Individual Microscopic Results Of Bottleneck Experiments

This contribution provides microscopic experimental study of pedestrian motion in front of the bottleneck, explains the high variance of individual travel time by the statistical analysis of trajectories. The analysis shows that this heterogeneity increases with increasing occupancy. Some participants were able to reach lower travel time due more efficient path selection and more aggressive behavior within the crowd. Based on this observations, linear model predicting travel time with respect to the aggressiveness of pedestrian is proposed.Comment: Submitted to Traffic and Granullar Flow 2015, Springe

Simulating crowd evacuation with socio-cultural, cognitive, and emotional elements

In this research, the effects of culture, cognitions, and emotions on crisis management and prevention are analysed. An agent-based crowd evacuation simulation model was created, named IMPACT, to study the evacuation process from a transport hub. To extend previous research, various socio-cultural, cognitive, and emotional factors were modelled, including: language, gender, familiarity with the environment, emotional contagion, prosocial behaviour, falls, group decision making, and compliance. The IMPACT model was validated against data from an evacuation drill using the existing EXODUS evacuation model. Results show that on all measures, the IMPACT model is within or close to the prescribed boundaries, thereby establishing its validity. Structured simulations with the validated model revealed important findings, including: the effect of doors as bottlenecks, social contagion speeding up evacuation time, falling behaviour not affecting evacuation time significantly, and travelling in groups being more beneficial for evacuation time than travelling alone. This research has important practical applications for crowd management professionals, including transport hub operators, first responders, and risk assessors

Understanding long-term changes in commuter mode use of a pilot featuring free e-bike trials

Globally, the need for more sustainable modes of transport is rising. One of the main contenders of the car is the electrical bike (e-bike). To promote the use of e-bikes, pilots are being organised worldwide (e.g. in the USA, Norway, and the Netherlands). Studies have shown that providing a free e-bike to people for a limited period of time changes their mode choice behaviour during the pilot period. Only few studies have also investigated the long-term effects of these free e-bike trial periods, which show increase in e-bike use in general. However, these studies have failed to investigate why some participants of the trials change behaviour on the long-term, whereas others continued their former behaviour. This study aims to bridge this gap. A pilot with e-bikes was organised at Delft University of Technology, The Netherlands, with the goal of reducing car use for commuter trips towards the university. Data was collected at various moments during and after the trial period to evaluate the long-term changes in commuting behaviour and to identify potential reasons for these changes. A total of 82 participants are included in this study. Overall, car use for commuting decreased from 88% before the pilot to 63% three months after the pilot. E-bike use went up from 2% to 18% in the same time period. A binary logistic regression model shows that the most important variables to explain the decrease in car use are 1) purchase of an e-bike, 2) the participant's perception regarding e-bike safety, and 3) the aim of the participant to use the pilot to change their current behaviour. Besides that, the most important predictor of increase in e-bike use is the purchase of an e-bike. Furthermore, participants identify the investment costs of an e-bike as the strongest reason for not purchasing an e-bike and, thus, not changing their commuting behaviour. Future pilot programs could consider the potential of incrementally purchasing an e-bike over a longer period of time, instead of at once, to increase e-bike adoption rate.Transport and Plannin

Monitoring the Number of Pedestrians in an Area: The Applicability of Counting Systems for Density State Estimation

Crowd monitoring systems are more and more used to support crowd management organizations. Currently, counting systems are often used to provide quantitative insights into the pedestrian traffic state, since they are fairly easy to install and the accuracy is reasonably good under normal conditions. However, there are no sensor systems that are 100% accurate. Detection errors might have severe consequences for the density state estimation at large squares. The consequences of these errors for pedestrian state estimation have not yet been determined. This paper studies the impact of one specific type of detection error on the functionality of counting camera systems for density state estimation, namely, a randomly occurring “false negative” detection error. The impact is determined via two tracks, a theoretical track and a simulation track. The latter track studies the distribution of the cumulative number of pedestrians after 24 hours for three stylized cases by means of Monte Carlo simulations. This paper finds that counting camera systems, which have a detection error that is not correlated with the flow rate, provide a reasonably good estimation of the density within an area. At the same time, if the detection error is correlated with the flow rate, counting camera systems should only be used in the situation where symmetric demand patterns are expected

Evaluating a data-driven approach for choice set identification

The specification of the choice set for travel behaviour analysis is a non-trivial task, as its size and composition are known to influence the results of model estimation and prediction. Most studies specify the choice set using choice set generation algorithms. These methods can introduce two severe errors to the specified choice set: false negative (not generating observed routes) and false positive (including irrelevant alternatives) errors. Due to increased availability of revealed preference data, like GPS, it is possible to identify the choice set in different way: data-driven. The data-driven path identification approach (DDPI), introduced in this paper, combines all unique routes that are observed for one origin-destination pair into the choice set. This paper evaluates this DDPI approach, by comparing it to two choice set generation methods (breadth-first search on link elimination and labelling). The evaluation is based on three main purposes of choice sets: analysis of alternatives, model estimation and prediction. The conclusion is that the DDPI approach is a useful alternative for choice set identification. The findings indicate that in analysing alternatives, the DDPI approach is most suitable, as it is equal to the observed behaviour. For model estimation the DDPI approach provides a useful alternative to choice set generation methods, as it provides insights into the preferences of individuals. In terms of prediction, the DDPI approach is suitable on a network level, but not on the individual level. The average performance over all alternatives is similar for all choice sets, but on individual level the DDPI method does not predict well.Transport and PlanningTransport and Plannin

How do people cycle in Amsterdam? Estimating cyclists’ route choice determinants using GPS data from an urban area

Nowadays, the bicycle is seen as a sustainable and healthy substitute for the car in urban environments. The Netherlands is the leading country in terms of bicycle use, especially in urban environments. Yet route choice models featuring inner-city travel that include cyclists are lacking. This paper estimates a cyclists’ route choice model for the inner-city of Amsterdam, based on 3,045 trips collected with GPS data. The main contribution of this paper is the construction of the choice set using an empirical approach which uses only the observed trips in the dataset to compose the choice alternatives. The findings suggest that cyclists are insensitive to separate cycle paths in Amsterdam, which is a city characterized by a dense cycle path network in which cycling is the most prominent mode of travel. In addition, cyclists are found to minimize travel distance and the number of intersections per kilometer. The impact of distance on route choice increases in the morning peak where schedule constraints are more prevalent. Furthermore, overlapping routes are more likely to be chosen by cyclists given everything elsebeing the same.Transport and Plannin

Evaluating a data-driven approach for choice set identification using GPS bicycle route choice data from Amsterdam

Specifying the choice set for travel behaviour analysis is a non-trivial task. Its size and composition are known to influence the results of model estimation and prediction. Most studies specify the choice set using choice set generation algorithms. These methods can introduce two types of errors to the specified choice set: false negative (not generating observed routes) and false positive (including irrelevant routes). Due to increased availability of revealed preference data, like GPS, it is now possible to identify the choice set using a data-driven approach. The data-driven path identification approach (DDPI) combines all unique routes that are observed for one origin-destination pair into a choice set. This paper evaluates this DDPI approach by comparing it to two commonly used choice set generation methods (breadth-first search on link elimination and labelling). The evaluation considers the three main purposes of choice sets: analysis of alternatives in the choice set, model estimation and prediction. The conclusion is that the DDPI approach is a useful addition to the current choice set identification methods. The findings indicate that in analysing alternatives in the choice set, the DDPI approach is most suitable, as it reflects the observed behaviour. For model estimation the DDPI approach provides a useful addition to the current choice set generation methods, as it provides insights into the preferences of individuals without requiring network-data for additional information or generating routes. In terms of prediction, the DDPI approach is not suitable, as it is not able to perform well with out-of-sample data.Transport and PlanningTransport and Plannin

How do people cycle in Amsterdam, Netherlands?: Estimating cyclists' route choice determinants with GPS data from an urban area

Nowadays, the bicycle is seen as a sustainable and healthy substitute forthe car in urban environments. The Netherlands is the leading countryin bicycle use, especially in urban environments. Yet route choice modelsfeaturing inner-city travel that includes cyclists are lacking. This studyestimated a cyclists’ route choice model for the inner city of Amsterdam,Netherlands, on the basis of 3,045 trips collected with GPS data. The maincontribution of this study was the construction of the choice set with anempirical approach, which used only the observed trips in the data setto compose the choice alternatives. The findings suggested that cyclistswere insensitive to separate cycle paths in Amsterdam, a city characterizedby a dense cycle path network in which cycling was the most prominentmode of travel. In addition, cyclists were found to minimize traveldistance and the number of intersections per kilometer. The impact ofdistance on route choice increased during the morning peak when scheduleconstraints were more prevalent. Furthermore, overlapping routeswere more likely to be chosen by cyclists, everything else being the same.Transport and PlanningTransport and Plannin