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In this deliverable, the architecture of the NOVIMOVE simulation model is developed. In addition, the necessary data for the model is structured and collected. The system boundary for each of the actors was developed in D.2.1. In the same deliverable, also the required data needed for the model were identified. Finally, the first deliverable also developed the KPIs, which should be computed by the NOVIMOVE transport model. Based on these insights and requirements the first draft version of the NOVIMOVE model is developed. The verification and the validation of the model are done in Task 2.3, which will result in Deliverable 2.3. This model will contain a data input such as seaport terminals, waterways, including locks, bridges and water level fluctuations, inland terminals, roads, railways, and distribution centres. The proposed elements will be modelled based on the requirements set by Deliverable 2.1. Next to that the actual cargo volume transported on the Rhine-Alpine corridor, along with the modal split and the cargo type composition of the container being currently transported along the corridor will be used in the model. All of these data are used as inputs in the transport cost and time module. The data collection process was a complex process. The data comes from many sources, and it is not always available at the desired level of detail. Some countries collect certain information, and others do not, and the data format for the same type of parameters may differ. It was, therefore, necessary in some cases to make assumptions to make data comparable and to fill in the missing information. The data files are stored on the MS Teams folder and will be stored according to the FAIR approach mentioned in Deliverable 6.1. Next to the collection of the data for the model, also the links (plugins) to the NOVIMOVE innovations are created. The main NOVIMOVE simulation model also needs to deal with the NOVIMOVE innovations that are going to be developed in WP2, WP3, and WP4. These innovations are going to impact different sub-models in the main NOVIMOVE simulation. The main simulation architecture is built up from different sub-models (cost and time, mode choice, and assignment) which are embedded in the main model. For the cost & time sub-model, the cost and times are calculated for three modes (road, rail, and IWT). This is also done from the perspective of three actors (barge operators, terminal operators, and shippers). In the mode choice model, the generalized costs have been used to determine the mode choice as it will be more convenient to use regarding the interactions with the other sub-models. Next to the generalized cost also the train departure frequency and the population density are included as extra determinates to estimate the mode choice. Regarding the assignment model, information about existing services and the network representation is used for IWT, but not for road and rail as their network is modelled only at a high abstraction level. Therefore, the assignment procedure for these two modes is performed by the use of heuristics. The main simulation model will be built from different components. First, the scenarios need to be defined (network configuration) along with the composition of the Origin Destination matrix of the container transports. At the start of a simulation run, the model sends initialization details to the submodels, such as network characteristics and details about the services offered for the modalities. When the initialization is done, a repetitive cycle is activated where cargo information is passed to the sub-models (cost and time, mode choice, and assignment). When the initialization is completed, and the first cycle of cargo information is retrieved by the sub-models, the modality split for the cargo is determined by the mode choice sub-model given the input parameters that are calculated by the cost and time sub-models. When the modality split is executed, the assignment model then takes this information and will generate transports for IWT, road, and rail. These transports are then sent – per modality – to the simulation model: the Transport requests. After the transport requests are executed by the simulation model, the NOVIMOVE KPIs will be calculated.

D.2.1 is the first deliverable of WP2 in the NOVIMOVE project. WP2’s mission is to develop a simulation model for today’s IWT container logistics system on the Rhine-Alpine corridor which will also allow detailed analyses of the impact of the NOVIMOVE innovations improving the IWT container logistics. Next to that, new approaches (functions) to improve the current IWT container logistics are developed. WP2 objectives are developing: •A simulation model for the IWT container logistics chains that: orepresents the current situation and delivers a benchmark scenario for NOVIMOVE innovations; oincorporates the NOVIMOVE innovations and determines their impact on the overall performance of the IWT container logistics chain over the Rhine-Alpine route, with respect to the benchmark scenario; •New logistics approaches (functions) to improve the current IWT container logistics: oEnhanced load factors through cargo reconstruction; oImproved port logistics; •Serious game based on the NOVIMOVE transport simulation model to: oTrain/educate actors, stakeholders, and decision-makers in the transport chain to allow for the market uptake of the NOVIMOVE developments; oBe included in academic teaching for transport engineering and transport economics students, awakening their interest and enhancing their competence in IWT integration in logistics planning. In this first deliverable, the detailed requirements of the initial transport model are developed within the domain established in WP1 (Task T1.1). In order to do this a good understanding of the current IWT container logistics system and of the other transport modes (road, rail) for the Rhine-Alpine corridor is obtained. These obtained insights helped define detailed requirements for the logistics model, defining the type of modelling, the model scope, the relevant actors and the necessary data. In this deliverable a literature study was developed and on top of that a questionnaire was developed with which the insights from the main stakeholders could be taken into account. The results of the survey confirm the conclusions from the literature and reveal that inefficient handling of barges, poor planning and coordination, lack of flexibility in container barge transport and low water levels are the most important challenges that the smooth operations container barge transport currently face. Additionally, the survey revealed the major stakeholders within container barge transport operations as barge owners, barge operators, shippers and waterway managers. It is based on this that the system boundary for each of the actors will be developed in the WP2 world model. The main important actors within the sector and their modes of operation that are taken into account are actors related to the shippers (cargo owners): shippers, freight forwarders and logistics services providers. Next to that also barge owners, inland and deep-sea terminal operators and waterway managers are taken into account. Finally, also the conditions at the end of the chain (distribution centres) are taken into account. The data needed for the model is identified. This data are cargo flow data for IWT, road and rail transport on the Rhine Alpine corridor. Next to that, also the sources for the cost data for all modes are determined. The modelling method that was chosen is a discrete event modelling approach. The simulation model requires a modular infrastructure in order to be able to incorporate the innovations from WP2, WP3 and WP4. In this deliverable also the KPIs are determined which should be computed by the NOVIMOVE transport model. These KPIs are determined at both the overall logistics level as well as the individual NOVIMOVE innovation level. The KPIs can be decomposed into different individual performance indicators and extended by further relevant performance indicators (e.g., additional transportation, handling, or financial indicators). This provides the various stakeholders with a holistic and relevant picture and enables them the performance monitoring of the innovations in their respective application context. Based on these developments the scope, data and functionalities and main outputs of the NOVIMOVE transport model can be developed.

We model panel data of crime careers of juveniles from a Dutch Judicial Juvenile Institution. The data are decomposed into a systematic and an individual-specific component, of which the systematic component reflects the general time-varying conditions including the criminological climate. Within a model-based analysis, we treat (1) shared effects of each group with the same systematic conditions, (2) strongly non-Gaussian features of the individual time series, (3) unobserved common systematic conditions, (4) changing recidivism probabilities in continuous time and (5) missing observations. We adopt a non-Gaussian multivariate state-space model that deals with all these issues simultaneously. The parameters of the model are estimated by Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare continuous time trends and standard discrete-time stochastic trend specifications. We find interesting common time variation in the recidivism behaviour of the juveniles during a period of 13 years, while taking account of significant heterogeneity determined by personality characteristics and initial crime records. © 2007 The Authors. Journal compilation 2007 VVS.