ACROSSEE TRANSPORT MODEL ELABORATION. Action 4.2. Dissemination level: WP: 4 TRANSPORT MODEL. Author (s): CEI LP. TeTra s.a.s.

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1 ACROSSEE TRANSPORT MODEL ELABORATION Action 4.2 Dissemination level: WP: 4 TRANSPORT MODEL Author (s): CEI LP TeTra s.a.s. Status (F: final, D: draft): F Date: 15 December 2014 File name: 2014_12_15_ACROSSEE_4_2 page 1 of 43

2 This document was produced with the technical consultancy of: TeTra s.a.s. Corso Matteotti, 42 Torino, Italy Tel Fax DISCLAIMER This document is property of the ACROSSEE project and its partners. In any case if you are interested to extract some pages from this Publication you have to mention the source: SEE/D/0093/3.3/X_ACROSSEE project, Transnational Cooperation Programme South East Europe. The publication/document reflects the author s views and the Managing Authority is not liable for any use that may be made of the information contained therein. Without derogation from the generality of the information of this document, the Managing Authority, the project partners, their officers, employees, agents and contractors shall not be liable for any direct or indirect or consequential loss or damage caused by or arising from any information or inaccuracy or omission herein and shall be not liable for any use of the information contained in this document. page 2 of 43

3 TABLE OF CONTENTS 1 INTRODUCTION TRANSPORT SUPPLY AND DEMAND MODELLING TRANSPORT SUPPLY THE ROAD NETWORK THE RAILWAY NETWORK TRANSPORT DEMAND TRANSPORT MODEL CALIBRATION RESULTS ROAD TRANSPORT NETWORK FLOWS RAIL TRANSPORT NETWORK FLOWS CONCLUSIONS page 3 of 43

4 1 INTRODUCTION The major aim of the ACROSSEE project WP4 is to create a unified transport model for the whole SEE (South East Europe), a key area for the European integration process consisting of both EU and non-eu countries. Other WPs activities are closely interlinked with such modelling activity. In particular WP5, which is dealing with Cross Border analysis, will provide detailed information concerning one the most relevant issues for the SEE connectivity. Concerning WP3, the analyses on the network implementation and extension will be chiefly linked and based on the WP4 outcomes. Figure 1 - WP4 in the overall ACROSSEE project framework Within the context of WP4, the present document, reporting the outcomes of action 4.2 concerning the current state modelling, plays a central role; in fact, on the one hand, it capitalises the 4.1 activity results related to both data collection and methodological setup, on the other hand, lays the basis for the 4.4 activity dealing with scenario evaluations. Furthermore the results of the modelling activity will feed all the other steps until the overall accomplishment of WP4 tasks. page 4 of 43

5 WP 4 Transport Model 1: Demand analysis 2: Transport Model 3: Extension of the Transtool model to the Western Balkans 4: Evaluation of different scenario 5: Data Manager Centre Figure 2 - WP4 actions In fact developing the transport model with reference to the current situation means achieving an understanding the mechanism which rule the interactions between transport demand and supply; such understanding allows not only to reproduce the existing traffic flows in each network links but also to simulate the consequences of specific changes. In other words, scenario evaluations can be performed, which is definitely what is needed with reference to transport system planning (especially on a large scale, both spatial and temporal, and implying high investment). Therefore rather than simply depicting the current situation the model provides a Decision Support System for stakeholders willing to perform what-if evaluations. The reference year for the current situation is 2013, thus been in line with the ACROSSEE data collection campaign. Therefore all the analyses described in the following of this document are referred to this specific year. Scenario evaluations, instead, will be related to the different (future) time horizons that will be described in 4.4 action deliverable. As already clarified in deliverable (in which the reader can find a wider explanations of the methodological framework related to the activities described in the following), given the attention paid to the achievement of a well-balanced overall modelling activity, the confrontation with real data will allow also the review of transport supply modelling (left green arrow). page 5 of 43

6 Therefore the actual development of the transport model described in the presented document is chiefly based on the data collection process performed in both WP4 and WP5. TRANSPORT MODELLING TRANSPORT SUPPLY TRAFFIC FLOWS TRANSPORT DEMAND REAL TRAFFIC FLOWS Figure 3 Transport modelling activities The following Figure 4 shows the main categories of traffic counts data sources (whose relevance for updating transport matrices have been explained in the previous paragraphs) that will be used for supporting transport modelling activity in ACROSSEE. While points 2 and 4 refer to gathering existing data, points 1 and 3 are referred to new survey related, respectably to rail and road traffic at borders. page 6 of 43

7 Rail cross-border traffic counts surveys by Acrossee Other rail traffic counts (official timetables, SEETAC database, SEETO, etc.) Road cross-border traffic counts surveys by Acrossee Other road traffic counts (SEETAC database, SEETO, etc.) Figure 4 Traffic counts to be used in the ACROSSEE transport model Also data provided by other kind of enquiries (see Figure 5) will be exploited within modelling activity. The related data will permit to address properly high relevance aspects in SEE freight transport; they regard cross-borders transits and relevant logistic nodes as maritime and inland ports. Furthermore the general response provided by different kind of transport operators will allow obtaining indication both on quantitative facts on transport commodities flows and other relevant perceived aspects from decision makers. Last but not least, the differentiation provided by such heterogeneous set of data sources will allow an efficient cross-checking process. Obviously, a thorough description of such complex set of enquiries goes beyond the scope of the present document. Therefore the reader is invited to refer to the related deliverables (i.e. those regarding action 4.1 and action 5.1) for further details Rail and road cross-border operations survey Questionnaires to transport operators Maritime and Inland ports survey Figure 5 Enquiries involving stakeholders that provide data to the ACROSSEE transport model page 7 of 43

8 The ambitious goal set (i.e. the multimodal modelling of the whole SEE) call for the usage of both consolidated methodologies and advanced implementing tools; therefore the transport modelling activities will be implemented by means of an advanced transport simulation software: Citilabs Cube 6. Furthermore, the general approach will be adapted in order to maximise the efficiency and reliability of the modelling process. In this purpose, it must be noted how the modelling activity will be implemented in a flexible way as to capitalise already available results taking also into account the level of uncertainty that could be associated to each modelling step. In particular, along with data coming from the aforementioned data collection process, the results of the previous modelling activities will be capitalised. On this purpose it must be also underlined the relevance of the SEETAC project. In fact, it constitutes the previous experience that paved the way to the development of the ACROSSEE project idea. Therefore differently from a complete/pure implementation of the classical Four Stages Method, when feasible and preferable, already existing results will be capitalised. For instance, instead of running brand new transport generation and distribution modelling steps, already available matrices will be exploited and updated (making use of newly collected data and also taking into account their consistency with indicators based on the four-step modelling approach). Another relevant issue to be duly underlined is the impact of the transport demand analysis level of detail (which is related to the availability of needed input data with reference to such overall area) on the final outcomes of the traffic simulation. Since traffic zones are mainly corresponding to the NUTS3 territorial classification and the related intra-zonal traffic is to be neglected for methodological reasons, it is not possible to fully reproduce the traffic situation in case of situation mainly characterised by the presence of local traffic (e.g. agglomerations). However, such inevitable biases have been limited making use of available traffic data. Moreover this issue is not affecting crossborder situations, which constitutes the main issue of the ACROSSEE project. page 8 of 43

9 The present document will provide a description of the transport model development process along with its main results. It will be based on the methodology deliverable; moreover the topics will be dealt, as much as possible, in the same sequence as to allow an easy cross-referencing among the two closely interlinked documents (thus allowing the reader to easily look for the methodological aspect whose results are presented here). page 9 of 43

10 2 TRANSPORT SUPPLY AND DEMAND MODELLING The present chapter focuses on the multimodal transport supply modelling activity of the SEE transport system. Such activities related to both the supply and the demand side of the transport systems constitutes the basis for the traffic flow simulations presented in the following chapter 2.1 Transport supply The transport supply modelling activity carried out has been characterised by the development of a detailed multimodal graph characterised by a georeferenced representation. The georeferencing adopted regards both nodes and links of the graph (Figure 6), thus marking a remarkable step forward with respect to SEETAC graph (in which only nodes were georeferenced). Such graph represents, in structured format (designed for supporting the traffic simulation activity), the main and secondary infrastructures of all transport modes under study (road, rail and waterways) and covers SEE including the accessing points of the Balkans area. In order to achieve such result a new graph, based on previous experiences but introducing remarkable novelties, has been developed by collecting and integrating information from all the different available data sources, including both printed and digital maps. The level of detail of the modelling and georeferencing process will be adequately matching the goals of a macroscopic approach thus skipping not relevant details. page 10 of 43

11 Figure 6 Links and nodes in a georeferenced graph thematic representation In particular, in the present context are deemed not relevant specific ramps at motorway entrances/exits, roundabout arcs/legs while it will be schematised the general alignment of the road axis (even in case of double carriageway infrastructures). Moreover useless nodes have been skipped as foreseen in the methodology adopted whose operational steps are represented in Figure 7. SEQUENCE OF STEPS IN THE ACROSSEE GRAPH DEVELOMENT PROCESS 1) DATA SOURCE GATHERING (DIGITAL AND NOT MAPS) 2) SORTING OUT TRANSPORT INFRASTRUCURES TO BE MODELLED 3) FILTERING NOT NEEDED LINKS (E.G. RAMPS) 4) IDENTIFYING AND MAPPING RELEVANT NODES 5) CONNECTING RELEVANT NODES WITH GEOREFERENCED LINKS 6) FILLING UP ATTRIBUTES TABLE ASSOCIATED TO THE LINKS Figure 7 General ACROSSEE graph development process page 11 of 43

12 This general approach has been applied to the three modes of transport composing the ACROSSEE multimodal graph: rail, road and waterway. In the following a particular deal has been paid to road and rail modes, which have been object of the ACROSSEE survey. However in order to assess modal shift in future scenarios (see 4.4 action deliverable), also relevant the inland waterway graph has been developed including the relevant links depicted in blue colour in Figure 8. Figure 8 Inland waterways in the ACROSSEE multimodal graph In the following, given the level of detailed achieved with reference to a vast geographical content, thematic representations of relevant link attributes will be given by means of zoomed view associated to the sectors indicate in the Figure 9. page 12 of 43

13 Figure 9 SEE area sectors page 13 of 43

14 2.1.1 THE ROAD NETWORK Road network graph include all primary links, as motorways and expressways, and also relevant secondary roads as national, regional and even, in some cases, provincial ones. Each element of the road network graph is associated with a set of features concerning, a part from naming and administrative identification of the road, the main performance characteristics. The list of the main attributes associated to each link is reported in the following Table 1. ATTRIBUTE DESCRIPTION/NOTES ID Identificative field which includes the codes of the delimiting nodes (e.g.1500_1245) NAME If available TYPOLOGY e.g. motorway, main road, secondary road, etc.) Allowed traffic directions 0 bidirectional (default value); ONEWAY 1 monodirectional link with the same direction of the traced polyline -1 monodirectional link with the opposite direction respect to the traced polyline. CATEGORY Group to which the link is belonging according to the links performance classification NUMBER_OF_LANES Number of lanes per direction CAPACITY Maximum number of travelling vehicles per hour (with reference to a specific direction) FREE_FLOW_SPEED Average desired speed experience when the link traffic flow is close to 0 LENGHT Link length TOLL_ ROAD /km It is referred to permanent limitations valid throughout the year 0 no limitations (default value); TRAFFIC LIMITATION 1 no entry for all vehicular categories 2 heavy vehicles ban Table 1 Road links attributes As to provide an example of the graph informative content, in the following figures are shown some thematic representations of one key attributes as number of lanes. page 14 of 43

15 Figure 10 SEE road network links: number of lanes Figure 11 SEE road network links: number of lanes (Sector 1) page 15 of 43

16 Figure 12 SEE road network links: number of lanes (Sector 2) Figure 13 SEE road network links: number of lanes (Sector 3) page 16 of 43

17 2.1.2 THE RAILWAY NETWORK The railway network is included almost entirely into the ACROSSEE graph; in fact only specifically touristic or metropolitan lines have been omitted. In the case of railway links the number of relevant attributes is smaller than in the case of road transport, as can be seen in Table 2. ATTRIBUTE ID POWER SUPPLY ELECTRICAL VOLTAGE NUMBER OF TRACKS CAPACITY MAX_ALLOWED_SPEED LENGHT PROFILE_CODE AXLE LOAD DESCRIPTION/NOTES Identificative field which includes the codes of the delimiting nodes (e.g.1500_1245) e = electrified; ne = not electrified Value in kv Overall number of tracks (no distinction is made with reference to directions) Estimated maximum number of trains/day vehicles per hour (summing up both direction) Maximum allowed speed Link length UIC Loading gauge classification of the maximum allowed transversal height and width (of travelling railway vehicles and loads) Classification of the maximum allowed axle load Table 2 Rail links attributes In the following figures some thematic representations of key attributes is provided, thus giving a first insight into the network characteristics. In particular, it must be noted a high share of not electrified tracks (grey links in Figure 14). On the other hand, in case of electrified links (coloured links in Figure 14) there is, especially in the Balkans, a higher homogeneity with respect to other parts of Europe (e.g. Central Europe). In fact apart from those in the North-Western part of the analysed area (i.e. Italy, Austria and Slovenia), all the other countries share the same electricity supply system: Volt alternating current. This aspect is, obviously, of particular relevance with reference to interoperability issues in transnational transport. With regards to the number of tracks, the predominance of single track links testifies, once more, the limited capacity of many connections. In addition, discontinuities (in number of tracks) can also be seen in relevant transnational corridors. In fact none of them experiences continuity in the presence of double tracks crossing the Western Balkans or the Hungary-Romania border. page 17 of 43

18 Figure 14 SEE railway network links: electrification Figure 15 SEE railway network links: number of tracks page 18 of 43

19 Figure 16 SEE railway network links: number of tracks (Sector 1) Figure 17 SEE railway network links: number of tracks (Sector 2) page 19 of 43

20 Figure 18 SEE railway network links: number of tracks (Sector 3) With reference to capacity, the attribution of link values has been carried out on the basis of average values that can be associated to the number of tracks and the presence (or not) of electrification. General reference values are reported in the following table. TYPE Number of Tracks Capacity [trains/day] Non electrified 1 70 Non electrified Electrified Electrified Table 3 Rail capacity general values page 20 of 43

21 2.2 Transport demand In order to reach an overall quantitative vision and, above all, to allow the implementation of the traffic simulation algorithms, the transport demand has to be express in form of an Origin-Destination (O-D) matrix, which is based on a subdivision of the study area in zones (zoning). The zoning system adopted here is based on NUTS3 level of the EU standard territorial subdivision Nomenclature of Territorial Units for Statistics (NUTS from the French Nomenclature des Unités Territoriales Statistiques). Usually non EU countries areas are not classified according to such standard. Therefore in some cases it has been necessary to take into account comparable administrative subdivision (e.g. Albania has been subdivided in its counties while Bosnia and Herzegovina will be subdivided in counties (also named as cantons). Figure 19 ACROSSEE project zoning system page 21 of 43

22 Figure 20 - ACROSSEE project zoning system (detailed view) The previous figures show the zoning system adopted distinguishing, by means of different colours, between internal and external zones. Furthermore, the following relevant ports, providing a localised gateway for external traffic, have been added as specific zones: 1. Venice 2. Trieste, 3. Koper, 4. Rijeka, 5. Split, 6. Durres, 7. Bar, 8. Igoumenitsa, 9. Thessaloniki, 10. Varna, page 22 of 43

23 11. Burgas, 12. Constantza, 13. Pireus, 14. Taranto, thus leading to a total number of 364 zones. The matrix estimations have been carried out adapting the available SEETAC matrix and by means of the different available data including other matrices from previous projects, partial statistics and, above all, traffic counts (first of all those collected within the ACROSSEE surveys but including also other available statistics). Therefore the so-called prior matrix was basically obtained from the SEETAC repository which provides an O-D modal matrix characterised by a NUTS2 level of detail (or similar for non EU countries) with reference to the internal portion of the analyzed area; for the external portion of the graph, instead, data area provided at country level. Therefore, in order make use of such data, they have been previously subdivided according to the ACROSSEE zoning system. Therefore a correspondence was established between the two zoning systems, using socio-economic data for assessing specific weights of subdividing zones. In particular the demand referred to the NUTS2 level where subdivided among the composing NUTS3 area on the basis of the related proportion of Population and GDP (for passengers and freight traffic respectively). The preliminary obtained matrix resulting have been improved and updated by means of the Matrix Estimation techniques exploiting all the available data. In this estimation process other data, even though partial were used for correcting and upgrading the estimation. In particular data from other projects, as SoNorA (Central Europe Programme, ), available data sources (see deliverable for further details)and other previously accomplished surveys were exploited. The methodology applied makes use of the Analyst module of Cube software, which implements the Maximum Likelihood method (described in deliverable 4.1.1). page 23 of 43

24 The reliability check on the estimations has been carried out within an iterative validation procedure, in which the estimated traffic flows of the model are compared with those measured in both, the ex-ante situation, where the assignment model is run with a Prior matrix and in the ex-post situation, where an updated matrix is used. In such iterative process a specific confidence level is used for allowing updates on each specific cell of the matrix. This confidence value was related to the coherence between the matrix and other sources (gravitational estimator and partial matrices used for confrontation). page 24 of 43

25 3 TRANSPORT MODEL CALIBRATION RESULTS The present chapter focuses on the results of the transport model calibration which led to the assessment of traffic flows in each link of the graph. The assessment of the calibration process will be made, both with reference of the road and of the rail network, focusing in particular on the main object of the ACROSSEE project activities: freight traffic. Coupling such outcomes with the links capacity evaluations, resulting from transport supply modelling, it is possible to assess the degree of saturation of each links (i.e. the ratio between actual flows and capacity). On such basis the evaluation of bottlenecks and criticalities can be carried out along with the assessment of spare capacity, in different modes of transport, thus giving a first basis for the evaluation of possible shifts and improvements toward a more sustainable transport system. In the following paragraphs such analyses will be performed with reference to both the road and rail transport. 3.1 Road transport network flows The traffic flows on the road network have been achieved applying the traffic assignment Deterministic User Equilibrium algorithm (modelling the effect on increasing travel time due to congestion in user path choices) to the O/D matrix and the road network described in the previous chapter. With particular reference to the heavy vehicles (mostly corresponding to freight transport), the following table shows the comparison between real data (i.e. those gathered within the ACROSSEE traffic surveys) and simulated ones. page 25 of 43

26 ROAD SECTION SURVEYED TRAFFIC SIMULATED TRAFFIC DIFFERENCE Batrovci ,07% Evzonoi ,53% Fernetti ,41% Gorican ,60% Hani i Hotit ,57% Horgos ,56% Kakavije ,54% Krystallopigi ,04% Lendava ,11% Moravita ,42% Morine ,37% Nickelsdorf ,25% Obrezje ,59% Oradea/Bors ,17% Presevo ,06% Promachonas ,43% Siret ,56% Spielfeld ,94% Tarvisio ,99% Vama Albita ,82% Vrbnica ,49% Zahony ,13% Zupanja ,04% Table 4 Comparison between surveyed and simulated heavy vehicles traffic flows Instead for assessing the general congestion, the resulting picture of SEE traffic, expressed in Passenger Car equivalent Units (figures 25-28) in the network underline relevant differences presenting highest values in the agglomeration of the North-Western portion of the graph, in particular in Northern Italy and in Austria. In general, a part from limited exceptions, outside relevant urban agglomerations, traffic flows in the SEE are quite low. In particular cross-border traffic is limited with a relevant percentage of heavy vehicles. Such outcomes are representative of limited integration between neighbouring countries and high share of road transport for long distance freight traffic. In order to assess level of congestion, the degree of saturation, expressed as the ratio between flows and the link capacity is represented in the Figure 29 (and the following ones with the usual zoomed views). It presents analogous results than the traffic flow representation even though in some case limited capacity of specific links lead to high degree of saturation (e.g. some links in the Western Balkans). page 26 of 43

27 Figure 21 Heavy vehicles traffic in the SEE road network Figure 22 Heavy vehicles traffic in the SEE road network - zoomed view (Sector 1) page 27 of 43

28 Figure 23 Heavy vehicles traffic in the SEE road network - zoomed view (Sector 2) Figure 24 Heavy vehicles traffic in the SEE road network - zoomed view (Sector 3) page 28 of 43

29 Figure 25 Traffic flows in the SEE road network Figure 26 Traffic flows in the SEE road network - zoomed view (Sector 1) page 29 of 43

30 Figure 27 Traffic flows in the SEE road network - zoomed view (Sector 2) Figure 28 Traffic flows in the SEE road network - zoomed view (Sector 3) page 30 of 43

31 Figure 29 Degree of saturation in the SEE road network Figure 30 Degree of saturation in the SEE road network: zoomed view (Sector 1) page 31 of 43

32 Figure 31 Degree of saturation in the SEE road network: zoomed view (Sector 2) Figure 32 Degree of saturation in the SEE road network: zoomed view (Sector 3) page 32 of 43

33 3.2 Rail transport network flows The reconstruction of traffic flows on the rail network has been achieved making use of the official timetables in the case of passenger transport. In the case of freight transport instead, only limited data were available; moreover actual traffic differs from the planned trains resulting from timetables. Therefore the simulation has been performed and on the basis of traffic data and other statistical data on transport demand that allowed improving the transport matrix obtained from the SEETAC project. RAIL SECTION SURVEYED FLOW SIMULATED FLOW Hani I Hotit 4,2 4 Nickelsdorf Spielfeld Dimitrovgrad 7,2 9 Koprivnica 11,2 12 Slavonski Šamac 2,4 3 Idomeni 12,2 11 Promachonas 0,4 2 Záhony normal gauge 2,4 3 Záhony wide gauge 5,6 7 Tarvisio 34,6 41 Holboca 4,4 5 Curtici Stamora Moravita 3,6 3 Dornesti 9 10 Sid 5,4 6 Preševo 7,2 9 Subotica 9,4 10 Loznica 2,6 3 Vrbnica 8,6 9 Dobova 7,4 8 Hodos 14,2 14 Table 5 Comparison between surveyed and simulated freight train flows The passenger train traffic in the network presents relevant differences presenting highest values in the agglomeration of the North-Western portion of the graph (Vienna, Budapest and in the Venice area). page 33 of 43

34 Figure 33 Passenger trains in the SEE rail network Rail freight traffic (Figure 37, Figure 38, Figure 39 and Figure 40) typically presents a concentration of flows along main corridors or dedicated lines. However, it must be noted how in SEE relevant macroscopic region are affected only marginally by low traffic volumes. For instance flows higher than 40 trains/day can be seen almost exclusively in the northwestern portion of the graph. In such area can be found corridors presenting a high level of maturity. In this purpose, it must be underlined the relevance of the cross-alpine route provided by the Brenner corridor and to a stretches of the Baltic-Adriatic corridor (whose fully exploitation is likely to be fostered by the Koralm and Semmering tunnels whose realisation process is currently ongoing). Concerning East-West direction, the direction north to the Alps (linking Munich, Vienna and Budapest) has a predominant role. In fact the Mediterranean corridor, even though presenting high values in the Western part and in Slovenia, is characterised by limited values between Venice and Slovenia. page 34 of 43

35 In the Balkans various branches of the developing Orient/East MED and Rhine/Danube corridors shows relatively remarkable flows. The Pan European corridor X, instead, crossing the West Balkans shows limited long distance traffic. Summarising both passenger and freight rail traffic in SEE results in a heterogeneous; however, in the case of freight traffic differences are more evident. Figure 34 Passenger train traffic flows - zoomed view (Sector 1) page 35 of 43

36 Figure 35 Passenger train traffic flows - zoomed view (Sector 2) Figure 36 Passenger train traffic flows - zoomed view (Sector 3) page 36 of 43

37 Figure 37 Freight trains in the SEE rail network Figure 38 Freight train traffic flows - zoomed view (Sector 1) page 37 of 43

38 Figure 39 Freight train traffic flows - zoomed view (Sector 2) Figure 40 Freight train traffic flows - zoomed view (Sector 3) page 38 of 43

39 In the light of intermodal transport development, it is important to assess the available spare capacity by means of the degree of saturation, expressed as the ratio between total flows (i.e. freight + passenger trains) and the link capacity. In the following figures the elaboration on daily averages gives a clear indication on a general high level of spare capacity. In fact, a part from some aspects related to the management of specific node (which is more pertinent to more detailed analyses), only a few links show high level of saturation. Such situation typically appears when nodes related traffic (in particular passenger trains) is merged together with long distance traffic along relevant corridor. In particular the area of Vienna along with (to a less extent) Budapest, Ljubljana and Zagreb presents high values. However it must be noted that values still below the unit evaluated at general level do not necessarily imply a critical condition affecting negatively the regular circulation of trains. Figure 41 Degree of saturation in the SEE rail network page 39 of 43

40 Figure 42 Degree of saturation in the SEE rail network: zoomed view (Sector 1) Figure 43 Degree of saturation in the SEE rail network: zoomed view (Sector 2) page 40 of 43

41 Figure 44 Degree of saturation in the SEE rail network: zoomed view (Sector 3) page 41 of 43

42 4 CONCLUSIONS In the present document the ACROSSEE project modelling activities and results have been presented. Such results have been achieved applying the methodology presented in deliverable which implements the classical Four Stages Method in flexible way as to capitalise already available results taking also into account the level of uncertainty that could be associated to each modelling step. The data needed for performing the modelling activities and ensuring good quality results have been gathered by means of the ACROSSEE surveys and by capitalising the results of previous projects (SEETAC in particular). The supply modelling activity has been carried out developing a detailed georeferenced representation of the multimodal transport system. In particular both road and rail network analyses have allowed to assess the heterogeneity and shortcomings that affect the current networks. The demand modelling, instead, has lead to an Origin/Destination matrix which, with reference to the central portion of the analysed area, is based on NUTS3 level zoning. The application of well-consolidated transport modelling algorithms have let to simulate the interaction between supply and demand thus reconstructing average traffic flows in each link of the network. The analysis of degree of saturation on network links shows not many bottlenecks in the region, apart from sections and nodes near capital, major cities and some areas in the north western portion of the graph. The main criticalities in the road network can be seen in along the main axis as in the case of the A4 motorway in Italy or in the main connection converging to Vienna. However also in the Balkans area Isolated links presents remarkable congestion due also to limited capacity. With reference to the rail network the low level of utilisation in general seen in the South Eastern area is related to very limited long distance flows due to low performances of the page 42 of 43

43 rail network and, more in general, lacking development of intermodality. However the still to be achieved exploitation of corridors is widespread. For instance it can be seen how the Italian portion of the Baltic-Adriatic corridor is currently under-utilised (see in particular the section between Venice and Tarvisio). In particular traffic volumes are quite low at most of the BCPs, thus expressing a still to be achieved cross-border integration. Furthermore, with reference to a key area for the integration process as the Western Balkans area traffic volumes are generally low; moreover limited performance of the rail network and services are hampering the development of a fully functional and sustainable long-distance transport. On the basis of the current state modelling described here, various scenario evaluations will be performed in the next steps of the ACROSSEE project, thus capitalising the results of the present activity. One particularly relevant scenario will assess the effect of the improvements in BCP transit times (which are included as specific link travel time attributes in the model) whose relevance has been ascertained in the present analysis and which are described in detail the WP5. In fact the detailed modelling activity performed allows assessing the impact of (relatively) small intervention dealing also with organisational issue or more in general with each aspect affecting the generalised transport cost. Moreover a long-medium term horizon analysis will also allow evaluating large scale interventions, including relevant infrastructural realisation, as those associated to the development of the TEN-T core network and its extension to neighbouring countries in the general context of the EU integration process. page 43 of 43