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Building fires can be considered a risk to the health and safety of occupants. Environmental factors in building fires might affect the speed of an evacuation. Therefore, in this study participants (N = 153) were tested in an experimental design for the effects of (1) a fire alarm, (2) darkness and (3) the use of emergency exit signs on building evacuation time. In addition, the effects of age and gender on evacuation time were investigated. The main results indicate that the combination of a fire alarm, darkness and not illuminated emergency exit signs had a significant negative influence on evacuation time, namely an increase in evacuation time of 26.6% respectively 28.1%. Another important finding is that age had a significant negative effect on evacuation time. The increase in evacuation time was at least 30.4% for participants aged 56 years or older compared to participants aged 18–25 years. For gender no significant effect was found. Building and safety managers can use these results by including longer evacuation time considerations – based on darkness and older age – in their evacuation plans. Future research should focus further on investigating the effects of personal and psychological characteristics on evacuation behaviour and evacuation time.

Abstract First/last mile transport is essential for transit but is often found to be the weakest link in a trip. Moreover, as a result of multiple developments (e.g. demographic shifts, urbanization, climate change, technology advancement) first/last mile transport will likely change rapidly. The literature review of this study shows six different categories of factors affecting first/last mile mode choice: (1) traveller, (2) psychological, (3) first/last mile trip, (4) first/last mile modes, (5) built environment, and (6) main stage. We used this framework to understand and predict the complex process of mode choice, specifically given the emerge of new modes. The performed mode choice experiment shows varying results regarding the preferences of travellers for existing and new means of first/last mile transport. Four future scenarios (varying in level of sharing and flexibility of rides) are investigated. Traditional means of transport such as private vehicles and traditional ride services remain preferred over shared vehicles and on-demand ride services. For instance, 21% of the travellers chooses a private but no shared vehicle, and 12% chooses a traditional but no on-demand ride service. On the other hand, 21% of the travellers prefer a shared vehicle and 23% prefer an on-demand ride service whenever these vehicles/services are available. These results illustrate that when mode choice factors are absent and there are no restrictions taken into account (for example the possession of a car and driving license when choosing car), the actual chosen means of transport in the current situation differs from the preferred means of transport in the future. The results also show potential for new, emerging, means of first/last mile transport. According to the ‘preferred situation’ by travellers, transit nodes and first/last mile systems require a different design regarding first/last mile facilities, dependent on the scenario(s) that will develop. The challenge for decision makers and planners is to steer mode choice decisions in the direction according to their policy objectives, where our insights support the corresponding design choices and policy interventions.

Commercial areas, especially urban ones with numerous buildings, are becoming increasingly prone to congestion because of their popularity. Visual inspections show that interactions between pedestrians and building entrances affect the distribution of pedestrian trajectories, which influences the utility of pedestrian spaces and the design of urban shopping areas. Herein, we analyse the dynamics of pedestrian deviations around building entrances. We used a video recorded using an unmanned aerial vehicle to determine pedestrian trajectories in a Chinese commercial walking space. First, the candidate variables affecting deviation behaviours were determined via correlation testing. Second, two regression models were developed by considering the deviation behaviours of pedestrians walking past a building entrance. The models suggest that the starting position of a pedestrian’s deviation, the total pedestrian flow at the building entrance, the density in an area in the vicinity of the entrance, and the number of interacting pedestrians impact the total distance traversed during path deviation.

Ports play an increasingly important role in the freight transportation chain due to containerization. High vessel flows and higher densities increase the relevance of the non-terminal related processes. Several simulation models have been developed in the recent decades with different goals, but their abilities to represent realistic vessel traffic in ports differ. In this paper, we identify the main navigational processes and operations related to the port nautical infrastructure, and review and assess the current port simulation models. This survey represents an exhaustive review of the state-of-the-art of simulation models for port assessment purposes focussing on safety and capacity. The model assessment focuses on the identification of the relevant criteria to represent vessel navigation, based on which processes are covered by each model and how they have been considered in each model. The assessment covers the nautical infrastructure representation and the navigational behaviour. The outcome of this review will be used for the development of a simulation based port assessment methodology. Future port simulation models should include the suitable criteria for a more realistic traffic representation that allows a proper safety and capacity port analysis and assessment. Keywords: Simulation model, Vessel traffic, Port simulation, Nautical infrastructure, Capacity assessment, Risk assessment

The capacity drop forms a major reason why the prevention of congestion is targeted by traffic management, as lower capacities are detrimental to traffic throughput. Various reasons describing the dynamics behind the capacity have been described, however one of these, reaction times, has had less explicit attention when modelling on a macroscopic flow level. In this contribution, a method to include the effect of reaction times for the capacity drop in heterogeneous traffic is proposed. The applied method further overcomes difficulties in including reaction times in a discrete time model through relaxation of the updating process in the discretization. This approach is novel for application in the considered first order approach, which is practise ready, contrary to many other models that propose similar approaches. The combination of the introduced method and the model form a solid development and method to apply the capacity drop based on this causation of the capacity drop. The results of the experiment case showed that the influence of traffic heterogeneity had a limited effect on the severity of the capacity drop, while it did influence the time of congestion onset. The influence of the reaction time on traffic showed greater capacity drop values for greater reaction time settings. The findings showed the method effective and valid, while the model application is also practise ready. Keywords: Capacity drop, Traffic modelling, Heterogeneous traffic, Traffic flow

Ideally, a multitude of steps has to be taken before a commercial implementation of a pedestrian model is used in practice. Calibration, the main goal of which is to increase the accuracy of the predictions by determining the set of values for the model parameters that allows for the best replication of reality, has an important role in this process. Yet, up to recently, calibration has received relatively little attention within the field of pedestrian modelling. Most studies focus only on one specific movement base case and/or use a single metric. It is questionable how generally applicable a pedestrian simulation model is that has been calibrated using a limited set of movement base cases and one metric. The objective of this research is twofold, namely, to (1) determine the effect of the choice of movement base cases, metrics, and density levels on the calibration results and (2) to develop a multiple-objective calibration approach to determine the aforementioned effects. In this paper a multiple-objective calibration scheme is presented for pedestrian simulation models, in which multiple normalized metrics (i.e., flow, spatial distribution, effort, and travel time) are combined by means of weighted sum method that accounts for the stochastic nature of the model. Based on the analysis of the calibration results, it can be concluded that (1) it is necessary to use multiple movement base cases when calibrating a model to capture all relevant behaviours, (2) the level of density influences the calibration results, and (3) the choice of metric or combinations of metrics influence the results severely.

Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

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.

Microscopic and macroscopic dynamic traffic models not fast enough to run in an optimization loop to coordinate traffic measures over areas of twice a trip length (50x50 km). Moreover, in strategic planning there are models with a spatial high level of detail, but lacking the features of traffic dynamics. This paper introduces the Network Transmission Model (NTM), a model based on areas, exploiting the Macroscopic or Network Fundamental Diagram (NFD). For the first time, a full operational model is proposed which can be implemented in a network divided into multiple subnetworks, and the physical properties of spillback of traffic jams for subnetwork to subnetwork is ensured. The proposed model calculates the traffic flow between to cell as the minimum of the demand in the origin cell and the supply in the destination cell. The demand first increasing and then decreasing as function of the accumulation in the cell; the supply is first constant and then decreasing as function of the accumulation. Moreover, demand over the boundaries of two cells is restricted by a capacity. This system ensures that traffic characteristics move forward in free flow, congestion moves backward and the NFD is conserved. Adding the capacity gives qualitatively reasonable effects of inhomogeneity. The model applied on a test case with multiple destinations, and re-routing and perimeter control are tested as control measures.

Several researchers have proposed that drivers do not just respond to the vehicle directly ahead, but also to the second, and even the third, fourth or fifth vehicle ahead. Little empirical evidence for this hypothesis has however been presented so far. We provide empirical evidence showing that drivers are not only reacting on the vehicle directly ahead, but also the ‘second leader’. This is achieved by analyzing vehicle trajectory data collected by observing a motorway traffic flow from a helicopter. These microscopic data enable estimation of individual car-following models. The extent to which this multi-anticipatory behaviour occurs turns out to be considerable: on average, the sensitivity with respect to stimuli coming from the second vehicle is half the sensitivity of the first vehicle ahead. For some vehicle triples, even higher sensitivities to the behaviour of the second leader than to the behaviour of the first have been observed. The estimation results show large differences in car-following behaviour between the different drivers. These differences can in part be explained by the vehicle-type composition of the considered vehicle triples. Trucks drivers show different behaviour than person-car drivers; drivers following a truck show dissimilar car-following behaviour than drivers following a personcar. Although not being a benchmarking study aimed at providing the best model of car-following behaviour, the research presented in this article shows that including multiple leaders can improve modelling of driving behaviour considerably.

To support operators in Regional Traffic Management Centers in their task to efficiently and safely manage traffic flows on the motorway and urban networks, a decision support system is being developed. An essential function of this system is its ability to predict the effects of a large number of candidate control scenarios, given the recurrent and non-recurrent conditions in the network. This article proposes such a prediction system referred to as BSES (Boss Scenario Evaluation System), which can evaluate control scenarios in real time, predicting their effects in terms of various measures of effectiveness, such as total travel times, vehicle loss times, average speeds, fuel consumption, etc. The main characteristics of the system are l) that it is case- based, i.e. it uses either synthetic or real-life examples of the effect of control scenarios under different circumstances; 2) that is determines the similarity of the current situation to different examples in the case-base using fuzzy logic, and 3) that it is agent-based, meaning that it predicts the effects of the different measures for small subnetworks and combines these predictions afterwards. In the article, synthetic data is used to set-up the case base. The test results described in the article illustrate the workings of the system, and shows that the system can provide the operator with real-time predictions. Furthermore, the predictions of the system are in comparable to the predictions from the simulation model used to fill the case-base, showing that the method is applicable to generalize the - in this case synthetic - data it uses.

To assess the design of walking infrastructure such as transfer stations, shopping malls, sport stadiums, etc., as well as to support planning of timetables for public transit, tools to aid the designer are needed. To this end, microscopic and macroscopic pedestrian flow models can and have been applied. To calibrate and validate such models, as well as to gain more insight into the characteristics of pedestrian flows under a variety of circumstances, very detailed pedestrian flow data are required. This is why Delft University of Technology has recently carried out experimental pedestrian flow research. This paper describes the experimental design (determination of process variables, measurement set-up, etc.), the resulting microscopic pedestrian data, as well as some first results for the narrow bottleneck experiment. Both microscopic and macroscopic characteristics of pedestrian flows are presented. Interesting first results pertain to the way in which the narrow bottleneck is used under saturated flow conditions, and the use of the space (or rather, width) upstream of the bottleneck in case of congestion.