Taking into account aspects of complexity theory such as resilience, vulnerability, emergence and criticality, this project aims to develop principles for robust network design and a computational model that provides access to: (1) real-time information about the dynamic state of a metropolitan area regional transportation network, including travellers on it, with a focus on assessment of resilience to disturbances; and (2) short term and long term predictions regarding the future state, with focus on mitigation of vulnerability. In addition, a pilot personal travel information service developed by WeCity will be tested that exemplifies the usage potential of available data to make multi-modal transportation more efficient for travellers, giving an opportunity to test tactical mitigation measures and real-time disruption management strategies. A preparatory grant is needed to bring together public transportation operators, ICT service providers, metropolitan area civil servants (Municipality of Amsterdam, Stadsregio Amsterdam) and scientific researchers. This will be carried out in two workshops. In addition expertise outside of the Netherlands will be gathered from Transport for London, Technical University of Denmark, Royal Institute of Technology and Massachusetts Institute of Technology.

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English: This living lab aims to support the creation, development and implementation of next generation concepts for sustainable healthcare logistics, with special attention for last mile solutions. Dutch healthcare providers are on the verge of a transition towards (more) sustainable business models, spurred by e.g., increasing healthcare costs, ongoing budget cuts, tight labor market conditions and increasing ecological awareness. Consequently, healthcare providers need to improve and innovate their business model and underlying logistics concept(s). Simultaneously, many cities are struggling with congestion in traffic, air quality and liveability in general. This calls for Last Mile Logistics (LML) concepts that can address challenges like effective and efficient resource planning, scheduling and utilization and, particularly, sustainability goals. LML can reduce environmental and social impact by decreasing emissions, congestion and pollution through effectively consolidating in-flows of goods and providing innovative solutions for care, wellbeing and related services. The research and initiatives in the living lab will address the following challenges: reducing the ecological footprint, reducing (healthcare-related) costs, improving service quality, decreasing loneliness of frail citizens and improving the livability of urban areas (reducing congestion and emissions). Given the scarcity and fragmentation of knowledge on healthcare logistics in organizations the living lab will also act as a learning community for (future) healthcare- and logistics professionals, thereby supporting the development of human capital. By working closely with related stakeholders and using a transdisciplinary research approach it is ensured that the developed knowledge and solutions deliver a contribution to societal challenges and have sound business potential.

Synchromodality is a highly powerful and promising concept for boosting the efficiency of freight transportation, based on combining multiple transportation modes (barges, trucks, trains) in a smart way. This makes a transition possible from the delivery of plain logistic services to integrated services by exploiting the complementary nature of available transportation modes. The problem is that under the current state of the art, the exploitation of synchromodality is strongly challenged by the inherent complexity of logistic supply chains due to the omnipresence of uncertainty (weather, delays, transport demand, disruptions, traffic dynamics, driver behavior), influencing many decisions of many stakeholders. Motivated by this, we propose in this project the concept of predictive synchromodality, incorporating models, methods and tools based on predictive data analysis and stochastic decision making in distributed control environments, for exploiting the great potential of synchromodality, addressing the question what to transport, how and when. This is the only way in which the gap from the intransparent and inefficient current transport state can be bridged to a streamlined logistic system with improved transport efficiency, higher loading rate of vehicles, less emissions and costs. To secure the alignment of the research to the real needs from the logistics sector, we investigate three real-life use cases identified with our industry partners as an integral part of the project: predictive synchromodality for container logistics, dry bulk logistics, and air cargo logistics.

A fundamental task for language is to provide the rules to map meaning to form. In the communication of an event, these rules should link semantic roles, such as agent and patient, to their grammatical functions, such as subject and object. Despite their importance for communication, grammatical strategies of argument marking (viz. word order, head marking, and dependent marking) are surprisingly limited, complex, and redundant. Why do languages not use a simple and straightforward means of encoding who did what to whom? The starting hypothesis of this project is that natural languages in fact do not have dedicated means to map semantic roles to syntactic functions. Although some constructions are put to use for grammatical marking indeed, they originally evolved for other usages. From this, complex systems have developed in which interacting strategies jointly cover the meaning space. By explicitly taking into account the developmental history of the different strategies, it will be possible to provide a much deeper understanding of the constant rise and fall of grammatical argument-marking strategies in the languages of the world. Thus, it is predicted that the meaning contribution of individual strategies is qualitatively different in different argument-marking systems.