Vertical collaboration for multi-modal and multi-carrier transportation chains bears great synergy potential, enabling the integration of otherwise isolated proprietary transport networks, and thus facilitating the flow of goods and creating a more efficient and flexible transport system. However, collaboration requires close alignment in the form of data exchange and integration of information systems, which creates dependencies and a risk of disruption through technical failure, cyber attacks, or organizational conflicts. Earlier works has shown that disruption in interdependent networks can propagate and lead to a cascade of failure, which casts doubt on the claim that more collaboration has a solely positive impact. This research aims at identifying and quantifying a trade-off in collaborative transport systems between synergies and vulnerabilities depending on the level of collaborative connectivity. Therefore, a multi-layer network model for collaborative transport systems is coupled with a model for propagation of intended or unintended data disruption, and the impact on network performance under varying levels of collaborative connectivity is observed for two classes of random networks as well as for the European intermodal transport service network. We find that increasing collaborative connectivity does not have a monotone effect on performance, but there is a threshold, beyond which additional collaborations have an adverse impact on the robustness of the system. Below this threshold, more collaborations have a mostly positive impact on performance, since unused synergy potential is high while the risk of disruption causing a cascade is low. Above it, failure cascades become larger and more likely while the marginal added synergies are diminishing. The formation of highly connected cliques of carriers in the collaboration network facilitates the propagation of failure and leads to a lower threshold beyond which vulnerabilities offset synergies.

Collaborative transport creates synergies as it enables efficient use of decentrally operated transport resources. However, extensive collaboration between carriers also comes with vulnerabilities, which can make disruptions very impactful. While the synergies of collaborative transport are widely addressed in the literature, existing transportation research models are not able to capture the vulnerabilities to disruption induced by collaboration. We aim to fill this gap by establishing a novel multi-layer network model capturing the functional dependence between the physical transport network, of which performance is impacted by the disruptions, and the network of carrier collaborations, which is being disrupted. The model is applicable to particular instances of collaborative transport networks generated from data or from random graphs. Moreover, by integrating the constraints imposed by collaborations into existing analytical methods for the analysis of network properties, the model allows for an analytical derivation of the impact of structural properties of characteristic random network populations on vulnerability. Using the model on a mix of probabilistic network populations and populations of networks generated through simulation, we show that market structure, represented by disparity in carrier sizes, has a non-trivial impact on the vulnerability of a collaborative transport network to targeted disruption at the collaborative level, resulting from the interplay between a system's dependence on collaboration and its susceptibility to targeted disruption. Networks are most vulnerable if they have intermediate disparity in carrier sizes.

The competitive position of sea ports depends on their capability to forward incoming cargo from overseas to its final destination. Such capability is associated with the hinterland connectivity of the sea port. Hinterland connectivity of a port is mostly treated as a local indicator, describing the number of different hinterland locations served by a port via a direct service. However, with intermodality being on the rise, transfer connections and connections including multimodal transshipment become more feasible and common. This turns hinterland transport into a complex multimodal network, whose connectivity cannot be captured by the existing local notion. Using methods from the science of complex networks, this work extends the notion of hinterland connectivity by non-local (network) and multimodal aspects, and uses this notion to analyze hinterland connectivity for the European hinterland transport network of scheduled rail and barge services. The results show that overall structural capability to perform hinterland transport assignments increases tremendously as transfer connections and multimodal routes are established. Moreover, non-local measures show that ports with poor local connectivity can still be well positioned within a network, an insight that would be overlooked in a purely local analysis. Last but not least, all ports benefit individually from multimodal integration, but some do more than others. For instance, 'Multimodal hubs' are the most important contributors to multimodal integration, but their relative accessibility does not improve much.