Periodic Reporting for period 4 - ALLEGRO (unrAvelLing sLow modE travelinG and tRaffic: with innOvative data to a new transportation and traffic theory for pedestrians and bicycles)
Période du rapport: 2020-05-01 au 2020-10-31
In comparison with motorised vehicular modes of transport, walking and cycling have received little attention from researchers so far. Prof. Serge Hoogendoorn is focusing precisely on these so-called slow modes of transport as the lack of understanding of their dynamics is becoming problematic in urban areas that experience more and more difficulties in dealing with large numbers of pedestrians, especially during events attracting large crowds. While overcrowding of cycling facilities is only an issue for several cities, the acquired knowledge will also be useful to make cities more attractive for bicycles.
The behaviours of pedestrians and cyclists, as well as their interactions with each other and with other modes of transportation, are much more complex and hard to predict than those of drivers, due to the many degrees of freedom in their decision-making process. In fact, there are large behavioural differences between pedestrians and cyclists, on the one side, and motorists, on the other, as the former are less bound by traffic regulations.
Prof. Hoogendoorn studies walking and cycling flows in order to establish a comprehensive theory of slow mode traffic and travel behaviour. His team uses innovative big data collection techniques for instance applied to the city of Amsterdam, including remote and crowd sensing, social media analytics, virtual and augmented reality. By combining data from these different sources, they are developing conceptual and mathematical models to explain and predict the dynamics of pedestrians and cyclists within an urban context. These models can be applied to a variety of circumstances and can facilitate new approaches in the management of crowds, design of slow traffic infrastructure, etc., with the aim of improving safety, comfort and efficiency.
The work in general started with extensive data collection efforts. We have used multiple method to collect our research data, both in the field and in more experimental settings. Examples are our large scale controlled experiments to study crowd dynamics and cyclist behaviour, our long-term panel surveys, and our GPS data collection efforts.
These data do not only have a research purpose: our active mode monitoring dashboard allows analysis and visualisation of different types of data to real-time and off-line purposes. In the first half of the ALLEGRO project, we have worked on this platform (sensors network design, data collection, data fusion and state estimation techniques, visualisation, machine learning methods for social data analysis, etc.).
Also, we have shown which are the determinants of route choice of cyclists (distance, number of intersections, percentage route having a separate cycle path, route overlap) as well as mode choice (why do people choose to cycle or walk) and activity choices. Using new clustering methods, we have been able to identify specific classes of travellers that are likely to use active modes (or not), and investigate the characteristics of these classes. All these results are very relevant for understanding the impacts of policy interventions.
Typical applications involve the monitoring of crowds during events, for which the dashboard has already been deployed multiple times to support crowd management for these events. Next to using all time of traditional sensors, we also have been integrating social media data both as a proxy for crowdedness, and to analyse the sentiment during the event.
The remainder of the project will focus on furthering our insights, as well as looking into the (engineering) implications of our findings. To this end, we will collect data using novel data collection techniques (VR, AR) for special conditions (e.g. evacuations); we will work on our simulation platform; and we will investigate the implications or our findings for control, planning and design.