Synchromodal versus Intermodal Freight Transport: The case of the Rotterdam Hinterland 1 Container Transport 2 M. Zhang1,2,* A. J. Pel1 3 1
Delft University of Technology, Transport and Planning Department, The Netherlands 4
2
TNO (The Netherlands Organisation for Applied Scientific Research), Sustainable 5
Transport and Logistics Department, The Netherlands 6
* Corresponding author: E: mo.zhang@tudelft.nl, T: +31 (0)15 27 85833, Stevinweg 1, 7
2628 CN Delft, P.O. Box 5048, 2600 GA Delft, The Netherlands 8 9 10 11 12 13 14 15 16 17 18 19 20 Words Count 21
Text: 5496 words ( of which, Abstract: 118) 22 Tables: 0 x 250 = 0 words 23 Figures:8 x 250 = 2000 words 24 Total: 7496 words 25
Submission date: 09 Nov 2015 26
Abstract
1
The synchromodal freight transport services is emerging and developing fast as a new 2
concept of freight transport operations. Various benefits of synchromodal freight transport have 3
been hypothesized in the Dutch logistics sector. This paper develops a model that enables 4
comparative analysis of intermodal and synchromodal operations. The model captures relevant 5
(day-to-day and within-day) dynamics in freight transport demand and supply, flexible 6
multimodal routing with transfers and transhipments. The schedule-based assignment algorithm 7
operating specifically at path level allows strategic modelling and evaluation accounting for the 8
freight transport system at operational level. The newly developed model is used to study the 9
Rotterdam hinterland container transport, to evaluate the performance of intermodal and 10
synchromodal operations from economic, societal, and environmental perspectives. 11
INTRODUCTION 1.
1
A key goal of European hinterland freight transport policy over the past two decades has 2
been to achieve a modal shift towards sustainable modes [1], and to catalyse economic growth by 3
facilitating the increase in freight transport demand while retaining a sustainable transport system 4
[2]. These efforts to promote modal shift entail: subsidizing intermodal freight transport [3], 5
pricing road freight transport [4], liberalizing the freight transport market [4], and improving 6
freight transport service quality [5]. Despite these efforts, the shift away from road transport has 7
been limited, necessitating an additional action plan [5]. The Netherlands shares this situation, 8
where intermodal transport has difficulties to compete with road transport, especially for haulage 9
distances shorter than 300 km. This is because intermodal transport in comparison tends to 10
provide less flexibility, lower reliability, extra transhipment costs, longer delivery times, and less 11
robustness. Therefore an emerging emphasis is now given to the design of services and the 12
cooperation of multiple service providers at operational level aimed at synchronization of 13
intermodal transport services. As the hinterland transport is highly developed in the Netherlands, 14
a synchromodal system is emerging and developing fast as a new concept of freight transport 15
operations. Hence it is now also on the agenda of the Dutch government and sector institutes, 16
including the Dutch Top Sector Logistics [6]. 17
Although the term synchronization in transport has been used before in literature 18
concerning freight transport, for example indicating a seamless supply chain [7] or an integrated 19
information-material flow [8], the concept of synchromodal transport is different. It can be 20
loosely defined as “the combination of intermodal planning with real-time switching” [6], or in a 21
more specific form: “A shipper agrees with a service operator on the delivery of products at 22
specified costs, quality, and sustainability but gives the service operator the freedom to decide on 23
how to deliver according to these specifications” [9], as proposed by the Dutch Institute for 24
Advanced Logistics. In this paper we follow the latter definition. 25
The reason why synchronization of different transport modes, in terms of both 26
infrastructure and service, has gained so much interest is that it has the potential to reduce 27
delivery times, provide better utilization of the capacity of each mode, and allow for buffering 28
effects between the alternative modes yielding a more robust transport system. In long-term, 29
when these services attract more demand and benefit from scale economies, the haulage and 30
transfer/transhipment costs may also reduce. 31
One of the key underlying reasons for this advantage of a synchromodal system relates to 32
coping with dynamics in both transport demand and supply at operational level. The dynamics in 33
hinterland freight transport demand are mainly caused by variations in demand volumes, large 34
deviations from planned arrival times of sea-going vessels, varying urgency levels due to 35
container dwell time limitations, differences in types of commodities, and different transport 36
conditions required by shippers. For the case of dynamics in the transport supply, these are 37
primarily related to time-window constraints and temporal variations in travel times and 38
capacities because of infrastructure, facility, service, and regulatory constraints (e.g., operating 39
hours at terminals and ship locks, variations in navigability of inland waters, schedule-based rail 40
services, city centre access restrictions). Synchromodal services allow service operators more 1
flexibility at operational level to respond to such dynamics. 2
However, to evaluate the performance of a synchromodal transport system brings new 3
challenges to transport planning. The network evaluation tools currently used in the field of 4
freight transport modelling are originally constructed for the purposes of long-term strategic 5
infrastructure network planning and medium-term tactical service network planning, and hence 6
are static in nature. Crainic and Laporte [10] and Crainic [11] discuss the main problems, 7
models, and methods in the literature of infrastructure and service freight transport planning 8
according to the three classical decision-making levels: strategic, tactic, and operational. 9
Macharis and Bontekoning [12] provide a review particularly focusing on intermodal freight 10
transport operations. While concluding that earlier operational research has focused mostly on 11
transport problems of uni-modal transport modes, the authors argue that the intermodal freight 12
transport is emerging as a new transport research fields. Since then, intermodal freight transport 13
has attracted increased attention in practice due to problems of road congestion, and 14
environmental concerns. Reviews of (inter)national or regional freight transport model 15
developments are given by Tavasszy et al. [13] and de Jong, et al. [14]. In the literature review 16
by Nuzzolo, et al. [15] it is concluded that among the freight transport forecast models developed 17
in the last two decades only few models are able to deal with large-scale problems while 18
accounting for micro-mechanisms in the underlying demand. Caris, et al. [16] identify the 19
current state of the art in existing decision support models in intermodal freight transport. The 20
methods and models presented in these recent literature reviews indicate the increasing relevance 21
of addressing issues of service network design. This is also supported by the literature review by 22
SteadieSeifi, et al. [17], which reviews the intermodal planning problems and relevant models. 23
There, synchromodal transport is mentioned as “the next step after intermodal and co-modal 24
transportation”, however, “no operations research literature had been found where synchromodal 25
is used”. While at the same time the literature on synchromodal freight transport is scarce. 26
What is lacking is a model for strategic planning that adequately incorporates dynamics at 27
operational level and thus allows comparative analysis between an intermodal and synchromodal 28
transport system. 29
In this paper we address this gap as (1) we present a new strategic model (referred to as 30
Synchromodal Modelling Operator) that meets the key requirements for simulating 31
synchromodal services, and that embeds two flow assignment methods representing respectively 32
a synchromodal and intermodal system, and (2) we apply the model to compare the performance 33
of a synchromodal system with that of an intermodal system for the hinterland container 34
transport of the port of Rotterdam. The paper is structured accordingly. 35
SYNCHROMODAL MODELLING OPERATOR 2.
36
Traditionally, in flow assignment models used for freight transport the mode choice and 37
path choice were modelled and computed sequentially, for example, in early models proposed by 38
Sheffi [18] and Crainic and Rousseau [19]. Later also the choice of service lines was 39
incorporated, for example by Crainic and Laporte [10], by specifying alternative service lines 40
and including their relevant attributes (such as delay and capacity restrictions) in the generalised 1
cost function used for the flow assignment. More recently, Zhang, et al. [20] extended this 2
further to include the influence of terminal operations on this choice, thereby integrating mode, 3
terminal, and service line decisions. 4
These models, and the vast majority of freight transport models in general, describe the 5
flow assignment as a function of static and aggregated attributes. That is, the available transport 6
alternatives, the factors in their generalised cost functions, and the resulting network flows are 7
typically modelled for time resolutions no finer than annual averages or aggregates. However, as 8
mentioned earlier there are dynamics in the freight transport system working at operation level, 9
which is a much finer than annual scale, that may influence the system performance. And more 10
importantly, the effects of these dynamics are different depending on the (intermodal or 11
synchromodal) transport system. Hence we argue that these dynamics are essential to include 12
when one wants to do a comparative analysis of intermodal and synchromodal freight transport. 13
Model Context 2.1
14
In the ensuing we consider a model that enables comparative evaluation of a transport 15
system providing intermodal or synchromodal services, from a governmental perspective. For 16
the model application we consider the container distribution in the lower section of the Rheine 17
corridor, from the port of Rotterdam to its hinterland as far as the Duisburg area. The underlying 18
reasons for this focus, and some implications, are elaborated on in this section. 19
First, the model compares intermodal and synchromodal transport systems, in principle 20
excluding adaptations to the existing transport infrastructure and services. The model application 21
further below is hence based on the current freight transport system, incorporating the relevant 22
motorway network, the freight rail network, and the navigable inland waterway network as 23
currently available for hinterland transport in the Rheine corridor. All modes are in principle 24
available for all shipment orders. These are, direct road transport, intermodal rail transport, and 25
intermodal inland waterway transport, where the intermodal modes require pre- and end-haulage 26
by road transport, typically charged at a slightly higher TEU-km price than for direct road 27
transport. Furthermore, the modelled services represent currently available services, including 28
their characteristics of fleet size, frequency, route, speed, and travel time. 29
Second, the model differentiates between network flows resulting from intermodal or 30
synchromodal services. Intermodal services relate to the situation in which shippers decide on 31
the transport mode and subsequently book the specific transport services with operators who 32
provide these services. In this case, the specific route of a shipment is determined by the 33
shipper’s mode choice and the operator’s service line and departure time choices. Contrarily, on 34
the assumed synchromodal services, shippers only dictate the shipment requirements, for 35
example, a preferred delivery time and transport conditions. Here the transport operator can 36
choose the complete shipment route including transport mode, service line, transfer/transhipment 37
terminal, and departure time. Thus, although the overall transport demand profile (in terms of 38
release time, commodity type, hazmat category, and volume) is unchanged, evidently, the 39
operator’s choice set of shipment routes is larger in a synchromodal setting than in a traditional 40
intermodal setting. Also, flexibly combining various modes of transport within one route may be 1
easier to achieve. 2
Third, the model evaluates these intermodal and synchromodal systems from a 3
governmental perspective. This governmental perspective implies that the model results should 4
predominantly provide insight into the overall system performance, in terms of economic, 5
societal, and environmental impacts. Insights into the effects on, for example, individual 6
transport operators or the competition / collaboration between operators are not included as a 7
requirement. Hence, the model should yield the following indicators. 8
Economic impacts
9
Total system costs, and cost composition 10
Total system time expense, and time composition 11
Capacity occupancies of service lines 12
Societal impacts
13
Network flows, and flow concentration 14
(Reduction in) Road traffic, both as direct road transport and as pre- and end-haulage 15
Environmental impacts
16
Emissions (e.g. carbon dioxide emissions) 17
Fourth, the model application describes the container distribution from the port of 18
Rotterdam to its hinterland. This geographic focus is chosen for its well-developed road, rail, and 19
inland waterway transport facilities and a large freight transport demand. We focus on container 20
transport because of its convenience in the intermodal handling. This relaxes the equipment-21
related and shipment-size-related constraints when modelling mode choice. 22
Fifth, the model captures the (most) relevant dynamics in the Rotterdam hinterland 23
container transport. This is required because the advantages of a synchromodal system over the 24
existing intermodal system are partially due to more flexibility in reacting to these dynamics. 25
Hence the model should be able to appropriately capture these dynamics, and how they affect 26
transport operations differently for the intermodal and synchromodal systems. 27
Modelling Approach 2.2
28
To model synchromodal services, we develop a new model (called SynchroMO, which 29
stands for Synchromodal Modelling Operator) that consists of (1) a demand generator, (2) a 30
super-network representation of the multimodal transport system, (3) a schedule-based flow 31
assignment algorithm, and (4) a system performance evaluator. The characteristics of the first 32
three modules are presented in this section, after which in the next section the evaluation module 33
is presented together with the case study for the Rotterdam hinterland container transport. More 34
details on the mathematical formulation and solution algorithm of the model can be found in 35
[21]. 36
Demand generator. The demand generator module describes the dynamic profile of 37
container shipments, thereby specifying, destination, release time, commodity type, hazmat 38
category, and in case of an intermodal system also mode preference. In order to adequately 39
capture the essential time dynamics mentioned earlier, we wish to model the transport demand in 1
terms of all container shipments within a 24-hour period. Due to the fact that empirically 2
observed demand profiles are generally only known at the annual level (as also the case for the 3
port of Rotterdam application presented in the next section), we propose to generate the 24-hour 4
demand by random sampling from the annual transport demand. Note that the annual demand 5
profile can be a multivariate empirical distribution, and need not be fitted to any specific 6
statistical function. By directly drawing from the multivariate distribution we maintain existing 7
correlations between the dimensions, for example, correlation between destination and 8
commodity type, and between destination and mode. Shipments are then sampled to construct 9
the transport demand for a 24-hour period, thus ensuring that the 24-hour demand profile 10
approximates the annual shares in origin-destination, release time, commodity type, hazmat 11
category, and mode. Furthermore, to encapsulate the randomness in these 24-hour samples, we 12
use repeated sampling in a Monte Carlo simulation. Within each Monte Carlo iteration the 13
sampled 24-hour demand is assigned to the network, while the repeated Monte Carlo simulation 14
shows the transport demand including stochastic effects (emulating day-to-day randomness). 15
Super-network representation. The multimodal transport system is represented as a 16
super-network [22, 23] with corresponding link characteristics and generalised cost functions. 17
This way, each link is an access/egress link, a haulage link, or a transfer/transhipment link. In 18
this network representation access/egress links are virtual ‘haulage’ links connecting the 19
origins/destinations with the network. Haulage links relate to physical infrastructure or transport 20
services, for example, road infrastructure segments, and rail and inland waterway service line 21
segments, and hence may belong to a specific mode. Both access/egress links and haulage links 22
are associated with haulage costs, commodity varying freight value loss, and vehicle value loss. 23
The haulage costs are both distance and time dependent. Transfer/transhipment links relate to 24
terminal activities, such as dwelling (waiting for handling), handling (loading and unloading), 25
and waiting for departure. Freight can be transferred among different service lines within the 26
same mode, or be transhipped among different modes. The costs for transfer/transhipment links 27
(being only time dependent) may consist of handling costs, as well as freight value losses, and 28
vehicle value losses, depending on which activities are represented by the specific link. More 29
specifically, handling incurs all three cost components; dwelling incurs freight and vehicle value 30
losses; waiting for departure only incurs freight value loss. Without loss of generality, a link can 31
be defined with respect to the next service based on the assumption that no overtaking occurs 32
among services along the same line. Road transport services can be assumed to be available at 33
any time, and hence road haulage links can be accessed at any time. 34
Schedule-based flow assignment. The schedule-based flow assignment module describes 35
the dynamic network flows as a result of operators’ decisions on terminal, service line, and path, 36
and in case of an intermodal system also transport mode. Each shipment is assigned to a route in 37
the super-network, where such a route specifies the mode(s), terminal(s), service line(s), and 38
path(s) (associated with a specific departure time) used for this shipment. Systematic capacity 39
shortage (with respect to fleet, infrastructure, or terminal) is rarely observed in practice for most 40
well developed hinterland freight transport systems, including that of the Rheine corridor where 41
systematic capacity shortage. The flow assignment therefore reduces to a schedule-based shortest 42
route problem, and can be solved using an incremental assignment procedure. In this procedure, 1
shipments are iteratively assigned to the schedule-based shortest route, accounting for time and 2
commodity varying generalised costs (where cost levels as well as freight value loss weights are 3
commodity specific), and subject to various constraints. Constraints are imposed to ensure that 4
the route adheres to predefined time restrictions such as a maximum initial waiting time at the 5
origin terminal, a maximum dwelling time, a maximum transfer/transhipment waiting time, and a 6
maximum shipment delivery time. For hazmat, and additional constraint ensures that the number 7
of transfers/transhipments along a route is for safety reasons limited to a maximum number of 8
handling operations. And finally, for the intermodal system an additional constraint ensures that 9
the route uses the preferred transport mode. 10
This modelling approach combining a demand generator, a super-network representation of 11
the transport system, and a schedule-based flow assignment yields the dynamic route flows for 12
synchromodal or intermodal transport. These route flows are used by the performance evaluator 13
module to compute various system performance indicators. This is demonstrated in the next 14
section where we assess synchromodal and intermodal transport according to their economic, 15
societal, and environmental impacts. 16
APPLICATION TO ROTTERDAM HINTERLAND CONTAINER TRANSPORT 3.
17
The SynchroMO model for intermodal and synchromodal hinterland freight transport is 18
implemented in the TransCAD® GIS-based transport planning software. This section presents the 19
results from a model study for the container transport network of the port of Rotterdam 20
hinterland. The following subsection first describes the case study and used data sources, after 21
which presents the model results in terms of the key performance indicators as computed by the 22
system performance evaluation module. 23
3.1 Case Study Description 24
For the case study 18 regions are selected along the lower section of the Rheine corridor as 25
main destinations for the hinterland container transport, where the selection is based on historical 26
flows. The annual demand pattern is estimated based on the Basic Database Freight Transport 27
2004 (see [24] for an introduction) constructed from a survey among Dutch transport service 28
operators conducted by the Dutch Central Bureau of Statistics. The database records relate to 29
freight flows to, from, and through the Netherlands in 2004. From this database we extract the 30
container flows from the port of Rotterdam towards the selected 18 regions, and derive the 31
empirical (cumulative) distributions for the shipment destinations, release times, commodity 32
types, hazmat categories, and transport modes. These distributions are directly used, given that 33
the production and consumption structure of the study area have not changed much. However, 34
the total 24-hour demand (in TEUs) is based on the average daily demand in 2012 (which is also 35
used as base year for the transport network services and costs parameters). The final origin-36
destination, release time, commodity types, and mode distributions used in the analyses are 37
presented in Figure 1. The shares of containers with hazmat are comparable among the 18 38
destinations, such that we can use an overall share of 2% of all TEUs. 39
(a) TEU demand per destination (b) Release timestamp
(c) Commodity type per destination 1
2
(d) Transport mode per destination 3
Figure 1. Empirical distributions of container flow for Rotterdam hinterland, according to (a) 4
destination, (b) release timestamp, (c) commodity time, and (d) transport mode 5
The transport demand for one day is sampled from these empirical distributions, and 6
Monte Carlo (MC) simulation with repeated random sampling is used to represent the stochastic 7
demand. The number of MC iterations should be large enough to represent the daily demand 8
variations with sufficient accuracy. Based on preliminary test results the number of MC 9
iterations is set to 30. The performance indicators presented in the next section represent either 10
the distribution of effects across these 30 runs of MC simulation, or the average effects if 11
specified as such. 12
The freight flow demand is assigned to the super-network that comprises of infrastructure 13
links, service line links, and transfer/transhipment links. The infrastructure network is 14
constructed on the basis of the geographic information system embedded in TransTools [25]. The 15
network of the study area consists of 1922 nodes (including 1747 road nodes, 70 rail nodes, 65 1
inland waterway nodes, 21 hinterland terminals, and 19 regional centroids) and 2675 links 2
(including 2445 road segments, 86 rail service segments, 72 inland waterway service segments, 3
19 access/egress links, and 53 transfer/transhipment links). Figure 2 illustrates this super-4
network. The service network is constructed based on information collected from transport 5
service operators and consists of 6 rail service lines and 12 inland waterway (IWW) service lines. 6
Figure 3 illustrates this service network. Service lines are defined as having a dedicated path, 7
capacity, haulage time, and frequency. The frequency of a service line is converted into a 8
timetable (assuming regularity) with departure and arrival times at terminals that are then used in 9
our schedule-based assignment. These costs and value loss sensitivities are based on the facts 10
and estimates reported by Dutch freight transport research organizations and consultancies [26-11
29], and a number of earlier surveys and enquiries conducted by the authors among transport 12
operators. 13
14
Figure 2. Rotterdam hinterland infrastructure network (Map visualized with TransCAD®) 15
1
Figure 3. Service lines for Rhine corridor (Map visualized with TransCAD®)
2 3
Finally, for the Rotterdam hinterland case study the time constraints are relaxed in the 4
intermodal systems to simulate the situation in practice. In the synchromodal system, to ensure 5
the assumed service level, these constraints are explicitly set as: maximum initial waiting time = 6
360 min, maximum dwelling time = 60 min, maximum waiting time = 240 min, and maximum 7
transport time = 3 days. The maximum number of handling operations for a hazmat shipment is 8
set as 2 (i.e. only one leg of rail or inland waterway haulage is allowed). 9
The following subsections present respectively the economic, societal, and environmental 10
performance indicators of the intermodal and synchromodal transport systems as computed by 11
the evaluation module within our model. 12
3.2 Economic Performance Indicators 13
Figure 4 presents the total system costs for the intermodal and synchromodal systems 14
including haulage costs, handling costs, freight value losses, and vehicle value losses. In 15
comparison, synchromodal system slightly reduce the overall system cost. However, much larger 16
differences are found in the cost composition for some specific activities. The synchromodal 17
system benefits from lower haulage costs, largely due to the reduced road haulage as shown in 18
Figure 5, while the haulage by rail and inland waterway increases. These modes do necessitate 19
more handling. 20
1
Figure 4. Total system costs of intermodal or synchromodal systems 2
3
4
Figure 5. Haulage distance of road transport 5
6
The relative reduction in road haulage in the synchromodal system, additionally impacts 7
the path occupancies of the service lines. The path occupancy of a service line is defined as the 8
ratio between the path flow and its capacity, where the ratio is taken as the distance-weighted 9
average over the whole path. Capacities are assumed to be 70 TEU per path for rail service lines 10
[30], and to be between 32 TEU and 200 TEU per path for inland waterway service lines 11
depending on the navigating condition of the path. Our analysis shows that path capacity 12
occupancies are 4% to 48% higher, and 8% higher on average, in the synchromodal system 13
compared to the intermodal system. These paths where the occupancy in synchromodal system 14
exceeds the current capacity typically depart in the morning and/or are shuttle services. 15
A comparison of transport times of intermodal and synchromodal systems is given in 16
Figure 6. The overall lower total time spent in the synchromodal system is largely due to the 17
constraint on a maximum initial waiting time at the origin terminal (of 6 hours). Furthermore, we 18
observe waiting time for transfer and transhipment in the synchromodal system, but not in the 19
intermodal system. This implies that in some cases the optimal route contains more than one 20
transfer/transhipment. This is not commonly observed in operations in practice. 21
1
Figure 6. Total time spent in intermodal and synchromodal systems 2
3
Therefore Figure 7 presents a breakdown of the average time spent for all shipments other 4
than those via direct road transport. Here the analysis shows that the average delivery time is 5
about 20 hours for intermodal services and about 13 hours for synchromodal services. The 6
reduced delivery time in the synchromodal services is due to much shorter waiting time at the 7
origin terminal (as is restricted to a maximum of 6 hours) and shorter rail and inland waterway 8
haulage time, where the latter is mainly caused by a modal shift from slower inland waterway 9
transport to faster rail transport (a large part operating on the dedicated freight rail track). The 10
fact that the synchromodal system yields shorter delivery times implies that it enables modal 11
shift from road to other modes without sacrificing the service quality in terms of transport time. 12
13
14
Figure 7. Average time spent per activity for non-direct-road shipments 15
16
3.3 Societal Performance Indicators 17
Compared with intermodal system, synchromodal system may yield a shift away from 1
direct road haulage as is shown in Figure 5. As mentioned earlier, this is mainly ascribed to 2
synchromodal system providing higher flexibility in departure times and more 3
transfer/transhipment opportunities in the hinterland. 4
Furthermore, in Europe 300 km is commonly recognized as the threshold distance beyond 5
which intermodal transport can be competitive with direct road transport [31]. Our model results 6
show that the average delivery distance of non-direct-road transport in the synchromodal system 7
is 120 km, compared with 158 km in the intermodal system. The furthest destinations in our case 8
study is about 250 km away from the origin by road, which is shorter than the threshold distance, 9
such that we cannot evaluate the market coverage of the synchromodal services based on the 10
European reference. However, the shorter average delivery distance compared with the 11
intermodal system indicates that the synchromodal services would enlarge the range of 12
competitive delivery distance of non-direct-road transport services. This demonstrates that 13
synchromodal services are a feasible solution for modal shift from road to the other modes. 14
The flows in TEU movements at hinterland destination terminals are on average 30% 15
higher in the synchromodal system than in the intermodal system. This introduces more truck 16
traffic in the surrounding of inland terminals. At the same time the average end-haulage distance 17
from 25 km in the intermodal services to 14 km in the synchromodal services. This increase and 18
concentration in truck traffic around destination terminals may locally possibly introduce new 19
concerns that need to be addressed regarding road congestion and noise emissions. 20
3.4 Environmental Performance Indicators 21
The environmental impacts are assessed according to CO2 emissions measured in kg, 22
where we account for vehicle type (including 10-20 ton truck, electricity-driven train, and 32, 96, 23
or 200 TEU capacity barge), average vehicle speed, and load factor. The formula is given as 24
follows, adapted from the methods proposed by den Boer, et al. [30] and Schmied, et al. [32]. 25
CO2 emissions of rail or inland waterway transport are non-linear. 26 𝐸𝐶𝑂2= 𝛼 ⋅ ∑ [ℓ𝑖𝑗 ⋅ 𝑓𝑖𝑗] 𝑖𝑗∈𝐿𝑅𝑂𝐴𝐷 + 𝛽1⋅ ∑ ∑ [ℓ𝑖𝑗 ℓ𝑞𝑖𝑗(𝑓𝑞 𝑖𝑗+ 𝜔 𝑇𝑅𝐴𝐼𝑁) 𝛽2 ] 𝑖𝑗∈𝑞 𝑞∈𝑄𝑅𝐴𝐼𝐿 + ∑ ∑ [ℓ𝑖𝑗 ⋅ 𝛤(𝑓 𝑞𝑖𝑗, 𝜔𝐼𝑊𝑊,𝑞, 𝜅𝐼𝑊𝑊,𝑞)] 𝑖𝑗∈𝑞 𝑞∈𝑄𝐼𝑊𝑊 27
Here ℓ𝑖𝑗 and 𝑓𝑖𝑗 denote respectively the link length and link flow, 𝜔𝑇𝑅𝐴𝐼𝑁 and 𝜔𝐼𝑊𝑊,𝑞 28
denote the weight of respectively an unloaded train, and unloaded barge operates on an inland 29
waterway path 𝑞. 𝜅𝐼𝑊𝑊,𝑞 denotes the barge capacity operating on path 𝑞 which belongs to the 30
set of all inland waterway paths 𝑄𝐼𝑊𝑊. Note that 𝜔𝐼𝑊𝑊,𝑞 and 𝜅𝐼𝑊𝑊,𝑞 are path dependent as
31
they differ according to waterway class. Emission factors are set in accordance with den Boer, et 32
al. [30] and Dutch Central Bureau of Statistics [33], where we set 𝛼 to 800 gram/vehicle-km 33
(based on a truck with 0-20 ton load and running on a European road), set 𝛽1 to 606 and set 𝛽2 34
to 0.38 (based on an electronic driven locomotives with electricity supplied from the 35
Netherlands). The function Γ(∘) determining barge emissions per vehicle-km is derived from 1
the database of the Dutch Central Bureau of Statistics [33] accounting for the load factor, barge 2
category, and inland waterway class. 3
Figure 8 shows the CO2 emissions in intermodal and synchromodal systems. It can be 4
observed that the synchromodal system yields an overall reduction of 31% in CO2 emission. This 5
is due to the modal shift from road (and inland waterway) to rail, and to a lesser extent also due 6
to the higher capacity occupancies of train and barge services. 7
8
9
Figure 8. Total CO2 emissions of intermodal and synchromodal systems 10
CONCLUDING REMARKS 4.
11
The benefits of synchromodal freight transport have been hypothesized in the Dutch 12
logistics sector as the potential to reduce delivery times, provide better utilization of the capacity 13
of each mode, and allow for buffering effects between the alternative modes yielding a more 14
robust transport system. An adequate model to quantify these effects is lacking until now. In this 15
paper we develop such a model that enables comparative analysis of intermodal and 16
synchromodal operations. The model captures relevant (day-to-day and within-day) dynamics in 17
freight transport demand and supply, transport time constraints, flexible multimodal routing, 18
freight handling processes related with transfers and transhipments, and transport costs related to 19
haulage, handling, freight value loss and vehicle value loss. The schedule-based assignment 20
algorithm operating specifically at path level allows strategic modelling and evaluation 21
accounting for the freight transport system at operational level. This setup enables incorporating 22
the load factor when evaluating economic and environmental impacts. 23
Furthermore, in this paper we quantitatively analyse the effects of synchromodal freight 24
transport as to economic, societal, and environmental performance indicators. This is done by 25
applying SynchroMO to the case of the hinterland container transport of the port of Rotterdam. 26
The model study led to the following findings. In comparison to intermodal transport, 27
synchromodal transport yields overall lower haulage costs and higher handling costs, resulting in 28
comparable total system costs. Despite the lack of direct economic benefit, service line 29
occupancies are increased (by 8%) and delivery times are reduced (by 12%). The latter 1
observation suggests that synchromodal services can provide higher service quality compared 2
with traditional intermodal transport in terms of delivery time. Synchromodal transport has clear 3
benefits from a societal and environmental perspective, as it facilitates a modal shift away from 4
road transport, yielding a reduction in CO2 emissions (of 31%). Synchromodal services also 5
enlarge the range of competitive delivery distance of non-direct-road transport services, further 6
supporting modal shift towards sustainable transport modes. A consequence of this modal shift is 7
higher throughput at hinterland destination terminals, and hence a concentration of truck traffic 8
in the vicinity of these terminals (within 15 km radius). 9
One aspect of synchromodal transport system that is not explicitly considered in this study 10
is its effect on the reliability and robustness of the system. Future research could hence be 11
recommended to extend the presented model to particularly incorporate the on-trip dynamics 12
regarding infrastructure and service variations and disruptions (i.e. supply side) as well as 13
transport operators’ responses in terms of rerouting decisions (i.e. demand side). This would 14
enable to study the role of synchromodal services in system reliability and robustness. 15
16
Acknowledgements
17
This research was partially funded through the Delft Infrastructures & Mobility Initiative (DIMI) 18
at Delft University of Technology. 19
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