IMPACT ANALYSIS OF FREIGHT TRANSPORT SCENARIOS ON THE GERMAN TRANSPORT SYSTEM – AN INDICATOR BASED APPROACH
Niels Schmidtke1, Fabian Behrendt2
1M.Sc., 2PhD
Otto von Guericke University Magdeburg, Institute of Logistics and Material Handling Systems
Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg
INTRODUCTION
Increasing globalization, personal mobility and freight transport are basic requirements for economic growth and the prosperity of a society [1]. Due to its central location in Europe and wide economic interrelation, especially Germany strongly dependents on an efficient transport infrastructure [2]. According to the Logistics Performance Index (LPI), which identifies the efficiency of logistics by a survey among leading logistics experts, Germany is ranked first in 2016 [3]. Regarding the infrastructure, however, Germany is only in the eighth place in the evaluation of the “infrastructure” pillar of the Global Competitiveness Index (GCI), tendency declining [4].
This increasing loss of the German transport infrastructure has already been identified by the commission “Zukunft der Verkehrsinfrastrukturfinanzierung” (2012) with an annual financing gap of billion EUR7.2 billion to upgrade existing infrastructure and to reduce neglected investments [5]. At the same time, within the German transport system the transport performance has shown a continuous increase [6] and thus a need for efficient and effective traffic. Therefore, it is important to estimate the impact of traffic policy measures and technological innovations to give recommendations for politics.
In literature there is a great variety of scientific work dealing with approaches to measure the performance of transport infrastructure development. Existing procedures such as LPI or GCI are often characterized by an ex post evaluation. In contrast, the Multi-Actor Multi-Criteria Analysis (MAMCA) e.g. as an methodology for ex ante assessments evaluates different policy measures, but is used only for urban and regional developments [7][8]. The following noval approach analyses ex ante assessments of traffic policy measures and technological innovations at the national (macro) level.
DESCRIPTION OF THE INDICATOR BASED APPROACH
A number- and forecast-based tool has been developed to compare and evaluate different future scenarios of freight transport systems [9], in order to provide appropriate decision support. The impact analysis is realized by the application of a procedure model which consists of the following five procedure steps:
I. System definition II. System analysis
III. Conception of scenarios
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IV. Calculation of scenarios V. Comparison of scenarios
In the core of the procedure model, a classification of key performance indicators (KPI) is compiled and analyzed by means of a relevance analysis to assess the importance and significance of defined objectives. For this approach the objectives and associated objectives of the “Bundesverkehrswegeplan 2030” (BVWP 2030), a national planning instrument for the German transport infrastructure, are used [10]. In total, 25 different KPI (e.g. transport volume, average transport distance) are checked towards the objectives such as ‘modernization and maintenance of transport infrastructure’ or
‘reducing transport costs’ [10]. A subsequent effects analysis identifies the qualitative relationships between all relevant KPI in order to set up an effect structure for each transport carrier (road, railroad and waterway). Regarding the forecast function and based on historical data, all KPI are forecasted by using appropriate forecasting methods such as linear regression analysis or exponential smoothing. As a result the tool allows the comparison of scenarios and the analysis of influencing factors on freight transport systems by means of a derived calculation scheme and its comparative indicator named
“VLV-indicator” (see figure 1).
Figure 1: Calculation scheme [9]
Against the background of heterogeneous data quality, the calculation scheme provides a comparative indicator which gives a statement about how a scenario behaves in comparison to the forecast as a baseline assumption. The percentage deviations as an essential measurement criterion of the calculation scheme are multiplied by weighting factors which result from the relevance analysis compared to the defined objectives of the BVWP 2030 [10]. Finally the “transport carrier indicators” are combined in a comparative indicator (“VLV-indicator”) taking the specific distribution of traffic (modal split shares per type of transport) into account. As a result, the amount of the comparative indicator shows for which percentage the entire transport system is influenced positively or negatively related to the forecast scenario of the forecast year. For a deeper understanding of the procedure model and the calculation scheme it is referred to [9].
APPLICATION – FREIGHT TRANSPORT SCENARIOS
As a first step of analyzing different freight transport scenarios the transport system must be defined. In the first procedure step, I. system definition, the considered system is limited by the following characteristics shaded (see figure 2). The consideration of freight transport with the transport carriers road, railroad and waterway are focused with a view on a national area, the Federal Republic of Germany, at a long-term period for the scenarios.
Figure 2: I. System definition of the freight transport model
Regarding this consideration with the mentioned transport carriers it is necessary to develop appropriate KPI schemes within the second procedure step, II. system analysis.
The effect structure for the transport carrier road is shown in figure 3 as an example which is a result of the performed relevance analysis and effects analysis. Similar effect structures exist for the other transport carriers railroad and waterway, consisting partly of other transport-specific KPI and different relation evaluations.
Figure 3: Example: Effect structure – Transport carrier Road
The third procedure step, III. conception of scenarios, includes the definition of future scenarios. As the tool allows an ex ante assessment of both, traffic policy measures, to strengthen the transport system through new financing instruments, and technological innovations, mainly to increase the efficiency of transport processes, these two issues are considered. The first defined scenario addresses the expansion of the toll for heavy goods vehicles (HGV) on all German roads and all commercial motor vehicles over 3.5t gross weight, which has been identified as a financing instrument by the mentioned commission [5] (currently in Germany toll is charged only for trucks over 7.5t gross weight, for highways and some heavily frequented federal roads). Due to the recently finished field trail for increasing truck size [11] the introduction of a Gigaliner as a technological innovation is defined as a second scenario to analyze the impact on the transport system.
Taking possible modal shift impacts into account the following scenarios are examined:
S1.1: Expansion of the HGV toll without modal shift impacts
S1.2: Expansion of the HGV toll with modal shift impacts to the railroad S2.1: Introduction of a Gigaliner without modal shift impacts
S2.2: Introduction of a Gigaliner with modal shift impacts to the road
For the impact analysis specific values for each scenario have to be determined for a defined forecast year. In this case, the forecast year is set to 2025, a period of 10 years, which is in line with the long-term period (see figure 2) and represents a good basis for showing realistic developments by using the approach. Based on relevant literature and current studies changes between the forecast values (baseline assumption) and the scenario values occur. These changes are listed in figure 4.
Figure 4: Definition of values for each scenario (forecast year: 2025) [5][11][13][14]
The objective of S1.1 and S1.2 is reducing the mentioned financing gap [5] by expanding the HGV toll. The expected additional revenues shall be completely and for that specific purpose reinvested in the German road infrastructure [12]. Consequently, over a period of 10 years expected funds of billion EUR4.064 [5] are available as financial resources to increase the gross fixed assets (replacement value of the German transport infrastructure, +6.73%) as well as the net fixed assets (fair value, +8.75%) in S1.1.
According to a study [13] the complete expansion of the HGV toll has economic effects such as shiftable potentials of road freight transport to the transport carrier railroad.
Taking the price-sensitive and railway suited (access to combined transport, quantities with corresponding time slots in rail transport and without special quality requirements) transport volume, with a raise in costs due to the tolls, into account, an increase of 11.0%
in transport volume for the transport carrier railroad is forecasted [13] and adopted as an assumption for S1.2. In direct connection the reduced transport volume on the road (- 1.16%) results in less expected revenues and thus lower gross (+6.65%) and net fixed assets (+8.65%).
S2.1 and S2.2 are addressing the expansion of transport units in the form of the volume- oriented variant of the Gigaliner (permissible total weight remains limited to 40/44t, while extension to an overall length of 25.25m, load volume increase from 100m3 to 150m3) [14]. Within S2.1 a modal shift impact is considered not to be very probable [11].
Regarding restrictions like point-to-point transport >25km, full load >70% volume utilization the market potential of shiftable transports on all truck transports is estimated to 3-7% in relation to the national area [11]. For the forecast year 2025 an assumed shift of 5% of conventional truck transports to Gigaliner results in a reduced road load due to lower axle loads and therefore to an improvement of the quality of roads indicator (+2.50%) [11]. As new findings of the field trial for increasing truck size a single tour with a Gigaliner is capable of replacing 1.55 tours with conventional trucks and is characterized by an increased average transport distance of 240km [11]. Relating to the assumed shift of 5% a decrease in the average daily traffic density (-1.76%) and an increase in average transport distance (+2.14%) for the transport carrier road can be calculated.
S2.2 in contrast considers a drop in demand of 7.6% in rail freight transport [14] and thus a shift towards the transport carrier road. According to this, it is expected that the increase in transport volume would lead to a 7% shift of all truck transports to Gigaliner. Due to the additional transport volume on the transport carrier road (+0.80%) there will be a higher decrease in the average daily traffic density (-2.47%) as well as a higher increase in average transport distance (+3.00%). Regarding the quality of roads indicator no clear estimation can be made compared to S2.1 due to the additional load caused by the increased transport volume on the transport carrier road.
Within the fourth procedure step, IV. calculation of scenarios, all steps of the calculation scheme are passed (see figure 1). The percentage deviations between the forecast value and the defined scenario values are calculated and result in the comparative indicator (“VLV-indicator”), which shows the impact towards the overall German transport system in weighted percentage points. Each of the four selected scenarios has a positive effect on the transport system, whereby the advantageousness is reduced in case of modal shift impacts. Due to the high amount of expected revenues which are completely reinvested in the German road infrastructure S1.1 can be identified as the most advantageous development. Regarding S2.1 and S2.2 it is apparent that small adjustments like reducing the average daily traffic density with increasing the average transport distance at the same time have positive effects on the transport system as well.
Figure 5: Calculation and comparison of scenarios for the freight transport scenarios (forecast year: 2025)
Besides the consideration of the “VLV-indicator” there is also the need to evaluate the changes on the transport carrier level, especially regarding the modal shift impacts.
Within this last procedure step, V. comparison of scenarios, S1.2 predicts a negative effect on the “transport carrier indicator” railroad as the increased freight transport volume will result in a substantially higher load without additional expansion, conversion or new construction measures for the railroad infrastructure. In comparison to S1.1 the
“transport carrier indicator” road is higher due to toll-related congestion in the form of evasion traffic (less stressed road infrastructure) and reduced total transport costs for this transport carrier.
Relating to the impact analysis of the technological scenarios S2.1 and S2.2 a positive development for the transport carrier road is predicted in both cases. In S2.2 the drop in demand of 7.6% in rail freight transport results in, on the one hand in a clear relief of the railroad (reduction of total transport and external costs as well as transport volume), but
on the other hand correspondingly gains on the road and the same KPI. Due to cost advantages compared to conventional trucks (extended truck load, comparatively lower fuel costs) and the substitution of 7.0% of truck transport to Gigaliner still a positive effect for the road is predicted. The result shows that the positive effect on the transport carrier railroad (+6.79) is qualified by the relatively small share of the modal split in transport performance [6] compared to the “transport carrier indicator” road.
CONCLUSION AND OUTLOOK
The indicator based approach provides a decision support for representatives in politics and the transport and logistics sector by determining advantages and potentials of new measures and concepts. It should be noted that the KPI system does not provide exact forecasts, but shows possible future freight scenarios and assesses them with regard to the defined objectives [8]. By means of the current information situation, individual parameters or scenario data are determined, which allow an assessment of the level of detail as well as the proximity to reality.
In order to deliver a holistic approach it is useful to bring the freight transport model and the already developed passenger transport model [15] together in the next step to be able to depict their interactions as well as logistic trends and measures in the forecast. So far, freight transport and passenger transport are treated separately [16]. The following figure shows a methodological approach for linking both transport systems to enable a holistic assessment of various future scenarios within the number- and forecast-based procedure model and to be used in addition with existing indicators, such as LPI.
Figure 6: Further methodological approach
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