Intermodal nodes and external costs: re-thinking the current network organisation

Transcription

1 Intermodal nodes and external costs: re-thinking the current network organisation Ambrosino aniela University of Genoa Ferrari Claudio University of Genoa Sciomachen Anna University of Genoa ei Alessio University of Genoa Abstract Modern ports are part of intermodal and international networks that have great effects at regional level, influencing both the efficiency of the local markets and the external costs of the served industries. Several studies underline how modern ports need to be inserted in an efficient network in order to exploit all their potential. Moreover, concerning the role of the ports within the whole supply chain, a key element is the location and the organization of intermediate facilities such as logistics parks or inland ports that heavily affect the effectiveness of the logistics corridors. he accuracy in designing a logistic system can have a great impact on externalities created by transportation. New and adequate infrastructure can reduce congestions, pollution, and accidents. he proposed study analyses the effects of different potential locations of logistics platforms that might serve a market through different transport alternatives, in terms of transport modes, costs and distances, in order to better understand the importance of a correct distribution network design. In order to achieve the goal a MILP model has been developed and is currently under a testing phase in order to evaluate a possible flow distribution using different transport modal alternatives and that considers the internalization of external costs. utcomes can be used in order to improve current transport policies that might foster a more efficient and less impacting hinterland transport solution.

2 1. Introduction International transports are today based on complex networks of services that involve a plurality of actors and of transport solutions in order to make possibile the efficient connection between globalised origins and destinations (e.g. Ferrari et al., 2011; Meersman et al, 2009). For these reasons all possible connections between the port and its own hinterland represent strategic links able to foster or slow down the port efficiency and its competitiveness (e.g. ongzon, 2009). Several evidences show how well connected hinterlands might increase port competitiveness (e.g. Ferrari et al., 2011) and how the organisation of intermodal transport that efficiently uses either logistics parks or inland ports might enlarge port catchment areas (e.g. Notteboom and Rodrigue, 2008) Moreover, these connections between the port and its hinterland are not just important for efficiency reasons but also considering the overall costs of different transport solutions, accounting not only for direct private costs but also for external ones. Starting from Pigou (1932), external costs of any economic activity gain importance due to the divergence between the perceived private cost on a specific economic behaviour and the real overal costs that in many cases include external costs, such as the enrivonmental ones. Concerning maritime academic literature, this issue has been seldomly studied until the 90s when it became one of the major concerns in the maritime science (e.g. Meersoman et al., 1998). Main studies then tried to evaluate the discrepancy between the private perceived costs of a specific transport solution and its actual cost for the society. Estimations varied over the time due to the difficulties in clearly evaluate external effects and their range of impact (e.g. Maibach, 2008). espite these external costs have been deeply studied recently also under many EU projects, for instance, aiming at assuring their internalization (e.g. eurovignette), only few studies try to link external costs to the optimization of intermodal transport solutions. hile in recent literature policies discussing effective flow distribution in a determined hinterland have been deeply analysed (e.g. Notteboom and Rodrigue, 2008) as well as researches focused on the internalization of transport costs (e.g. Grosso, 2011) or effect of EU policy on both this issues (e.g. Ferrari and ei, 2012), at the best of our knowledge no studies can be found trying to study the most effective flow distribution in a given port hinterland, taking into account not only transport costs but also external ones. Current paper tries to fill this gap. In order to do so a mixed integer linear programming model (MILP) has been developed, hypothesizing the location of different hubs on a specific hinterland and then the distribution of flows among the region considering three competing transport modes: rail, road and intermodal transport. he model is currently under a

3 testing phase, based on North Italian regions and using real data provided by Italian National Customs for Ligurian ports (i.e. Genoa, La Spezia and Savona) at Moreover infrastructural data has been collected in order to draw a realistic model corresponding on the whole highway and raiway network. In order to allow intermodal transport, four inland ports (called hubs) have been located in specific points of the network, as described in the following section. he paper is structured as follows. After this introduction, Section 2 focuses on the regional and port characteristics while Section 3 is dedicated to the model. Section 4 discusses the results and Section 5 addresses conclusions and provides insights for future research developments. 2. Regional framework Freight distribution in Italy is mainly based on an extensive use of the road transport and this modal solution is also the main transport alternative considering the ones used by port generated traffic (e.g. Consulta Nazionale per l Autotrasporto e la Logistica, 2011). Following the statistics produced by the European Environmental Agency (EEA, 2015; 2008) as well as several relevant literature (e.g. Ferrari and ei, 2012), this issue is deeply impacting on the environment given the fact that road transportation is the one producing the worse external effects. his issue has been deeply analyzed and several local and national projects are trying to deal with the externalities generated by trucks. hile modal shift policies are often based on incentives to make another modal solution more convenient than the more pollutant one as for the Ecobonus that in Italy characterises the Motorways of the Sea (e.g. ei and Ferrari, 2012) less attention has been paid on policies able to internalize external costs. his issue is mainly linked to the poor political consensus given by these latter policies and by the fact that they are harder to apply. Nevertheless, rational used of the most efficient transport solution considering the overall generated costs is essential in order to minimize external costs. Concerning the cost internalization, the Maibach et al. (2008) underline how road transport would became much more expansive in the case of consideration of external costs instead of only private ones. Among main external costs, the andbook takes into consideration pollution (atmposperic and noise related one), risk and congestion. ther specific external costs have been also considered in the handbook but not all of them can be easily adapted to all different European contexts. It is important to underline that external costs not only relate to environmental aspects but also to some side effects that can have a great impact also to the network efficiency. Among these aspects, the main one is related to the congestion issue that can heavily affect the efficiency of all the

4 transport solution. For this reason, many authors (e.g. Ferrari and ei, 2012) underline how external costs internalization might positively impact on the rationalization of the flows, also incentivising the use of intermodality due to a better mix among the transport solutions. he internalization might then reduce road transport, incentivising intermodal one, and generating benefits at both efficiency and environmental level. 2.1 he collected data In other to evaluate the proposed model (please, refer to section 3) the flow distribution of three of the main Italian ports has been analysed (i.e. Genoa, La Spezia and Savona). Ligurian ports, in fact, accounted in 2013 for more than 33% of the Italian container traffic being the greatest gateway port region in Italy and for more than 17% of the overall Italian port throughput (Assoporti, 2015). Port hinterlands are quite similar even if specific traffic concentration differentiate the three Italian ports, as shown in figure 1 in which NUS-3 regions are used as basic geographical unit to underline the distribuion flow structure. Figure 1 Port freight distribution

5 Source: wn elaboration form Customs atabase, 2012.

6 Figure 1 show the import distribution of containerised cargo, as also used in the model. his kind of traffic is currently used in the model for two main reasons: containerised cargo is the only one, in Italy, that is currently able to be competitive using both the rail and the road transport solution (even if with different level of performance) and because the import flows might be easier to aggregate for the rail transport, given the specific origin. Considering the 2011 spatial distribution of the import containerised cargoes handled by the three Ligurian ports, it is important to underline a substantial overlapping in the catchment areas between the ports of Savona and Genoa basically Piedmont (in particular urin and Cuneo) and Lombardy (in particular Milan) while La Spezia seems to be more focused on the cargoes arriving for the Emilia-Romagna region. espite the geographical concentration of some traffic, another peculiarity of the ports is the wide range of the catchment area. For instance the smallest studied container port (i.e. Savona) attract cargos from the Venice region despite the presence of local and closer ports as Venice, Ravenna and rieste. Considering the port of Genoa, its activity is distributed through all the North Italian provinces, with only a small part of the cargo handled that remains within its province s borders (10%) and abound the 30% direct to the Milan province. All the main Lombardy and Piedmont industrial provinces register traffic above the 1% for the Genoese port. La Spezia, as mentioned above, focuses more on the Emilia-Roma provinces with Modena and Reggio Emilia that accounts for more than 15% of the overall traffic while Milan accounts for less than that value. La Spezia is also the only Ligurian port with a solid presence in uscany: over 10% of its activity is linked to the provinces belonging to this Region. Savona, on the other hand, derives about 40% of its import traffic from only two provinces: urin and Milan, having a concentrated traffic origin distribution despite some small traffics arriving also from the East Italian provinces. As said, the strict majority of the flows are currently delivered by road while only inland ports in Alessandria Province (for the port of Genoa) and in Milan (mainly for La Spezia) are currently used. Considering the network data about infrastructural constraints and distances, official statistics has been used in order to collect all the needed information from official databases published by the Ligurian Region (i.e. Regional Logistics Plan) and by the main infrastructure managers (i.e. Rete

7 Ferroviaria Italiana for railways and Autostrade per l Italia for the motorways). hus, considering the collected data, the two network alternatives have been built: as underlined by the Regional Logistics Plan (Regione Liguria, 2010), main hinterland connections are represented by four main inter-regional links (Genoa-Milan/urin; Savona-urin; La Spezia-Parma; La Spezia-Leghorn) and an intra-regional one (Ventimiglia-La Spezia) that assure to easily reach all the main destinations while similar rail infrastructures can be found but with several technical constraints (e.g. La Spezia- Parma and Genoa-Milan) that are currently increasing the costs of the rail solution. In particular constraints are related to the slope of the rail tunnels mainly in the two branch connecting Genoa with Piedmont the presence of only one track in mix freight/pax rail branch in both Savona and La Spezia inter-regional links and the tunnel size. hese limitations affect the costs of the rail operations in three main ways: reducing the length of the trains and then the capacity; affecting the number of locomotives needed in part of the link and diminishing the number of possible daily trains together with the time schedule. For instance, only concerning ligurian network, the maximum number of daily trains varies from a minimum of 60 to a maximum of 180 (considering also passenger ones). n the other hand the motorways do not present similar constraints, even if the truck characteristics should be charged more than trains in case of an internalization of external costs, making the rail solution more convenient. Nevertheless, bottlecknesses are quite frequent on the highways too, with the number of lanes that varies depending on the specific highway (and impacting on the overall capacity). Moreover, in all the three ports part of the freight truck movements are made within the city centre area, affecting also the urban congestion level. Eventually, because the current intra-regional motorway connection passes through the Genoese city centre and it is also used as a ring road many of cargos also affect the city pollution even if they are not directly handled in the port terminals located within the city center. In order to establish related costs of the different solutions, several academic works has been used in order to find main cost elements. For the private cost component, official documentation has been used, mainly related to the establishment of an official minimun cost for the road transport for the year 2011 (year of the flows used in the model) and establishing an average value of about 1.6 euro for UI/km varying in respect of the distance from a minimum value of about 1.3 /km to a maximum of about 1.9 /km (Italian Ministry of ransport, 2011). In order to estimate rail costs, different values have been compared with the literature (e.g. Baumgartner, 2001; Grosso, 2011) and validated by an interview with a rail company. he estimation led us to an average value of about

8 1.8 euro UI/KM, but with high variations depending on constraints and distances (costs are actually estimated, in the model, using the vehicle as unit of measure). External costs have been collected from the andbook on estimation of external costs in the transport sector (Maibach et al., 2008) and because different characteristics of the transport and of the delivery issue only noise, pollution and congestion can be properly included in the estimation. All external costs refer to vehicle/kilometer as unit of measure. 2.2 he ratio of the model he problem is modelled on a weighed multimodal network. he evaluation of the effects of different networks is performed thanks to a mathematical model that is an extension of a mixed integer linear programming (MILP) model used for solving an intermodal hub location problem in Ambrosino and Sciomachen (2014). Given a set of potential logistics platforms to locate and a set of origin destination (o-d) demands to satisfy, the problem is to determine which platforms have to be located and how to allocate o-d demand to the chosen platforms so to minimize the total logistics costs and satisfy all the capacity restrictions related to both facilities and arcs. he involved costs are the transportation ones and the operative and fixed opening cost for the facilities. Moreover external costs are included in the analysis in order to make externalities part of the decision making process of transport users. Capacity constraints are given to the specific highways and railway constraints in terms of capacity, also subtratting passenger vehicles from the total one.

9 3. he applied model In order to evaluate the flow distribution among the abovementioned environment, a MILP model has been developed in order to design the distribution network considering not only private costs but also transport externalities costs. In this Section a MILP model for the problem described above is described. Let us introduce the required notation. Sets and indexes he multimodal network is modelled on a weighted graph G = (V, E) where V is the set of n nodes that is partitioned into the three following subsets: V V : set of h nodes (candidate inland ports); V V : the set of maritime ports (offer nodes); V V : the set of destination nodes (demand nodes). Note that V= V V V and V V V = E is the set of m oriented arcs that is partitioned into the three following subsets: E E: set of arcs representing the road connections between nodes of V \ V and V ; E E: set of arcs representing the railway connections between nodes of V \ V and V ; E E: set of arcs representing the road direct connections from origin to destination nodes. M = {m 1,m 2,m 3,m 4 } is the set of available traveling modalities for o-h-d paths. m 1 road-road: both from o to h and from h to d goods are shipped by truck; m 2 road-rail: from o to h goods are shipped by truck; then, from h to d are shipped by train; m 3 rail-rail: both from o to h and from h to d goods are shipped by train; m 4 rail-road: from o to h goods are shipped by train; then, from h to d are shipped by truck. Concerning the constants included in the model, the following notion has been applied: Nodes weight Cf h Cv h K h q h q h Ce o fixed cost for locating an inland port at node h, h unit handling cost at inland port h, h maximum handling capacity of inland port h, h in/out road capacity of inland port h, h in/out rail capacity of inland port h, h externality cost paid for unit of outflow from port o, o

10 α i portion of the direct out-/in- flow of node i that is in competition for the road capacity on arcs belonging to E. Arcs weight d flow demand from node i to node j,,. c c m h transportation cost of arc (i,j), (i,j) E cost for shipping one UI from node i to node j through inland port h with a combined modality m,,, h, m M Note that c m h is obtained by combining in accordance with m transportation costs c Flow Capacity for arcs for each modality q flow capacity of arc (i,j) with road modality, (i,j) E q flow capacity of arc (i,j) with rail modality, (i,j) E. Maximum flow for arcs for not paying externality costs cq max flow for no congestion on arc (i,j) with road modality, (i,j) E nq max flow for no noise on arc (i,j) with road modality, (i,j) E nq max flow for no noise on arc (i,j) with rail modality, (i,j) E Externality fixed costs for arcs (paid when the maximum flow is exceeded) cc congestion cost of arc (i,j) with road modality, (i,j) E nc noise cost of arc (i,j) with road modality, (i,j) E nc noise cost of arc (i,j) with rail modality, (i,j) E. Referring to the specificity of the present problem, the main decisions concern the number of inland ports to locate and their location, together with the transportation modalities to be used for shipping the required volume of goods from origins to destinations. he following variables are then defined: Flow variables: x hm 0, i V,, h V, m M: flow from port i to destination node j through inland port h with combined modality m; xd 0, i V, : flow from port i directly shipped to destination node j;

11 f 0, (i,j) E road flow on arc (i,j); f 0, (i,j) E rail flow on arc (i,j); of i 0, i V, road outflow from port i. Inland port location variables: y h {0,1}, h V, indicate which inland ports are chosen; in particular, y h =1 if node h is chosen, y h = 0 otherwise. Externalities variables: z {0,1}, (i,j) E congestion variables n {0,1}, (i,j) E road noise variables n {0,1}, (i,j) E rail noise variables he proposed MILP formulation for the problem here considered is the following: min h m M c m h x hm + c ( i, j) E xd + Cfh yh + Cvh h h m M V + subject to cc z + nc n + nc n + i i ( i, j) E ( i, j) E ( i, j) E (1) Ce of x hm x + hm h m M m M x hm hm1 K h xd y h d h i hm2 j, 2 x x + x q y h (4) + hm4 h h (2) (3) 2 x x + x q y h (5) hm3 + hm2 hm4 h h f ih = xhm + xhm i α xd q h, i (6) ih

12 f f f hj = x + x + xd q h, j hm hm α j hj (7) 1 4 ih = xhm + 3 hj x = x + x hm4 q q ih h h, j, i hm2 hm3 hj (9) (8) f f cq z 0 ( i, j) E (10) nq n 0 ( i, j) E (11) f of nq * n 0 ( i, j) E (12) = i xhm + xhm h h xd i (13) x hm 0, i, j, h, m M (14) xd 0, i V, f 0, (i,j) E f 0, (i,j) E of i 0, i V, y h { 0,1} h, z {0,1}, (i,j) E n {0,1}, (i,j) E n {0,1}, (i,j) E he objective function (1) minimizes the traveling, location, handling costs at the inland terminal nodes and external costs paid for congestion and noise. he (2) represents the demand constraints for each o-d demand. Looking at Figure M1, it is to note that d can be satisfied by sending goods on different o-h-d paths, by using different combined modalities and by sending directly from the origin node i to the destination one. Constraints (3) establish the maximum handling capacity of the inland terminals. As it is depicted in Figure M2, for checking the capacity (K h ) of the inland port h it is necessary to sum all the flows passing on o- h -d paths. If the inland port is not chosen, i.e. y h =0, its capacity is zero.

13 Fig M2: Capacity constraints for inland terminals Fig M1: emand constraints Constraints (4) and (5) are related to the handling capacity of the inland terminals; in particular, if h is chosen, the total amount of in/out flow from h via truck (4) (or train (5)) has to be less or equal than its capacity q h (q h ). therwise, no flow can enter or leave node h. For checking the capacity on rail flow in inland port h it is necessary to sum the flows passing on o- h -d using combined modalities that includes rail, i.e. m 2, m 3 and m 4, while for the capacity road flow the combined modalities that includes road are m 1, m 2 and m 4 (see Figure M3 and M4). Fig M3: Capacity on rail flow in inland ports Fig M4: Capacity on road flow in inland ports Constraints (6)-(9) are the arc capacity ones; more precisely, (6) and (8) are the capacity constraints for the arcs connecting nodes i V to nodes h V by the road and the rail modality, respectively, while (7) and (9) are the road and the rail capacity constraints for the arcs connecting nodes h V to nodes j V. Note that for imposing the capacity constraint on each arc of the network for each modality, it is necessary to compute the exact either rail or road flow going through each arc. For example, the rail flow along the arc (h,j) can be obtained as the sum of the flow going from all

14 nodes i to node j, through node h by both combined modality m 2 (that uses the truck on the arc (i,h) and the rail on arc (h,j)) and m 3 (that uses the train both on arc (i,h) and (h,j)). (see FigureM6) Note that the road flow along the arc (i,h) (see Figure M5) can be obtained as the sum of the flows going from i to all destination nodes j through node h by both combined modality m 1 (that uses the truck both on the arc (i,h) and (h,j)) and m 2 (that uses the truck on the arc (i,h) and the train on (h,j)), plus a given percentage of the flows that are directly shipped from i to destination nodes. Constraints (10)-(12) set the externalities variables to one when the road rail flows are higher than the given maximum value for not paying externality costs. Constraints (13) compute the total road outflow of each origin node. Finally, (14) define the decision variables. Fig M5: Road flows on arcs Fig M6: Rail flows on arcs 4. Conclusion and future work development As introduced above, the link between optimal flow distribution among competing transport solutions and external costs may induce an optimization of intermodal transport thanks to the location of logistics parks able to rationalize and optimize railways transports. he proposed original model might bind these two issues in order to solve several problems. In fact, the described model even if it is still in a testing phase shows promising provisional results that will be able to (i) promote new intermodal connections in the North of Italy, thanks to the rationalization of logistics hubs spread in the North Italian regions, and (ii) make internalization policies easier thanks to possible advantages related to the increasing transport distribution efficiency.

15 Furthermore, results will be used in order to discuss specific transport policies able to incentivise transport decisions and to aggregate flows on specific inland ports, with the aim to also reduce possible externality concentrations in the specific points of the network, such as the city centres (such as in the city of Genoa, due to the mix transport flows). Future research will be then focus on the finalization of the current research in order to test the proposed model to the real case scenario and then propose actual transport plans for the current network.

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