During a natural disaster, when roads are damaged or blocked, rescue agents search the area to find new routes from start to destination. Their trajectories are sent to a crisis center and merged into a new map. The DeepSeek and ChatGPT algorithms help build this map by combining the agents' explored routes. This paper presents the algorithm and its application.

At many times we observed disturbances of traffic flow on highways. This may be caused by traffic accidents, bad weather conditions, road maintenance or rush hours. The Road Traffic Management takes many rules and regulation, to make road sections more robust to disturbances and improve the recovery from disturbances in traffic flow (traffic resilience). To study the effect and impact of these rules and regulations assessment models of traffic resilience was needed. In this paper, we designed and tested such an assessment model. The model is inspired by the well-known Resilience Triangle. The assessment of traffic resilience was based on measurements of the speed of traffic flow on highways. Neural Networks were used to model and smooth recorded speed data. The Traffic Resilience model has been tested on real life data.

For many years the Dutch NS Railway Company runs all trains in the Netherlands. It can be expected that next year different transport companies will exploit the Dutch railway network. Possible that they will use different systems to design timetables, generate travel plans and to handle delays. In this paper we research the coordination between different systems. A prototype of distributed systems in different areas has been developed using agent technology. The system has been tested in simulation studies.

In 2002 the city of Prague was affected by a flooding of the river Vltava, caused by heavy rainfall in the Southwest area of the Czech Republic. Successive parts of the city were inundated and inhabitants have to be evacuated from areas threatened by the raising water. After breaching the dikes and upcoming water via caves and sewers, streets were flooded. A special Crisis App was designed to route people to safe areas via safe routes. The routing system is visualized by fishbone maps, commonly used in car routing systems. To compute the shortest path from current location to a safe area a hierarchical version of Dijkstra shortest path algorithm has been used. Fleeing citizens and special observation agents report about changing situations. If crossings and streets are flooded, new routes have to be computed or existing routes have to be adapted by applying Dijkstra again. The application is Prague specific but can be generalized to similar smart cities in case of flooding disasters. More details will be presented in the paper including a simulation study showing the adaptive routing of the Crisis App.

The areas of many cities in the Netherlands are covered by a network of stationary sensors, measuring special components of air pollution such as CO2, NO2, PM2.5 and PM10. The pollution with fine dust along roads, surrounding and crossing the city is primarily related to traffic density. To measure traffic density, we used a license plate recognizer based on a special Neural Network Neocognitron, analyzing the video footage of surveillance cameras along the roads. We also studied the onset and offset of traffic density to predict traffic density, using the first recorded sparse traffic data. In cooperation with MIT Senseable City Lab the Technical University of Delft has developed special mobile, low cost sensors to measure air pollution. These mobile sensors are integrated with stationary sensors to a heterogeneous sensor network and enable measurement of air pollution out of the reach of the stationary sensor network..

Recently articles in Newspapers, University News Bulletins and Scientific Literature report about negative aspects of the wellbeing of students caused by COVID-19 epidemic. Half of the students have mental problems and don't participate in the teaching learning process anymore. In the Netherlands, Universities are surveyed by questionnaires, researching the mental health problems of students. In this paper we focus on students of Delft University of Technology. It proved from surveys, that many students complain about loneliness, fear, sleep deprivation and lack of study motivation. In this paper we report about experiments at the Faculty of Electrical Engineering, Mathematics and Computer Science, how students can be activated, motivated and socialized via study activities presented at the website of one of the Study Societies and via study-buddy groups. Students were personally invited to take part in discussions via a Forum, to enroll in group activities and to visit special lectures. A special COVID-19 didactics has been developed to stimulate students to make assignments in Calculus and Programing via Massive Open Online Courses developed in the framework EdX, an online learning destination and MOOC provider.

Social media play an important role in crisis events. People in crisis events can report their observations of the crisis environment, and their mental and physical state. They can ask for help or assistance via Twitter. Some studies report, that some or a lot of compassion, concern and empathy is involved. Verification of this thesis was the main concern of our research. We analyzed huge datasets of Tweets available via Internet of recorded messages posted during natural disaster events. We detected many emotional tweets characterized by keywords. Emotional keywords were extracted and composed in an emotional dictionary, to be used for future flooding disasters.

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation Sub- Challenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.

The Corona crisis limits the mobility of people and it proves that many people have an increased body mass index. A lot of people want to increase their fitness and to boost their immune system. There is a run-on fitness schools and fitness equipment for the home environment. At fitness schools, fitness coaches are available for personalized instructions and supervision. Fitness in the home environment lacks personalized instruction and supervision and can cause serious injuries. In this paper a digital coach will be introduced providing digital support and supervision. The digital coach offers personalized fitness program, supervises athletes during fitness exercises and provides a supporting system concerning injuries. A prototype of the developed system can be downloaded as an app on a smart phone.

In this paper we present an information system improving situational awareness, communication and management during a flooding crisis. The system is based on the agent framework (JADE) and a blackboard like functionality, which enables rescue workers and services to improve communication, increase context awareness and activate rescue services. Observers in the crisis field, modelled as an agent, report about their observations using an iconbased crisis App on a smartphone. A prototype has been implemented and tested in field experiments.

Current route planners on smart phones or special routing devices try to minimize the traveling time. But many users are no longer interested in the shortest route in time but they prefer a healthier route. In many smart cities there is a centralized distributed network of sensors measuring the pollution in a city on different locations for different time of the day. In this paper we present a routing App computing the less polluted route along the streets using the measurement of the eco sensors. The council of a smart city takes care of the health of their citizens. The developed routing App enables travelers with lung diseases to choose a healthy route. The routing algorithm is based on a special variant of the well-known Ant Based Control routing algorithm. To combine data measuring pollution and shortest traveling time, we solved a special multi-parameter problem. In some experiments we performed a user test of the routing App on different time and location.

The recovery of the economy, after the crisis at the beginning of this century, causes an enormous increase of traffic on the roads in the Netherlands in and around cities. After many years of discussion road pricing is considered as the most reasonable solution. Car drivers have to pay additional tax if they use the entrance roads of a city during peak times. A network of surveillance cameras along the roads have been installed and tested to register entering cars on the access roads. For many years sensors in the road surface are used to measure speed of cars on the roads to generate alarming messages for upcoming traffic jams and to provide alternative routes via panels along the road. Finally there is also a huge electrical network of traffic lights along the roads. By the enormous rise of mobile phones the interest in existing wired networks along the road is decreasing. In this paper we discuss the revival of wired networks and the integration with wireless networks.

A Smart City can be considered as an open living lab, where new technology will be developed, implemented and tested. The focus of smart cities in this paper is on the safety of citizens, sustainable energy, risk prevention of disasters, smart transport systems using information technology and Internet of Things. Smart cameras and smart sensor systems play a crucial role in smart cities. Citizens are surveyed by smart cameras, registrating their presence, their activities but also their needs and security. Big Brother is watching you, the Orwellian Nightmare come true. In this paper we focus on the smart city of Amsterdam. At the entrance road of the city smart cameras register all entering vehicles; their license place will be recognized and checked for access in available databases. Similar smart cameras check car drivers for their access to the inner city and green zones. But not only car drivers are surveyed. Entering boats on the many waterways of Amsterdam are surveyed. Every vessel is supposed to have a transponder on board with an Automatic Identification System (AIS), enabling control of anomaly of shipping on the water and detection of intruders. This paper is a survey paper of developed technology and research around smart cities.

Most routing devices use real time traffic information. It proved that the first hours of a new day can be used to find a good matching day in the past. The traffic data from the matching day can be used to predict traffic streams on the current day. We designed a historic database storing traffic information from real time available public domain databases. A dynamic version of the well-known Dijkstra shortest path algorithm was used to design a dynamic routing algorithm.