mMarketing Opportunities for User Collaborative Environments in Smart Cities

2017 ◽  
pp. 219-247
Author(s):  
Artemis D. Avgerou ◽  
Despina A. Karayanni ◽  
Yannis C. Stamatiou
Keyword(s):  
Wireless Communications ◽  
Mobile Devices ◽  
Large Scale ◽  
Collective Intelligence ◽  
Personal Information ◽  
Smart Cities ◽  
Mobility Patterns ◽  
Communications Networks ◽  
Pollution Levels ◽  
Commercial Activities

Smart City infrastructures connect people with their devices through wireless communications networks while they offer sensor-based information about the city's status and needs. Connecting people carrying mobile devices equipped with sensors through such an infrastructure leads to the “collective intelligence” or “crowdsourcing” paradigm. This paradigm has been deployed in numerous contexts such as performing large-scale experiments (e.g., monitoring the pollution levels or analyzing mobility patterns of people to derive useful information about rush hours in cities) or gathering and sharing user collected experiences in efforts to increase privacy awareness and personal information protection levels. In this chapter, we will focus on employing this paradigm in the mMarketing/mCommerce domain and discuss how crowdsourcing can create new opportunities for commercial activities as well as expansion of existing ones.

mMarketing Opportunities for User Collaborative Environments in Smart Cities

2017 ◽  
pp. 1213-1241
Author(s):  
Artemis D. Avgerou ◽  
Despina A. Karayanni ◽  
Yannis C. Stamatiou
Keyword(s):  
Wireless Communications ◽  
Mobile Devices ◽  
Large Scale ◽  
Collective Intelligence ◽  
Personal Information ◽  
Smart Cities ◽  
Mobility Patterns ◽  
Communications Networks ◽  
Pollution Levels ◽  
Commercial Activities

Smart City infrastructures connect people with their devices through wireless communications networks while they offer sensor-based information about the city's status and needs. Connecting people carrying mobile devices equipped with sensors through such an infrastructure leads to the “collective intelligence” or “crowdsourcing” paradigm. This paradigm has been deployed in numerous contexts such as performing large-scale experiments (e.g., monitoring the pollution levels or analyzing mobility patterns of people to derive useful information about rush hours in cities) or gathering and sharing user collected experiences in efforts to increase privacy awareness and personal information protection levels. In this chapter, we will focus on employing this paradigm in the mMarketing/mCommerce domain and discuss how crowdsourcing can create new opportunities for commercial activities as well as expansion of existing ones.

scholarly journals Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

2020 ◽  
Vol 9 (2) ◽  
pp. 125 ◽  
Author(s):  
Zeinab Ebrahimpour ◽  
Wanggen Wan ◽  
José Luis Velázquez García ◽  
Ofelia Cervantes ◽  
Li Hou
Keyword(s):  
Social Media ◽  
Large Scale ◽  
City Planning ◽  
Smart Cities ◽  
Human Mobility ◽  
Location Based Services ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Planning Decisions ◽  
Media Data

Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

scholarly journals Geosmartness for Personalized and Sustainable Future Urban Mobility

2021 ◽  
pp. 59-83
Author(s):  
Martin Raubal ◽  
Dominik Bucher ◽  
Henry Martin
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Information Technologies ◽  
Large Scale ◽  
Smart Cities ◽  
Mobile Technologies ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Movement Data ◽  
Spatio Temporal

AbstractUrban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

scholarly journals Wireless Sensor Networks for Smart Cities: Network Design, Implementation and Performance Evaluation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 218
Author(s):  
Ala’ Khalifeh ◽  
Khalid A. Darabkh ◽  
Ahmad M. Khasawneh ◽  
Issa Alqaisieh ◽  
Mohammad Salameh ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Experimental Evaluation ◽  
Large Scale ◽  
Performance Metrics ◽  
Smart Cities ◽  
Field Tests ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Real Field

The advent of various wireless technologies has paved the way for the realization of new infrastructures and applications for smart cities. Wireless Sensor Networks (WSNs) are one of the most important among these technologies. WSNs are widely used in various applications in our daily lives. Due to their cost effectiveness and rapid deployment, WSNs can be used for securing smart cities by providing remote monitoring and sensing for many critical scenarios including hostile environments, battlefields, or areas subject to natural disasters such as earthquakes, volcano eruptions, and floods or to large-scale accidents such as nuclear plants explosions or chemical plumes. The purpose of this paper is to propose a new framework where WSNs are adopted for remote sensing and monitoring in smart city applications. We propose using Unmanned Aerial Vehicles to act as a data mule to offload the sensor nodes and transfer the monitoring data securely to the remote control center for further analysis and decision making. Furthermore, the paper provides insight about implementation challenges in the realization of the proposed framework. In addition, the paper provides an experimental evaluation of the proposed design in outdoor environments, in the presence of different types of obstacles, common to typical outdoor fields. The experimental evaluation revealed several inconsistencies between the performance metrics advertised in the hardware-specific data-sheets. In particular, we found mismatches between the advertised coverage distance and signal strength with our experimental measurements. Therefore, it is crucial that network designers and developers conduct field tests and device performance assessment before designing and implementing the WSN for application in a real field setting.

A Framework for the Design and Deployment of Large-Scale LPWAN Network for Smart Cities Applications

2020 ◽  
pp. 1-8
Author(s):  
Bassel Al Homssi ◽  
Akram Al-Hourani ◽  
Kagiso Magowe ◽  
James Delaney ◽  
Neil Tom ◽  
...  
Keyword(s):  
Large Scale ◽  
Smart Cities

scholarly journals Gender Inequality in Safety and Security Perceptions in Railway Stations

2021 ◽  
Vol 13 (7) ◽  
pp. 4007
Author(s):  
Pierluigi Coppola ◽  
Fulvio Silvestri
Keyword(s):  
Gender Gap ◽  
Public Transport ◽  
Stated Preference ◽  
Revealed Preference ◽  
Behavioral Models ◽  
Mobility Patterns ◽  
Railway Stations ◽  
Commercial Activities ◽  
Transport Services ◽  
Disaggregate Data

Recent studies have shown that gender is the personal aspect that mostly affects mobility patterns and travel behaviors. It has been observed, for instance, that female perception of unsafety and insecurity when traveling using public transport forces them to make unwanted travel choices, such as avoiding traveling at certain times of day and to specific destinations. In order to improve the attractiveness of public transport services, this gender gap must not be overlooked. This paper aims at contributing to research in gendered mobility by investigating differences in safety and security perceptions in railway stations, and by identifying which policies could be effective in bridging any existing gap. The methodology includes the collection of disaggregate data through a mixed Revealed Preference/Stated Preference survey, and the estimation of fixed and random parameters behavioral models. Results from a medium-sized Italian railway station show that female travelers feel safer in the presence of other people; they prefer intermodal infrastructures close to the entrance of the station and commercial activities in the internal premises. Moreover, unlike male travelers, they do not appreciate the presence of hedges and greenery outside stations.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Sign in / Sign up

Export Citation Format

Share Document