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)
Periodo di rendicontazione: 2020-05-01 al 2020-10-31
In urban areas, an increasing number of travellers are turning to more sustainable means of transport such as walking and cycling. The ALLEGRO project studies pedestrians and cyclists' behaviour in traffic, a field that offers many opportunities for ground-breaking knowledge.
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.
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 research performed in the ALLEGRO project focusses on the various levels of behaviour in slow mode - or rather active mode - transportation. We look at the split-second decision making that pedestrians and cyclist perform the walking or cycling, and the collective behaviour stemming from these decisions. We also look at the longer term decision making, including route choice decision, and activity scheduling, as well as wayfinding and mode choice decisions.
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.).
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.).
We have been able to establish many novel behavioural insights that allow us to better understand active mode traffic and transportation. This includes new empirically underpinned theory and mathematical models that allow us to understand and reproduce active mode traffic operations based on the principle of least effort, where effort is defined as a generalised concept including different sorts of costs incurrent by the pedestrian or cyclists (including mental effort, physical effort, etc.). The empirical underpinning is currently based on data collection from intersections in Amsterdam, but will be extended using the data from the experiments discussed earlier. This will further refine the theory and the model. Despite that refinements are needed, the current model already provides new insights into the determinants of walking and cycling (including interactions with the other traffic participants). In doing so, we have been able to reproduce collective (self-organised) patterns often observed in traffic flows.
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.
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.