Scenario analyses for a sustainable transport system development

1. Introduction

Researchers exploring transport systems build variety of analytical models enabling examination of interaction between transport modes on different organizational levels and from different perspectives [7]. Selected models can be used to determine the priorities for national transport policy with regard to external costs off transport activity and costs of undesired effects of transport on the environment and human life. The external costs of transport and its harmful environmental impact are dependent not only on transport volumes and technical equipment, but also on the distribution of traffic into modes of transport within given network and expeditious switching it between the modes. Switching modes requires time, resources and appropriate regulations forcing or allowing it [7], [8]. Assessing plans of transport system development and elements of transport policy is a complex task that should have been solved at the level of greater detail.

As many authors point out, one of the key features of a modern approach to transport systems planning is the possibility of harmonious interaction or normal competition between different modes of transport [1], [4], [8], [12]. Since the transport system is based on graph structure it needs nodal elements for goods and passenger transition between modes and places to enter and leave the system like logistics hubs or freight handling facilities and passenger traffic centres. The infrastructure cannot be expanded endlessly not only because it is not environmentally friendly, but costs money, space and environmental degradation [4], [5], [7].

The quality of the offer of transport services provider's is related to the quality of transport system including network, infrastructure and organization. Transport system quality criteria are derived from transport policy strategies of individual operators, carriers and business entities which implement a specific transport processes under policy of the country [5], [7], [13].

Strategies of the development of transport system in a pro-ecological way must take into account the interests of all participants of transport process, as well as external bodies [8]. The first face is the level of harmful environmental impact (ecological aspect), and the second one is the traffic safety (number of accidents and disasters) under planned costs and volumes. Thus, the development of transport modes is not only determined by new of better technologies and vehicles, but must take into account dynamic factors like just-in-time strategies, increasing need of mobility and ecological and safety constraints [7], [8], [13], [9], [11].

EMITRANSYS was prepared and tested as efficient support for multiobjective analyse of sustainable transport system development through scenario analysis.

2. Scenario analysis

3. Application of scenario analysis to develop a sustainable transport system - case study

The analyse of the development of a transport system of Poland was provided to demonstrate the method. The experiments were carried out in EMITRANSYS model [6] developed on the base of VISUM environment [10]. The model consists of the model of network and the model of demand for transport services. 4 035 nodes and 4 437 connections of railway network were mapped together with 14 863 nodes and 16 666 connections of road network. The demand was forecasted for the needs of freight transport and passenger transport. Both models were calibrated up to districts or significant municipalities.

Three scenarios of system development were analysed in EMITRANSYS:

$sc=$ 1 – realistic, for which stable economic development is assumed, investment in transport infrastructure is moderated but stays on average level, number of vehicles with high EURO standards increases in the structure of rolling stock while low EURO standard vehicles withdraw.

$sc=$ 2 – optimistic, for which a strong economic development is assumed, investments in transport infrastructure are high, a number of high EURO standard vehicles grows significantly together with a significant fall in the number of vehicles with lower EURO standards.

$sc=$ 3 – pessimistic, for which a slow economic development is assumed, investments in transport infrastructure are far below what is really required, the number of vehicles with high EURO standards increases slowly while low EURO standard vehicles dominate in rolling stock.

The study was carried out for the year 2020. Mode switching possibilities are related to the accessibility and quality of nodal infrastructure. The analysis was based on the multifaceted forecast of the goods and passengers volumes. Demand for services in the passenger segment was divided into business trips, trips to school and work, and other trips. Demand for freight segment was determined for fourteen basic cargo groups [10]. Vehicles used for the transport were aggregated into five categories (buses, passenger cars with compression ignition and spark ignition, and trucks with compression ignition and spark ignition) and seven types of EURO norms (EURO 0 - EURO 6). The railway rolling stock was limited to diesel and electric traction with appropriate cars. The structure of vehicles was defined for each scenario.

The distribution of cargo and passenger traffic for 2020 made in EMITRANSYS model implemented in VISUM, as shown in Fig. 1. The transport work passenger transport was amounted to 715 466 582,5 pkm, while the transport work in freight transport is amounted to 264 294 491 tkm.

Fig. 1Distribution of traffic flow for 2020. Source: [10]

The appropriate analyses of harmful emission were done on the base of own studies of exhaust emission in real traffic conditions. The emission of carbon monoxide (CO – red in figures), nitrogen oxides (NO_x – green in figures) and hydrocarbons (HC - blue in figures) was estimated.

Total emission of particular compounds for the realistic scenario is shown in Fig. 2(a). Total emission of HC was 80 998 641 g, of CO was 402 946 593 g, and of NO_x was 1 413 797 760 g.

Total emission for optimistic scenario is shown in Fig. 2(b). Total emission of HC was 77 617 900 g, of CO was 394 469 119 g, and of NO_x was 1 382 488 645 g.

The emission for pessimistic scenario is shown in Fig. 3. Total amount of emission of HC was 82 444 260 g, of CO was 404 924 785 g, and of NO_x was 1 424 592 543 g.

Fig. 2Emission of harmful compounds for scenarios a) realistic b) optimistic. Source: [10]

a)

b)

Fig. 3Emission of harmful compounds for pessimistic scenario. Source: [10]

As it was expected, the littlest emission of harmful compounds was obtained for optimistic scenario, even under significant grow of demand for freight and passenger transport. High economic growth, more infrastructural investment and switching high-emission vehicles to low-emission foster better organization, higher traffic velocity and better access to the transport network. Better planning and high volumes justify using high capacity, low emission vehicles.

4. Conclusions

EMITRANSYS model offers a possibility to perform multifaceted analyses of material and passengers flow in transport network. It is a complex tool set for analysing scenarios of transport system development under different economic, technological and environmental criteria. Alternative traffic distributions according to different development scenarios can be analysed and evaluated on the required level of detail. Investment expenditures can change infrastructure and stock, while economic projections show the indicate the direction of changes in demand for transport services.

The model uses a set of partial criteria functions related to different aspects of transport activity. Each variant requires individual set of functions to reveal the objectives of decision maker.

Due to the diverse nature of the partial criteria functions represented by each participant in the decision-making process, it is difficult to obtain a satisfactory solution for each transport system user. Multi-criterion methods are used to assess the decision from the point of view of contrary criteria representing different expectations of users. The basic difficulty in the practical implementation of the multi-criteria objective function is that only in exceptional cases a mutual conformity of the partial functions of the criteria appears. In general, the inclusion into the multi-criteria mathematical programming model more than one criterion does not lead to the unequivocally best solution, i.e. one that would provide the optimum for all criteria at the same time.