 0.86 | 0.15 | 12.40 | 0.10 | 0.05 | 0.13 | 0.02 | 1.89 | 0.02 | 0.01 |
Norway | 75,704 | 15.92 | 2.71 | 229.91 | 1.89 | 0.89 | 2.43 | 0.41 | 35.08 | 0.29 | 0.14 |
Pakistan | 1467 | 0.68 | 0.12 | 9.80 | 0.08 | 0.04 | 0.10 | 0.02 | 1.50 | 0.01 | 0.01 |
Philippines | 2982 | 1.20 | 0.20 | 17.29 | 0.14 | 0.07 | 0.18 | 0.03 | 2.64 | 0.02 | 0.01 |
Poland | 13,861 | 4.09 | 0.70 | 59.11 | 0.49 | 0.23 | 0.62 | 0.11 | 9.02 | 0.07 | 0.04 |
Russian Federation | 10,751 | 3.34 | 0.57 | 48.24 | 0.40 | 0.19 | 0.51 | 0.09 | 7.36 | 0.06 | 0.03 |
Saudi Arabia | 20,804 | 5.67 | 0.96 | 81.80 | 0.67 | 0.32 | 0.86 | 0.15 | 12.48 | 0.10 | 0.05 |
Singapore | 60,298 | 13.27 | 2.26 | 191.64 | 1.58 | 0.75 | 2.03 | 0.34 | 29.24 | 0.24 | 0.11 |
South Africa | 6127 | 2.13 | 0.36 | 30.77 | 0.25 | 0.12 | 0.33 | 0.06 | 4.69 | 0.04 | 0.02 |
Spain | 28,208 | 7.23 | 1.23 | 104.37 | 0.86 | 0.41 | 1.10 | 0.19 | 15.93 | 0.13 | 0.06 |
Sweden | 53,253 | 12.02 | 2.05 | 173.51 | 1.43 | 0.67 | 1.83 | 0.31 | 26.48 | 0.22 | 0.10 |
Switzerland | 80,333 | 16.70 | 2.84 | 241.09 | 1.98 | 0.94 | 2.55 | 0.43 | 36.79 | 0.30 | 0.14 |
Thailand | 6578 | 2.26 | 0.38 | 32.56 | 0.27 | 0.13 | 0.34 | 0.06 | 4.97 | 0.04 | 0.02 |
Turkey | 10,500 | 3.28 | 0.56 | 47.34 | 0.39 | 0.18 | 0.50 | 0.09 | 7.22 | 0.06 | 0.03 |
United Arab Emirates | 40,325 | 9.62 | 1.64 | 138.90 | 1.14 | 0.54 | 1.47 | 0.25 | 21.20 | 0.17 | 0.08 |
United Kingdom | 39,932 | 9.54 | 1.63 | 137.82 | 1.13 | 0.54 | 1.46 | 0.25 | 21.03 | 0.17 | 0.08 |
United States | 59,928 | 13.21 | 2.25 | 190.70 | 1.57 | 0.74 | 2.02 | 0.34 | 29.10 | 0.24 | 0.11 |
Average | 7.23 | 1.23 | 104.37 | 0.86 | 0.41 | 1.10 | 0.19 | 15.93 | 0.13 | 0.06 | |
Table
7 indicates that the implied values are in line with those obtained in previous studies and seem consistent with research that considered shippers values. These studies reported a higher VFTTS for roads than those derived for other transport modes, except air mode, which has the highest VFTTS (De Jong et al.
2004, De Jong et al.
2014). Large variations in the VFTTSs were observed for different modes and type of survey respondents, but these variances were expected because of the dissimilarities in GDP levels amongst the countries (Column 2, Table
7). Variations within each country occurred in accordance with transport mode and type of survey respondent, leading to the conclusion that the VFTTS for countries with high GDPs is higher than that of countries with low GDPs. This pattern was also observed in passenger transport, as was a higher VTTS for the air mode (Wardman et al.
2016). The average VFTTS of carriers on roads is 7.23 per- tonne/ hour, whereas that of shippers is 1.10. These values are within the range reported in primary studies as measured by the 95% confidence interval.