In this sub-chapter, predictions have been made for the period of 2019-2025 by performing trend analysis based on the transport data (in Net tonne ) published from 2001 to 2018. In this respect, the statistical grouping made for Chapter 4 within the scope of this study. As mentined before that TCDD Taşımacılık A.Ş. (TCDD Transportation JSC / TCDD-T) has been established and registered as of 14 June 2016 and has started to render rail freight and passenger services as of 1 January 2017. After this date, the statistical forecast needs to be done individualy for both companies.
6.2.1 Forecast for Rail Freight Sector
The rail freight sector have several actor after de-regulation actualisation that are incumbent operator TCDD-T and private companies such as OMSAN Logistic, Korfez Taşımacılık A.Ş etc. (TCDD, 2018). The inftastructure owner TCDD keep the overall rail transport statistics including stated companies data. In this section, the rail freight movements analysed and made forecast until 2025 by using existing data between 2001 and 2018 excluding the transition period. In this analysis, there are three trend analyses, carried out and the result shown on the Table 6.9 below.
Table 6.9 The results obtained from the trend analysis for after period
2017 2018
Actual Transported Net-tones (Thousand) 28469 31673
Estimated Transported Net-tones (BAU-BP) (Thousand)
Trendline Formula R²
(1a) Linear y = 981.24x + 13863 0.9666 30544 31525
(2a) Exponental y = 14765e0.0449x 0.9397 31602 33049
(3a) Polynomial y = -17.376x2 +1305.7x + 12792 0.9742 29967 30665
The best fit trendline on the observed data have been predicted by performing the second-degree polynomial trendline. According to this analysis, the best fitting curve on the data has been obtained primarily by transferring the existing data on the graph (see Figure 6.6). In parallel with trend analyses, the residual plot created with observed network value and forecasted network value by using the data from the selected trendline formula. (see Figure 6.7)
Figure 6.7: Estimated versus Observed Network Values
6.2.2 Forecast for the Incumbent Operator
The data belonging to TCDD-T, whose transportation data have been separated as of 2017, have been analyzed in Figure 6.8. similar with previous trend analyses. There are three main trendline formulas have used for the analyses. As a result of the
analysis shown on the Table 6.10, the polynomial (2nd degree) defined as the best fit in accordance with its highest R2 value and 2017-2018 values.
Table 6.10 Trend Analysis of Incumbent Operator for After Period
2017 2018
Actual Transported Net-tones (Thousands) 28430 28734
Estimated Transported Net-tones (BAU-BP) (Thousands)
Trendline Formula R²
(1b) Linear y = 912.99x + 14274 0.9467 29795 30708
(2b) Exponental y = 14967e0.0426x 0.9151 30810 32147
(3b) Polynomial y = -32.026x2 +15117x + 12300 0.9757 28731 29122
Figure 6.8: TCDD-T Transported Net Value- Predicted Trendlines
Similar with overall rail freight sector trend analysis, the residual plot generated by using the observed data and forecasted values from the 2nd degree polynomial trendline formula. The resiudal values range is almost ± 2 million tonne. (see Figure 6.9)
Figure 6.9: TCDD-T Estimated versus Observed Network Values
Net tonne-km values are one of the most widely-used data types within the transportation sector. These values are especially used by the sector because both the transported quantity (Net tonne) and the distance carried (Km) are significant for the cost accounting. Within the scope of this study, it has been revealed that the trends belong to the Net tonne values emerged in some years are different from the net tonne-km values. The results show the same trend in parallel with the transported net tonne values as well.
6.2.3 Impact of the Railway Reforms
TCDD and TCDD-T companies have started to show statistical differences as of 2017, with the complete realization of their organizational separation. While TCDD,
y = 1,0106x R² = 0,9586
10000 15000 20000 25000 30000 35000
10000 15000 20000 25000 30000
Estimated Transported Nettones (Thousands) (3b)
Observed Transported Nettones (Thousands)
as the owner of railway network, were keeping transport data of all the railway operator companies, which carry out their freight and passenger transports on the rail infrastructure owned by TCDD, TCDD-T has recorded its own transport data only.
As is seen from Table below, while 28.47 Mtonne freight have been carried out on the TCDD’s network in 2017, 28.43 Mtonne of this have been transported by TCDD-T. The difference of approximately 40,000 tons of freight have been carried out by new entrants such as OMSAN Logistics.
There two different forecast methodology used in the analysis. The first one is business as usual estimations (BAU) made regarding the data for 2001 to 2014, the same results shown on the Table 6.11 since TCDD-T company did not incorporated.
The result shows that BAU forecast is more appropriate for overall rail freight sector since its resource data and trends on 2017 and 2018. The second methodology for the forecast is after period (AP) demand for both TCDD and TCDD-T Companies.
The resource of this forecast is the data of 2001-2018 (excluding Transition Period) and the difference has been respectively forecasted as 1.88 Mtonne, 2.25 Mtonne and 2.64 Mtonne for the years of 2019, 2020 and 2021 between two companies. This means that the new entrants companies might have higher transport rate in the future (see Table 6.11). In this process, it is expected that the existing railway transportation activities carried out by large industrial establishments will shift to new entrants companies. Railway reforms are predicted to decrease the performance and efficiency of TCDD-T while increasing the statistical performance of TCDD, which owns the infrastructure. Considering that the main goal of railway reforms is to increase the modal share rate and shift more freight from road transport, modal share calculations will be of great importance when measuring the rate of railway reform success in the near future.
Table 6.11 The Results Obtained from the Trend Analysis for After Period