For the new version of the Dutch design guidelines for buildings, a threshold value for the capacity of emergency doors needs to be set. Innovative large-scale laboratory experiments have been performed to investigate the capacity of emergency doors during evacuation conditions. This paper focuses in particular on the relation between capacity and the independent variables doorway width, population composition, stress level and presence of an open door. It turned out that only the experiment with the widest doorway (275 cm) resulted in a capacity lower than the capacity from the current design guidelines (2.25 P/m/s). The average observed capacities are for all widths lowest for the lowest stress level and highest for the highest stress level. The population with a greater part of children has the highest capacity (on average 3.31 P/m/s). This is mainly due to the smaller physical size of children compared to adults and elderly, which makes it possible that more children can pass a door at the same time than adults. The lowest capacity (on average 2.02 P/m/s) is found for the experiment with 5% disabled participants. The presence of a door opened in the escape direction in an angle of 90° for a doorway of 85 cm results in a 20% capacity reduction compared to the reference experiment. The open door does not physically narrow the doorway, but it leads to interactions between participants reducing their speed and the corresponding outflow.

Incidents and waiting for train connections are registered by dispatchers as sources of train delays, but route and headway conflicts are not always clearly recognized. Moreover, traffic management and route setting are the primary task of dispatchers and signallers, whilst monitoring and incident registration is not allowed to take up too much of their time. This paper describes a tool that automatically and without discrimination identifies route conflicts and the train numbers involved. It is based on standard train describer and infrastructure messages recorded on the Dutch railway network. The logic of these messages is captured in a coloured Petri net (CPN) model on which a prototype tool for route conflict identification and estimation of knock-on delay has been developed.

Ports represent a key element in the maritime transportation chain. Larger vessels and higher traffic volumes in ports might result in higher risks at the navigational level. Thus, the dire need for a comprehensive and efficient risk assessment method for ports is felt. Many methodologies have been proposed so far, but their application to aggregated vessel traffic risks for the overall assessment of ports is not developed yet. Hence, the development of an approach for the appraisal of the vessel traffic risks is still a challenging issue. This research aims to develop an assessment methodology to appraise the potential risk of accident occurrence in port areas at an aggregated level by creating a ‘Nautical Port Risk Index’ (NPRI). After identifying the main nautical risks in ports, the Analytic Network Process (ANP) is used to derive the risk perception (RP) weights for each criterion from data collected through surveys to expert navigators. The consequences related to each nautical risk are identified in consultation with risk experts. By combining the RP values and the consequence of each criterion for a time period, the NPRI is calculated. The risks in the Port of Rotterdam are presented in a case study, and the method has been validated by checking the results with experts in assessing nautical port risks from the Port of Rotterdam Authority. This method can be used to assess any new port design, the performance of different vessel traffic management measures, changes in the fleet composition, or existent ports using the Automatic Identification System (AIS) data.

Nowadays, there is a need for tools to support city planners in assessing the performance of cycling infrastructure and managing bicycles and mixed flows. Microscopic and macroscopic bicycle traffic models can be used to fulfill this need. However, fundamental knowledge on individual cyclist interaction behavior (which should underpin these models) is hardly available in literature. Detailed bicycle traffic data are necessary if we want to gain insight into cyclist interaction behavior and develop sound behavioral theories and models. Laboratory experiments have been proven to be one of the most effective ways to collect detailed traffic data. For this reason, a controlled experiment aimed to investigate cyclist interaction behavior has been carried out at Delft University of Technology. This paper describes the experimental design, the resulting microscopic bicycle trajectories, and some preliminary results regarding one of the most common interaction situations: the bidirectional interaction. The preliminary results reveal how and to what extent cyclists interact in bidirectional cycling. It is found that cyclists perform a clearly-visible evading (collision avoidance) maneuver when they have face-to-face encounters. During these maneuvers, changes in speed and displacements in the lateral direction are observed. Cyclists start to deviate from their original path when they are around 30 m from each other, and they strongly prefer passing on the right-hand side. Moreover, the expectation of gender differences in cycling behavior reported in the literature is confirmed: our results show that women generally cycle more slowly than men and deviate more from their intended paths in face-to-face encounters. More observations will be available in the next stage of data analysis. These findings can be used to formulate improved microscopic bicycle traffic models for infrastructure design and policy development.

This paper presents an integrated modelling framework to capture pedestrian walking behaviour in congested and uncongested conditions. The framework is built using a combination of concepts from the Social Force model (basic one-to-one interaction), behavioural heuristics (physiological and cognitive constraints), and materials science (multi-body potential concept). The approach is ultimately designed to capture pedestrian interactions in transit stations. Due to the lack of available trajectory data of pedestrians within transit stations, the model is calibrated using pedestrian trajectory data from narrow bottleneck and bidirectional flow experiments provided by the Delft University of Technology. These two scenarios were chosen due to the frequency with which they occur in transit stations. The integrated modelling framework reproduced similar trajectory patterns observed in the experiments which encouraged a transit station simulation in an environment similar to that at the Foggy-Bottom METRO station in Washington, D.C.