Negative effects of a massive use of cars, such as congestion, air pollution, noise, and traffic injuries, are affecting the cities everywhere. Recently introduced shared vehicles, such as e-scooters and electric bicycles, could potentially accelerate the transition towards sustainable mobility. Although these vehicles are becoming increasingly common and accepted within regulatory frameworks, some local governments are not yet ready to integrate e-scooters into their transport systems. Indeed, the legislation is unclear as it is not easy to determine whether the e-scooter is more like a bicycle or a vehicle. Moreover, it is difficult to predict the impact of e-scooters on road traffic, as well as the type of road infrastructure chosen by e-scooter drivers or the possible interaction of such vehicles with weak road users, such as pedestrians or cyclists. This study showed an analysis of speed and behaviour of e-scooter drivers in the city of Trondheim (Norway) to investigate how to manage this mode of transport. A total of 204 e-scooters were observed on six different roads in the city centre. The speed of e-scooter drivers was measured by a speed tracker (average value 15.4 km/h) and their behaviour recorded by a hidden observer in the field. Gender, age, distance from pedestrians, speed adaptation to the environment, and type of vehicle used were registered for each e-scooter. Through a Binomial Logit analysis, the data obtained were used to analyse the type of road infrastructure preferred by e-scooter drivers. Results showed that the cycle path is more widely used with percentage value from 60% to 90% of users. In addition, the probability of choice depended mainly on the road environment. The aim of this analysis was to assist local authorities in regulating the safe use of e-scooters and developing appropriate policies for their integration into cities.

Human impression plays a crucial role in effectively designing infrastructures that support active mobility such as walking and cycling. By involving users early in the design process, valuable insights can be gathered before physical environments are constructed. This proactive approach enhances the attractiveness and safety of designed spaces for users. This study conducts an experiment comparing real street observations with immersive virtual reality (VR) visits to evaluate user perceptions and assess the quality of public spaces. For this experiment, a high-resolution 3D city model of a large-scale neighborhood was created, utilizing Building Information Modeling (BIM) and Geographic Information System (GIS) data. The model incorporated dynamic elements representing various urban environments: a public area with a tramway station, a commercial street with a road, and a residential playground with green spaces. Participants were presented with identical views of existing urban scenes, both in reality and through reconstructed 3D scenes using a Head-Mounted Display (HMD). They were asked questions related to the quality of the streetscape, its walkability, and cyclability. From the questionnaire, algorithms for assessing public spaces were computed, namely Sustainable Mobility Indicators (SUMI) and Pedestrian Level of Service (PLOS). The study quantifies the relevance of these indicators in a VR setup and correlates them with critical factors influencing the experience of using and spending time on a street. This research contributes to understanding the suitability of these algorithms in a VR environment for predicting the quality of future spaces before occupancy.

This study analyses five months of continuous monitoring of different lighting warning systems at a pedestrian crosswalk through video surveillance cameras during nighttime. Three different light signalling systems were installed near a pedestrian crossing to improve the visibility and safety of vulnerable road users: in-curb LED strips, orange flashing beacons, and asymmetric enhanced LED lighting. Seven different lighting configurations of the three systems were studied and compared with standard street lighting. The speed of vehicles for each pedestrian–driver interaction was also evaluated. This was then compared to the speed that vehicles should maintain in order to stop in time and allow pedestrians to cross the road safely. In all of the conditions studied, speeds were lower than those maintained in the five-month presence of standard street lighting (42.96 km/h). The results show that in conditions with dedicated flashing LED lighting, in-curb LED strips, and orange flashing beacons, most drivers (72%) drove at a speed that allowed the vehicle to stop safely compared to standard street lighting (10%). In addition, with this lighting configuration, the majority of vehicles (85%) stopped at pedestrian crossings, while in standard street lighting conditions only 26% of the users stopped to give way to pedestrians.

In the last decades, tourism in urban areas has been constantly increasing. The need for short-term accommodations has been coupled with the emergence of internet-based services, which makes it easier to match demand (i.e., tourists) and supply (i.e., housing). As a new mass tourist destination, Bologna, Italy, has been experiencing tensions between tourists and long-, mid-, or short-term renters. The possibility of easy profits for lessees has led to an increase in such housing, which can be rented out either for touristic reasons or not. This paper aims to unveil the contribution of short-term rental accommodations in distorting the real estate market and conditioning social and economic inequalities. To do this, multiple linear regression analyses (MLR) were performed between accommodation density, real estate market information, and indicators about social, economic, and demographic vulnerability and fragility. Analyses were based on official open data and datasets from a major web-based hospitality exchange platform, i.e., Airbnb, able to provide information on registered accommodations, e.g., type, characteristics (e.g., number of bedrooms and average rating), and location. Outputs of the analyses reveal the role of Airbnb in both rental market and social, economic, and demographic vulnerability and fragility and, hence, can be a solid tool for public policies, both housing- and tourism-related.

Cycling represents a significant share of urban transportation, especially in terms of last-mile delivery. It has clear benefits for delivery times, as well as for environmental issues related to freight distribution. Furthermore, the increasing accessibility of low-cost environmental sensors (LCSs) provides an opportunity for urban monitoring in any situation. Moving in this direction, this research aims to investigate the use of LCSs to monitor particulate PM2.5 and PM10 levels and map them over delivery ride paths. The calibration process took 49 days of measurements into account, spanning different seasonal conditions (from May 2024 to November 2024). The employment of multiple linear regression and robust regression revealed a strong correlation between pollutant levels from two sources and other factors such as temperature and humidity. Subsequently, a one-month trial was carried out in the city of Faenza (Italy). In this study, a commercially available LCS was mounted on a cargo bike for measurement during delivery processes. This approach was adopted to reveal biker exposure to air pollutants. In this way, the operator’s route would be designed to select the best route in terms of delivery timing and polluting factors in the future. Furthermore, the integration of environmental monitoring to map urban environments has the potential to enhance the accuracy of local pollution mapping, thereby supporting municipal efforts to inform citizens and develop targeted air quality strategies.

Although the use of bicycles is increasingly promoted and widespread across Europe, cyclist fatalities have remained stable since 2010, likely due to insufficient safe of the infrastructure. The study investigates bicycle performances and cyclist’s safety in urban context to assess which factors influence the perception of the users. Employing an instrumented bike with advanced sensors and eye-tracking device, the effects of environmental and infrastructural factors were assessed. Conducted in Florence, Italy, the research collected data from 20 participants on a 1.2 km route divided into sections with varying separation types, including curbs, vegetation, barriers, and parking zones. The safety assessment was realized comparing speed with participants’ fixation patterns, declared safety, infrastructural elements and vehicular flows across cycle sections. The accelerometer data quantified pavement-induced vibrations, categorizing comfort levels per ISO standards, and highlighting bicycle performance in riding comfort. Statistical analysis, including ANOVA and correlation assessments, identified that duration of fixations in the front area (r = 0.469, p < 0.001) and declared safety perception (r = − 0,449, p < 0.001) most influence the speed in the analyzed cycle section. These findings lead to reveal information on the impacts of cycle infrastructure on safety perception and users behavior. This research underscores the necessity for human-centered design in cycling infrastructure to enhance urban cyclists’ safety and comfort.