Urban Mobility and Influence Factors: A Case Study of Prague

Sobková, Ladislava Fialka; Čertický, Michal

doi:10.2495/ut170181

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…As one of the major sources of pollutants in our cities is road traffic (Steele, 2001), reducing it would be an effective strategy to improve urban livability and their inhabitants' health. For example, several studies have proven that pedestrianization has a positive impact on health (Sobková and Čertickỳ, 2017;Soni and Soni, 2016;Tobon et al, 2018;Ward, 2010). Consequently, many cities have started to shift toward non-car friendly access (Parajuli and Pojani,2 Irene Lebrusán, Jamal Toutouh 2018; Sobková and Čertickỳ, 2017;Tobon et al, 2018;Ward, 2010).…”

Section: Introductionmentioning

confidence: 99%

Car restriction policies for better urban health: a low emission zone in Madrid, Spain

Lebrusán¹,

Toutouh

2020

Air Qual Atmos Health

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The design of most cities prioritizes the use of motorized vehicles, having a negative effect on urban health. A major concern in the European Union (EU) is air pollution, especially nitrogen dioxide (NO 2 ), which causes many inhabitants health problems and decreases the quality of life. A non-expensive way to reduce pollutants is implementing road restriction policies, as the creation of low emission zones. In this work, we analyze the case of Madrid Central a low emission zone deployed in Madrid, Spain. We evaluate if it was effective to reduce air pollutants and if there were a side effect, as pollution displacement, during its application. Drawing on open data, we analyze air quality at different points of the city, before and during the application of this measure. Taking into account the EU directives in terms of what healthy air means, we consider three metrics: a) the trend of NO 2 concentration in the air in both periods, b) the difference between the NO 2 concentration during both periods, and c) the percentage of time in which the population is exposed to air with NO 2 concentration under a specific threshold (healthy air as defined by the EU). According to the results, Madrid Central significantly reduces the NO 2 concentration in the air and does not produce pollution displacement. Thus, the population breathes healthy air during more time, and there is a positive effect on the whole city.

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Section: Introductionmentioning

confidence: 99%

Car restriction policies for better urban health: a low emission zone in Madrid, Spain

Lebrusán¹,

Toutouh

2020

Air Qual Atmos Health

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“…In this way walking is incorporated in people’s everyday transport routines. A recent case study in Prague showed that daily walking routines – such as the walk from home to the public transport station and the walk from the car park to the workplace and back – covered, on average, 85.4% of people’s daily walking activity (Sobková and Čertický, 2018).…”

Section: Introductionmentioning

confidence: 99%

Walking Without Awareness

et al. 2019

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Pedestrians are commonly engaged in other activities while walking. The current study assesses (1) whether pedestrians are sufficiently aware of their surroundings to successfully negotiate obstacles in a city, and (2) whether various common walking practices affect awareness of obstacles and, or, avoidance behavior. To this end, an obstacle, i.e., a signboard was placed on a pavement in the city centre of Utrecht, the Netherlands. The behavioral measure consisted of the distance to the signboard before pedestrians moved to avoid it. After passing, participants were interviewed to obtain thought samples, self-reported route familiarity, a confirmation of secondary task engagement, and to assess awareness through recall and recognition of the signboard and its text. In this study 234 pedestrians participated. More than half of the participants (53.8%) was unaware of the signboard, still none of them had bumped into it. Mind wandering, being engaged in secondary tasks such as talking with a companion or using a mobile phone, and being familiar with a route, did not affect awareness nor avoidance behavior. In conclusion, despite being very common there was no evidence that walking without awareness necessarily results in risk. The absence of awareness does not imply any absence of cognitive and perceptual processing. Pedestrians are still capable of successfully avoiding obstacles in their path, even in visually more challenging environments such as a city centre. It is argued that this is because walking consists of highly automated, skilled behavior.

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“…Databases containing the data categories indicated in Table 1 can provide data to examine the influence of sex, age [40] and BMI on ordinary daily physical activity of the population.…”

Section: Physical Activity Researchmentioning

confidence: 99%

Smartphone-Based Sensing: Lifestyle and Mobility Data Interpretation by Smart Cities

Sobková

Jiráèek

Èertický

2020

WIT Transactions on Ecology and the Environment

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The increasing demand for data and smart solutions is one of the fastest growing sectors of human activity. In recent decades smartphones and mobile phones have become a significant and stable source of data. Architects and urban planners have used them in various cases to identify urban patterns. This paper focuses on data gathered by fitness tracker applications which collect information about the movement of their users. The applications record the trajectory of the movement and detect the mode of transport. They require some basic information about the user (age, weight, height and sex) to calculate their caloric consumption. The data from activity tracking smartphone applications create a data lake that can be transformed into a new data source for the designing of healthier and more liveable cities. Combining the data layers and analysing them further could reveal properties and qualities of life in a given location that would not be apparent without processing these data. By analysing the data, we can observe the current state as well as tendencies in human behaviour over longer periods of time. Through the observation and comparison of physical activity in different urban contexts (topography, size of settlement, density of population, density of infrastructure, quality of public spaces, location, etc.), we can develop new alternatives and better knowledge of the influence the above-mentioned factors have on the life in cities. This paper describes data layer combinations that bring novel insights into the connection of physical activity and urban contexts by using data mining technology based on smartphone applications. The theoretical framework can be subsequently applied to various data sets with certain properties.

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Urban Mobility and Influence Factors: A Case Study of Prague

Cited by 8 publications

References 16 publications

Car restriction policies for better urban health: a low emission zone in Madrid, Spain

Car restriction policies for better urban health: a low emission zone in Madrid, Spain

Walking Without Awareness

Smartphone-Based Sensing: Lifestyle and Mobility Data Interpretation by Smart Cities

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