Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic.

Background: The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods: We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results: Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(ß) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (ß = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion: Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

Air quality improvements from a transport modal change in the São Paulo megacity.

Air quality and urban mobility are complex socioenvironmental issues faced by megacities, particularly those in the Global South. Investments in efficient and inclusive public transport networks, as well as encouraging active transport modals, can mitigate both traffic congestion and air pollution, leading to improved quality of life. Few studies have integrated the potential effects of a well-developed rail network in influencing modal choice - from motorised to active transport - and their impacts on pollutant emission and air quality through dynamic air quality models. In the megacity of São Paulo, the expansion of the subway system has not achieved the planned targets, as intended by state government plans. This induces the question "What would have happened to air pollution if planned stations had been properly implemented?" which this paper aims to answer, through the increased adoption of cycling or walking to and from railway stations. We applied the WRF-Chem air quality model to model the effects of railway development in a megacity, focusing on criteria pollutants. Results show that the more investments there are in public transport, by expanding the metro system, the more expressive are the decreases in air pollution, and the farther these benefits spread out spatially. More intense reductions in pollutant concentrations are predicted to occur near the new stations' buffers, but citizens living far from the metro stations will also benefit from these improvements. Additionally, modal change from car to walking/cycling and subway in all trips within a radius of 1.3 km centered in existing and planned stations is expected to result in a decrease of 11.7 % in CO2eq vehicular emissions. Therefore, improvements in public transportation could help improving environmental and social justice, leading to better air quality and transport accessibility, decreasing public health problems and costs, while helping cities reach their GEE emissions reduction targets.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

The untapped health and climate potential of cycling in France: a national assessment from individual travel data.

Background: Promoting active modes of transportation such as cycling may generate important public health, economic, and climate mitigation benefits. We aim to assess the mortality and morbidity impacts of cycling in a country with relatively low levels of cycling, France, along with associated monetary benefits. We further assess the potential additional benefits of shifting a portion of short trips from cars to bikes, including projected greenhouse gas emissions savings. Methods: Using individual data from a nationally representative mobility survey, we described the French 2019 cycling levels by age and sex. We conducted a burden of disease analysis to assess the incidence of five chronic diseases (breast cancer, colon cancer, cardiovascular diseases, dementia, and type-2 diabetes) and the number of deaths prevented by cycling, based on national incidence and mortality data and dose-response relationships from meta-analyses. We assessed the corresponding direct medical cost savings and the intangible costs prevented based on the value of a statistical life year. Lastly, based on individual simulations, we assessed the likely additional benefits of shifting 25% of short (<5 km) car trips to cycling. Findings: The French adult (20-89 years) population was estimated to cycle on average 1 min 17 sec pers-1 day-1 in 2019, with important heterogeneity across sex and age. This yielded benefits of 1,919 (uncertainty interval, UI: 1,101-2,736) premature deaths and 5,963 (UI: 3,178-8,749) chronic disease cases prevented, with males reaping nearly 75% of these benefits. Direct medical costs prevented were estimated at €191 million (UI: 98-285) annually, while the corresponding intangible costs were nearly 25 times higher (€4.8 billion, UI: 3.0-6.5). We estimated that on average, €1.02 (UI: 0.59-1.62) of intangible costs were prevented for every km cycled. Shifting 25% of short car trips to cycling would yield approximatively a 2-fold increase in deaths prevented, while also generating important CO2 emissions reductions (0.257 MtCO2e, UI: 0.231-0.288). Interpretation: In a country with a low- to moderate-cycling culture, cycling already generates important public health and health-related economic benefits. Further development of active transportation would increase these benefits while also contributing to climate change mitigation targets. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data.

BACKGROUND: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. METHODS: This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities. RESULTS: The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 µg/m3 and 55.62 µg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road". CONCLUSION: These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Ciclismo recreacional e mobilidade urbana: análise das motivações dos ciclistas de Blumenau/SC / Urban mobility: an analysis of the motivations of cyclists in Blumenau/SC

A motivação é um fenômeno complexo que impulsiona as ações humanas. O diagnóstico de tais interesses oferece elementos de reflexão para a promoção de atividades e a implementação de políticas públicas. Neste contexto, o presente trabalho tem o objetivo de analisar as motivações dos praticantes de ciclismo recreacional de Blumenau/SC, município com uma população de 363 mil habitantes e uma malha cicloviária de 137 quilômetros. A coleta dos dados empíricos da pesquisa ocorreu por meio da aplicação de um questionário. Ao final, obteve-se uma amostra de 304 respondentes. Os resultados do estudo indicam a motivação para as atividades físicas ocupa papel central na relação com outros fatores de interesse, contribuindo para a superação de aspectos eventualmente desencorajadores. Em contrapartida, praticantes que têm outras motivações, como a interação social e o desenvolvimento pessoal, dependem mais de estímulos encorajadores, o que sugere que em tais casos existe a necessidade de maior incentivo para a prática do ciclismo recreacional. As conclusões deste estudo são relevantes para planejadores urbanos e gestores de comunicação. Ao mensurar a pertinência de causas motivadoras e desmotivadoras do ciclismo, os resultados da pesquisa indicam a importância de investir em infraestrutura adequada como a expansão da rede cicloviária e a criação de campanhas de divulgação, a fim de promover a bicicleta como meio de transporte e/ou recreação.

Motivation is a complex phenomenon that drives human actions. Diagnosing these interests provides elements for reflection in promoting activities and implementing public policies. In this context, the present study aims to analyze the motivations of recreational cyclists in Blumenau, SC (Brazil), a city with a population of 363,000 inhabitants and a cycling network of 137 kilometers. Empirical data for the research were collected through a questionnaire. Ultimately, a sample of 304 respondents was obtained. The study's results indicate that motivation for physical activities plays a central role in the relationship with other factors of interest, contributing to overcoming potentially discouraging aspects. Conversely, individuals motivated by other factors, such as social interaction and personal development, rely more on encouraging stimuli, suggesting that in such cases there's a need for greater incentives for recreational cycling. The conclusions of this study are relevant for urban planners and communication managers. By assessing the relevance of motivating and demotivating factors in cycling, the research results underscore the importance of investing in appropriate infrastructure, such as expanding the cycling network and launching promotional campaigns, to promote cycling as a means of transportation and/or recreation.

Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility.

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity.

Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers.

The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R0, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R0 and both the number of trips and the HDI; in BH, the values of R0 show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.

Edge Computing for Effective and Efficient Traffic Characterization.

Traffic flow analysis is essential to develop smart urban mobility solutions. Although numerous tools have been proposed, they employ only a small number of parameters. To overcome this limitation, an edge computing solution is proposed based on nine traffic parameters, namely, vehicle count, direction, speed, and type, flow, peak hour factor, density, time headway, and distance headway. The proposed low-cost solution is easy to deploy and maintain. The sensor node is comprised of a Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a centroid tracking algorithm is used to estimate vehicle speed. The measured traffic parameters are transmitted to the ThingSpeak cloud platform via 4G. The proposed solution was field-tested for one week (7 h/day), with approximately 10,000 vehicles per day. The count, classification, and speed accuracies obtained were 79.8%, 93.2%, and 82.9%, respectively. The sensor node can operate for approximately 8 h with a 10,000 mAh power bank and the required data bandwidth is 1.5 MB/h. The proposed edge computing solution overcomes the limitations of existing traffic monitoring systems and can work in hostile environments.

Assessing the effectiveness of road safety measures in Barcelona (2013-2018).

Introduction: This article aims to determine the effectiveness and extent of measures taken to decrease the severity of traffic crashes in Barcelona from 2013 to 2018. This will be achieved through an analysis of the traffic crash data. Method: Our approach involves the use of binary logistic regression models. We rely on the traffic crash dataset from 2010-2019 available in the Open Data Barcelona platform. Results: The outcomes obtained from the suggested models are contrasted with the strategies outlined in the Local Road Safety Plan 2013-2018 to minimize the severity of crashes. Effective preventive actions were identified, such as road safety educational programs, creating calm zones, enhancing pedestrian crossings, or expanding bicycle lanes. However, certain measures were found to be ineffective or their impact remained uncertain. Conclusions: Our findings indicate that the measures implemented in Barcelona may have participated in and influenced the decrease in the severity of traffic incidents over the past decade. Notably, fatalities have decreased more than severe injuries. More attention should be given to less effective measures such as speed controls and drug/alcohol testing.

Dataset for multimodal transport analytics of smartphone users - Collecty.

Urban mobility is facing many challenges, such as energy consumption, pollution, and safety. Therefore, it is necessary to analyze the mobility of users through the transportation network using data containing information regarding the used transport mode. This data article describes a dataset from mobile devices collected by users as they move through the transportation network. Each sample in this dataset is labelled with a corresponding transport mode. Eight transport modes are present in the dataset: Car, Bus, Walking, Bicycle, Train, Tram, Running and Electric Scooter. The basic breakdown of the raw data according to users, transport modes and multimodal routes is presented. During data collection, data from the accelerometer, magnetometer, and gyroscope sensors mounted within the mobile device were stored. The data were collected using a mobile application from mobile devices with an embedded Android operating system. The structure of the text files in which the data were stored and the structure of the application used to collect the data are presented in the paper. The collected data provides a highly relevant basis for mobility analysis and planning, analysis of road conditions, clustering of user behaviour, and comparison of transport mode classification methods.

Telematics data for geospatial and temporal mapping of urban mobility: Fuel consumption, and air pollutant and climate-forcing emissions of passenger cars.

In this study, we use the approach of geospatial and temporal (GeoST) mapping of urban mobility to evaluate the speed-time-acceleration profile (dynamic status) of passenger cars. We then use a pre-developed model, fleet composition and real-world emission factor (EF) datasets to translate vehicles dynamics status into real-urban fuel consumption (FC) and exhaustive (CO2 and NOx) emissions with high spatial (15 m) and temporal (2 h) resolutions. Road transport in the West Midlands, UK, for 2016 and 2018 is the spatial and temporal scope of this study. Our approach enables the analysis of the influence of factors such as road slope, non-rush/rush hour and weed days/weekends effects on the characteristics of the transport environment. The results show that real-urban NOx EFs reduced by more than 14 % for 2016-18. This can be attributed to the increasing contribution of Euro 6 vehicles by 63 %, and the increasing contribution of diesel vehicles by 13 %. However, the variations in the real-urban FC and CO2 EFs are less significant (±2 %). We found that the FC estimated for driving under the NEDC (National European Driving Cycle) is a qualified benchmark for evaluating real-urban FCs. Considering the role of road slope increases the estimated real-urban FC, and NOx, and CO2 EFs by a weighted average of 4.8 %, 3.9 %, and 3.0 %, respectively. Time of travel (non-rush/rush hour or weed days/weekends) has a profound effect on vehicle fuel consumption and related emissions, with EFs increasing in more free-flowing conditions.

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic.

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021. By distinguishing between weekday and weekend usage, we found different temporal patterns between trip volume and duration, and distinct spatial patterns of within- and between-region rides with respect to naturally separated regions. Overall, the results illustrate the resilience and the vital role of bike-sharing during the pandemic, consistent with previous observations on bike-sharing systems of a larger scale. Our study contributes to a comprehensive understanding of bike-sharing that calls for more research on smaller-scale systems under disruptive events such as the pandemic, which can greatly inform decision-makers from smaller sized cities and enable future studies to compare across different urban regions or modes of transportation.

Impacts of COVID-19 on Bike-Share Usage: The case of Daejeon, Korea.

After COVID-19 began spreading through fecal-oral routes, crowded cities introduced social distancing policies. Mobility patterns in urban also changed because of the pandemic and the policies to reduce the infection of it. This study investigates the impact of COVID-19 and related policies such as social-distancing by comparing bike-share demand in Daejeon, Korea. By using big data analytics and data visualization, the study measures differences in bike-sharing demand between 2018-19, before the pandemic, and 2020-21, during the pandemic. According to results, (1) bike-share users tend to travel long distances and cycle more than before the pandemic, (2) bike users choose cycling not for commuting but for transportation during the pandemic, and (3) the pandemic has broadened the spatial borders bike-usages. These results provide meaningful implications for urban planners and policymakers by identifying differences in the ways people use public bikes during the pandemic era.

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study.

The COVID-19 pandemic has remarkably impacted urban mobility. All non-essential movements were restricted in Valencia (Spain) to contain the virus. Thus, the transport usage patterns of Valencia's bike-sharing system (BSS) users changed during this emergency situation. The primary objective of this study was to analyse the behaviour patterns of BSS users in Valencia before and after the COVID-19 outbreak, specifically those who maintained or changed their transport routines. A within-subjects comparison design was developed using a group of BSS users before and after the onset of the pandemic. Data mining techniques were used on a sample of 4355 regular users and 25 variables were calculated to classify users by self-organising maps analysis. The results show a significant reduction (40 %) in BSS movements after the outbreak during the entire post-outbreak year. There was some recovery during the rest of 2020; however, this has yet to reach the pre-pandemic levels, with variations observed based on the activities performed in different areas of the city. Of the users, 63 % changed their BSS use patterns after the onset of the pandemic (LEAVE group), while 37 % maintained their patterns (REMAIN group). The user profile of the REMAIN group was characterised by a general reduction of approximately 35 % of journeys during 2020, with a slight increase in morning movements compared to those made in the evening. These users also presented an equivalent number of cycling days to those of the previous year, reduced the number of connections and increased the network's density and the travelling speed. These results can be useful in estimating the percentage of people who do not vary their usual behaviour during emergencies. Finally, several policy implications are outlined based on the findings.

Urban mobility trends and climate change: sustainability policies in the parking industry.

The concern to create cleaner and more ecosystem-friendly production processes has extended to the parking sector in Spain. Since the creation of the multi-level institutional framework for sustainable mobility management (mainly composed of the Infrastructure, Transport and Housing Plan 2012-2024, the Sustainable Urban Mobility Plans, Law 9/2006, and Law 9/2017), environmental considerations, including sustainable management certificates, have occupied a privileged place in public procedures for the management of parking structures and regulated surface parking facilities. Although there have been previous academic studies on the design and implementation of SUMPs and the growth of the parking sector, this article is novel in that it analyzes the market concentration of the parking sector in a scenario where climate change policies are crucial and the importance of sustainability certificates takes on a new meaning. Therefore, the objective of this article is to analyze whether the growing importance of environmental aspects has led to an increase in the concentration level of the parking sector in Spain. For this purpose, several concentration and stability indices are calculated. The results show that, although there are additional factors, the certification of a cleaner activity is relevant in the process of public tenders in the sector, which has served to strengthen the dominance of the most prominent companies in the sector that are in possession of environmental certificates. This shows that environmental policies can also have negative effects on the market, so the results of this analysis are of great value to policymakers.

Telework and MaaS adoption in a post-pandemic scenario. Evidence from municipal employees of Padua, Italy.

In recent years, the adoption of teleworking has witnessed a significant surge, partly driven by the COVID-19 pandemic. The existing literature suggests that its implementation has elicited mixed reactions from workers; while some workers have expressed satisfaction with its introduction, others prefer to work in a traditional, on-site setting. Concurrently, there has been a growing interest in Mobility-as-a-Service (MaaS) and an increase in the number of companies offering such services. However, there is limited research exploring the relationship between teleworking and the utilization of MaaS. This paper aims to fill this gap, by analyzing (1) which factors influence users to adopt teleworking in a post-pandemic scenario and (2) the relationship between willingness to telework and the propensity to join a MaaS system. An ordered logit model and a mixed logit model were developed to achieve the two goals, respectively. These models were calibrated and validated using data collected from questionnaires administered to Padua Municipality employees between October 2020 and January 2021. As expected, the employees most inclined toward teleworking are those who seek more flexibility and are unable to commute by private means. In addition, results show that employees who expressed the preference to telework more in the future are less likely to adopt MaaS, suggesting that the increased popularity of teleworking due to the pandemic may have a negative effect on the uptake of MaaS. These findings were utilized to formulate several policy recommendations.

Design of a concept vehicle for future-oriented urban mobility using design-driven methodologies.

Due to ever-increasing technological acceleration leading to rapid changes in society and its needs, just as today's habits and needs turn out to be completely different from those of only a few years ago, likewise it is reasonable to assume that the same trend will continue in its growth path, making today's solutions rapidly obsolete as time passes and technological innovations follow. This study aims to investigate possible solutions in search of a futuristic and breakthrough response to what is present today. The idea concerns the design of a new type of means of transportation that can best interface with what today are the various criticalities given by vehicular traffic mainly urban but also suburban, going to solve by generating new opportunities from previous problems. This system will be able to go alongside and gradually replace a substantial portion of the current means of transport going to conceptually redefine some elements taken for granted today. In this regard, the application of the IDeS (Industrial Design Structure) methodology has been found to be of great use, which, thanks to the scientific and repeatable methods contained within, has made it possible to arrive at a very clear visualization of the problem, a precise definition, and a level of innovation that is fully satisfactory with respect to the contemporary scenario, while always keeping an eye on feasibility while taking into account the conceptual and therefore deliberately very driven nature of the solution being designed.

Modeling non-linear changes in an urban setting: From pro-environmental affordances to responses in behavior, emissions and air quality.

Interactions in urban environment were investigated using a multidisciplinary model combination, with focus on traffic, emissions and atmospheric particles. An agent-based model was applied to simulate the evolution of unsustainable human behavior (usage of combustion-based personal vehicles) as a function of pro-environmental affordances (opportunities for sustainable choices). Scenarios regarding changes in multi-pollutant emissions were derived, and the non-linear implications to atmospheric particles were simulated with a box model. Based on the results for a Nordic city, increasing pro-environmental affordances by 10%, 50% or 100% leads to emission reductions of 15%, 30% and 40% within 2 years. To reduce ambient particle mass, emissions from traffic should decrease by > 15%, while the lung deposited surface area decreases in all scenarios ([Formula: see text], [Formula: see text] and [Formula: see text], correspondingly). The presented case is representative of one season, but the approach is generic and applicable to simulating a full year, given meteorological and pollution data that reflects seasonal variation. This work emphasizes the necessity to consider feedback mechanisms and non-linearities in both human behavior and atmospheric processes, when predicting the outcomes of changes in an urban system.