Congestion is an important characteristic of transportation systems and a pervasive issue that affects millions of people in their daily lives. Typically, congestion is observed on highways and roads when cars are forced to stop due to high levels of traffic, preventing them from continuing their journey.

However, congestion is not limited to roadways; it is also prevalent in pedestrian crowds, with people experiencing it regularly when riding trains during rush hours in busy urban centers. This problem is particularly pronounced in rapidly developing Asian cities such as China, Indonesia, and India, but European and American cities are also grappling with congestion-related challenges.

Measuring congestion in pedestrian crowds is a complex task because "congestion" is not a property that can be directly measured like time, distance, or speed. In the context of my research, I have developed a method that assesses the "smoothness" of pedestrian flows, enabling us to determine congestion levels (or the so-called congestion number) within crowds of people.

This method has undergone testing in various environments, allowing us to identify lane formations in bidirectional streams, differentiate between homogeneous crowds (where all people walk at the same speed) and heterogeneous crowds (where people move at different speeds), and pinpoint the riskiest areas during evacuations. It was also tested in Shinjuku station, one of the busiest railway stations in the world.

Furthermore, this method also enables us to estimate the risk associated with how people move. This information can be invaluable during mass events or for monitoring crowds in transportation hubs, aiding security personnel in taking appropriate measures when dangerous levels of risk are reached.

Bidirectional flow is a common occurrence in pedestrian traffic, such as the way crowds of people move in corridors, crosswalks, or on sidewalks/walkways. Researchers in the field of pedestrian traffic have particularly focused on bidirectional flows due to the self-organization mechanism that emerges when people arrange themselves into lanes.

One of the most interesting and fascinating aspects of lane formation is that people can organize themselves without the need for a leader or prior knowledge of how they should behave in a group. Lanes simply emerge as a collective process in which each person tries to avoid collisions with the counter-flow (you can watch a video here that illustrates the lane formation process).

However, lanes do not always appear, and sometimes people coming from both directions get stuck, forming a dangerously dense crowd. This has been a contributing factor to tragedies in the past, such as the Love Parade in Duisburg in 2010.

Researchers, in particular, have been trying to determine whether bidirectional flows are more dangerous when the number of people from both directions is equal or when there is a significant difference between them.

In my research, I thoroughly investigated bidirectional flows and the lane formation process, connecting it with past literature on the subject. Our conclusion is that lane formation is more challenging when the number of people from both directions is equal because individuals need to consider both the crowd coming from the opposite direction and the people on their left and right sides. However, lanes are more stable when the groups from both directions are similar in number, leading to increased long-term efficiency. When there is a substantial difference in the number of people between both groups, lane formation is more challenging, but interactions are relatively limited, resulting in minimal or no long-term changes.

Assessing crowd conditions and replicating crowd movements using numerical models is undoubtedly an important task. However, ultimately, the goal is to influence and modify crowd behavior when deemed necessary.

In my research, I delved into how information provision can impact crowd behavior and which types of pedestrians are more likely to influence overall crowd performance. Both theoretical scenario studies employing numerical simulations and experiments involving real participants were used to examine this critical aspect of crowd management.

Additionally, I explored methods to enhance egress from crowded facilities by leveraging real-time information gathered through sensing devices, integrating them with simulators to optimize the overall egress process.

The results demonstrated that achieving optimal crowd control hinges on finding a balance between the quantity of information provided and the delivery methods used. Preparing steering strategies should take into account multiple scenarios. Furthermore, I found that targeting a specific category of traffic users can yield results closely resembling the impact of informing the entire crowd. In my research, providing information to people using wheelchairs was found to be beneficial for both them and the entire crowd. Similarly, other vulnerable users may benefit from targeted information, creating advantages for everyone in their vicinity.

The soldier crab, technically known as Mictyris guinotae, is a cute (not a technical definition here...) species of crab found in tropical regions, including Australia, South East Asia, and a few islands in the southern part of Japan. This particular type of crab is intriguing for several reasons. It moves in a straight line, unlike most crabs that have lateral movement. It forms large swarms consisting of thousands of individuals. Most importantly, unlike bees or other social animals, it exhibits straightforward social behaviors, primarily focused on eating, seeking shelter in times of danger, and reproducing during winter. This simplicity makes it an excellent subject for studying fundamental aspects of collective behavior.

In my research, I am attempting to investigate how light can influence the overall dynamics of these crab swarms, particularly whether light can be employed to "synchronize" the movements of soldier crabs. I designed a specialized device, aptly named the "crabs disco," featuring a circular track and a rotating light used to guide the crabs in a specific direction (clockwise or counterclockwise). To gain a better understanding of swarm dynamics and test various hypotheses related to their behavior, I also developed a Cellular Automata model that replicates swarm dynamics.

In the initial phase of the research, I am exploring how synchronization occurs by varying swarm density (3, 10, 30 crabs). In a second part, I am investigating whether an already synchronized swarm can be reversed by changing the direction of the light source. Results have revealed that under low densities, there is no discernible collective organization, rendering light virtually ineffective. At high densities, collective behavior naturally emerges even without external intervention. However, at medium densities, it is possible to reasonably control the swarm: crabs initially move in the same direction as the light and change their walking direction when the light's direction is reversed. This demonstrates the importance of maintaining a balance between "social" density (ability to engage in interactions) and "physical" density when studying animal swarms.

In today's world, it has become a common practice to employ crowd simulation models when designing pedestrian infrastructures and planning mass events that accommodate a large number of people. A considerable number of commercial software options are available for this purpose, and new ones continue to be introduced and developed every year.

However, many of the models used for pedestrian simulation are primarily designed to replicate everyday conditions and do not facilitate the consideration of very dense crowds. Consequently, investigating crowd accidents or replicating extreme conditions is not feasible using conventional models.

Typically, pedestrian densities in most public spaces remain below 1 person per square meter. Movement becomes challenging when densities exceed 2 persons per square meter, but in scenarios like packed trains during rush hours, densities in the range of 6 persons per square meter are commonly observed. Several researchers have reported that in cases of crowd accidents, densities exceeded 10 persons per square meter, with some even reaching up to 15 persons per square meter.

Standard simulation models can only replicate crowds with a maximum density of approximately 5-6 persons per square meter. To enable the simulation of dense crowds, I have developed a simulation model capable of handling densities well above 10 persons per square meter, with the maximum density being 17 persons per square meter. This expansion allows for the consideration of extreme scenarios. The model's validation has been conducted using empirical data, demonstrating good agreement across various conditions.

Pedestrian and vehicular traffic are typically segregated into areas accessible only to pedestrians and those accessible solely to cars. Pedestrians walk on the sidewalk, while cars move on the road. Unsignalized crosswalks represent one of the few points where cars and pedestrians interact. Unlike signalized crosswalks, where traffic lights coordinate the movements of both cars and pedestrians, preventing collisions, in unsignalized crosswalks, pedestrian safety relies on the successful negotiation of road crossing when individuals attempt to traverse.

Accidents can easily occur due to misunderstandings or distractions, with pedestrians typically being the victims. Therefore, it is essential to comprehend the decision-making process when people attempt to cross the road and identify the most relevant variables affecting traffic volumes for both road users.

In the course of this research, conducted in collaboration with the University of Milano-Bicocca, I utilized results from on-field observations to develop a simulation model that replicates the dynamics of pedestrians and drivers in unsignalized crosswalks.

The model has been validated using experimental data, demonstrating its capability to accurately estimate waiting times caused by the presence of the crosswalk for both pedestrians and drivers. Such a model can be applied, for instance, to determine whether installing a traffic light would reduce waiting times or if an unsignalized crosswalk is appropriate.

In a subsequent study, I also examined pedestrian safety to assess which policies are more effective in reducing the severity and number of collisions between pedestrians and vehicles. Our research indicated that while enforcing speed limits clearly helps reduce pedestrian fatalities, alternative "soft" solutions like implementing "shared spaces" can also be effective. Additionally, we emphasized the importance of conducting safety campaigns targeting drivers and raising pedestrians' awareness of potential risks.

In a collaborative research, I further investigated cognitive and environmental aspects related to unsignalized crosswalks, utilizing Virtual Reality and a self-developed driving simulator.

All crowds are composed of people, but the relationships between their members are important factors determining overall behavior. Individuals behave differently from couples, and couples behave differently from families. Understanding how groups of people behave in various situations and discerning the differences from individual behavior is essential for enhancing the models used in simulations.

In the context of my research, I collaborated with various institutions, including the University of Milano-Bicocca, Okayama University, ATR Kyoto, and Kyoto University, to specifically study the behavior of dyads, which are groups composed of two persons.

While my role in this area has mostly been peripheral, mainly involving assisting in the execution of experiments and providing advice on analytical methods, these diverse collaborations have enabled me to acquire knowledge and expertise in group behavior and the methods employed to analyze and simulate their behavior.

Typically, pedestrians are detected using cameras or specialized sensors (more recently, distance sensors are increasingly used), which, although relatively expensive, also raise concerns about violating people's privacy. Although solutions that do not rely on privacy-sensitive data are under development, pedestrian spaces are more user-friendly when they don't require a large number of cameras and sensors.

In today's world, the majority of people walking in public spaces carry a smartphone or a tablet. These types of devices come equipped with a plethora of sensors and maintain constant connectivity to a communication network. While GPS is sometimes employed to determine people's positions and estimate their speeds, this method also involves handling privacy-sensitive information. A more efficient and user-friendly solution involves leveraging the inertial sensors present in electronic devices to estimate people's movement speed by analyzing their body motion.

In the context of this research, I evaluated the feasibility of using inertial sensors from commercial electronic devices to estimate the speed and density of pedestrian crowds. Our research demonstrated that under controlled conditions, both speed and density can be estimated with sufficient accuracy. However, our findings also indicated that a large-scale application may necessitate several improvements and would require a relatively substantial number of active users to ensure reliable results.

The software platform developed to collect inertial data from multiple devices in real-time was also utilized by colleagues to measure body orientation using the gyroscope sensor.

In this research, conducted in collaboration with various partners, I am endeavoring to comprehend how population density can influence people's lives and what kind of connections exist between the "crude" measure of people per square kilometer and the actual perceptions of individuals.

On one side, I have been collaborating with Alice Pacher (an expert in sexuality) and Andrea Gorrini (an environmental psychologist) to explore whether population density, or more broadly, the living environment, can impact human sexuality and in what ways. We found that housing environment has minimal influence on our sexual lives, regardless of the cultural context. However, working environment played a significant role in Western countries, such as Germany and Italy. Specifically, individuals who found their work meaningful were more likely to have an active sexual life. In Japan, social life took on greater importance, with the so-called "third place" playing a notable role in sexuality.

On the other hand, I am collaborating with architects and urban planners, namely Karri Flinkman and Hideki Kozumi, to investigate whether population density, expressed as people per square kilometer, can effectively measure perceptual aspects related to everyday life. We are also exploring how individuals from diverse cultures perceive different environments, such as the countryside and metropolis, for example.

Prior to embarking on my research concerning pedestrian crowds, I was engaged in various research projects across different disciplines, primarily focusing on fluid dynamics.

As an undergraduate student, I delved into the generation of turbulence within a jet stream by conducting laboratory experiments. I investigated high-speed jet streams using Particle Image Velocimetry (PIV) to explore the feasibility of employing this technique to study turbulence.

As a graduate student in nuclear engineering, I designed a device for gas mixing that facilitated the measurement of radioactive Xenon (Xe) using gas mass microscopy. This device allowed for the controlled dilution of radioactive gas with a precisely known dilution ratio, subsequently enabling its analysis with highly sensitive specialized equipment.

During my tenure as an R&D engineer in the field of polymer science, I developed a computational method for determining the thermal conductivity of fiber-reinforced polymers through simulation. We submitted a patent related to this innovative method. Throughout my engineering experience, I had the opportunity to utilize various commercial software applications in fluid dynamics, including the simulation of phenomena like falling ice using Flow3D (view a movie here).

Throughout my educational and research endeavors, I also had the privilege of working with several codes related to fluid dynamics, spanning various disciplines, including reactive polymers and tsunami propagation (view a movie here).

While employed at a small company in Switzerland (MAIN Gmbh), I additionally created software to simulate the dynamics of a twin-roll casting plant and estimate Return on Investment (ROI) based on various factors, including electricity and gas costs and invested capital (see screenshot).