Smart Mobility in Smart City

Benevolo, Clara; Dameri, Renata Paola; D’Auria, Beatrice

doi:10.1007/978-3-319-23784-8_2

Cited by 333 publications

(206 citation statements)

References 11 publications

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“…It is possible to identify many studies focused on individual applications in the literature, while it is more difficult to find studies that provide a holistic view of these and related actions [26]. Benevolo et al [26] (p. 16) state that smart mobility "could also be seen as a set of coordinated actions addressed at improving the efficiency, the effectiveness and the environmental sustainability of cities. In other words Smart Mobility could consist of a hypothetically infinite number of initiatives often (but not always) characterized by the use of ICT".…”

Section: Differences Between Sustainable Mobility and Smart Mobilitymentioning

confidence: 99%

Urban Policies and Mobility Trends in Italian Smart Cities

2017

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This study uses indicators to evaluate the progress made by Italian cities in the smart mobility sector, in order to understand how these cities have approached the new guidelines imposed by the European Union, and how they have implemented the European changes. Specifically, this study analyses the evolution of public transportation systems, using a sample of twenty-two Italian cities for three successive time periods (2005, 2010, and 2015). The outcomes identified are then linked to funding provided for the implementation of projects related to smart mobility in the cities studied, in order to verify possible correlations between the growth of these services and European and national financial investments. The data analysis shows remarkable progress in the field of sustainable mobility, especially between 2010 and 2015 as well as how this progress is linked to significant financial support that favours the realization of projects related to smart mobility.

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Section: Differences Between Sustainable Mobility and Smart Mobilitymentioning

confidence: 99%

Urban Policies and Mobility Trends in Italian Smart Cities

2017

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“…Information and communications technology (ICT) have also influenced the mobility sector and has made shared mobility services more feasible. Carsharing and bicycle sharing are considered essential contributions to smart mobility solutions [5][6][7][8].…”

Section: Literature Reviewmentioning

confidence: 99%

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

2017

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Abstract:When the first free-floating carsharing operators launched their business, they did not know if it would be profitable. They often started in highly populated cities without performing extensive target group analysis, and were less concerned about fleet management. Usually, there are two main datasets that can be used to find areas that would have a high demand for free-floating carsharing: booking data, for measuring the actual demand; and land use and census data for describing the activities performed in different areas in a city. In this paper, we aim to use this information to help predict the demand of free-floating carsharing systems. We use booking data provided by DriveNow for Berlin in 2014 and contextual information about the type of activity each neighborhood has. Using Berlin as a case study, we apply a negative binomial statistical model to explain the number of bookings. From the results, we conclude that free-floating carsharing is predominantly successful in areas with more affluent citizens who are open to trying new and sustainable technologies. Other important determinants that result in a high number of carsharing bookings are the area's centrality and parking lot availability. The statistical model for Berlin was then transferred to Munich and Cologne, two other cities in Germany with similar population sizes. A comparison between the estimated demand categories and actual bookings shows satisfying results, but also non-negligible local conditions influencing the spatial demand for bookings.

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“…Smart mobility can be regarded as a collection of coordinated activities aimed at improving efficiency, effectiveness, and environmental sustainability [46]. In addition, smart mobility generates an impact through the combination of technology, existing physical and technical capabilities, and by considering human and social needs [47].…”

Section: (P 2)mentioning

confidence: 99%

Mapping Parallels between Outdoor Urban Environments and Indoor Manufacturing Environments

Schabus

Scholz

Lampoltshammer

2017

IJGI

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Abstract:The concepts of "Smart Cities" and "Smart Manufacturing" are different data-driven domains, although both rely on intelligent information technology and data analysis. With the application of linked data and affordance-based approaches, both domains converge, paving the way for new and innovative viewpoints regarding the comparison of urban tasks with indoor manufacturing tasks. The present study builds on the work, who state that cities are scaled versions of each other, by extending this thesis towards indoor manufacturing environments. Based on their structure and complexity, these environments are considered to form ecosystems of their own, comparable to "small cities". This conceptual idea is demonstrated by examining the process of human problem-solving in transportation situations from both perspectives (i.e., city-level and manufacturing-level). In particular, the authors model tasks of human operators that are used to support transportation processes in indoor manufacturing environments based on affordances and spatial-temporal data. This paper introduces the fundamentals of the transformation process of outdoor tasks and process planning activities to indoor environments, particularly to semiconductor manufacturing environments. The idea is to examine the mapping of outdoor tasks and applications to indoor environments, and vice-versa, based on an example focusing on the autonomous transportation of production assets in a manufacturing environment. The approach is based on a spatial graph database, populated with an indoor navigation ontology and instances of indoor and outdoor objects. The results indicate that human problem-solving strategies can be applied to indoor manufacturing environments to support decision-making in autonomous transportation tasks.

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Smart Mobility in Smart City

Cited by 333 publications

References 11 publications

Urban Policies and Mobility Trends in Italian Smart Cities

Urban Policies and Mobility Trends in Italian Smart Cities

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

Mapping Parallels between Outdoor Urban Environments and Indoor Manufacturing Environments

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