Urban and social sensing for sustainable mobility in smart cities

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Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces

The sustainable development of smart cities through digital innovation

The ‘smart city’ concept has been wrought from distinctive theoretical underpinnings. Initially, this term was used to describe those cities that utilized advanced computerized systems to provide a safe, secure, green, and efficient transportation services and utilities to meet the demands of their citizens (Caragliu, Del Bo & Nijkamp, 2011; Hall, Bowerman and Braverman, Taylor, Todosow and Von Wimmersperg, 2000). A thorough literature review suggests that several cities are already using disruptive technologies, including advanced, integrated materials, sensors, electronics, and networks, among others, which are interfaced with computerized systems to improve their economic, social and environmental sustainability (Camilleri, 2015, 2017; Deakin and Al Waer, 2011; Hall et al., 2000). These cities are increasingly relying on data-driven technologies, as they gather and analyze data from urban services including transportation and utilities (Ramaswami, Russell, Culligan, Sharma and Kumar, 2016; Gretzel, Sigala, Xiang and Koo, 2015). Their underlying objective is to improve the quality of life of their citizens (Ratten, 2017; Buhalis and Amaranggana, 2015). Hence, ‘smart cities’ have introduced technological innovations to address contingent issues like traffic congestion; air pollution; waste management; loss of biodiversity and natural habitat; energy generation, conservation and consumption; water leakages and security, among other matters (Camilleri, 2019; 2014; Ahvenniemi, Huovila, Pinto-Seppä and Airaksinen, 2017; Ratten and Dana, 2017; Ratten, 2017). Ecologically-advanced local governments and municipalities are formulating long-term sustainable policies and strategies. Some of them are already capturing data through multisensor technologies via wireless communication networks in real time (Bibri, 2018; Bibri and Krogstie, 2017). Very often, they use the Internet’s infrastructure and a wide range of smart data-sensing devices, including radio frquency identification (RFID), near-field communication (NFC), global positioning systems (GPS), infrared sensors, accelerometers, and laser scanners (Bibri, 2018). A few cities have already started to benefit from the Internet of Things (IoT) technology and its sophisticated network that consists of sensor devices and physical objects including infrastructure and natural resources (Zanella, Bui, Castellani, Vangelista and Zorzi, 2014). Several cities are crunching big data to better understand how to make their cities smarter, more efficient, and responsive to today’s realities (Mohanty, Choppali and Kougianos, 2016; Ramaswami et al., 2016). They gather and analyze a vast amount of data and intelligence on urban aspects, including transportation issues, citizen mobility, traffic management, accessibility and protection of cultural heritage and/or environmental domains, among other areas (Angelidou, Psaltoglou, Komninos, Kakderi, Tsarchopoulos and Panori, 2018; Ahvenniemi et al., 2017). The latest advances in technologies like big data analytics and decision-making algorithms can support local governments and muncipalities to implement the circular economy in smart cities (Camilleri, 2019). The data-driven technologies enable them them to reduce their externalities. They can monitor and control the negative emissions, waste, habitat destruction, extinction of wildlife, etc. Therefore, the digital innovations ought to be used to inform the relevant stakeholders in their strategic planning and development of urban environments (Camilleri, 2019; Allam & Newman, 2018; Yigitcanlar and Kamruzzaman, 2018; Angelidou et al. ,2018; Caragliu et al., 2011). In this light, we are calling for theoretical and empirical contributions that are focused on the creation, diffusion, as well as on the utilization of technological innovations and information within the context of smart, sustainable cities. This Special Issue will include but is not limited to the following topics: • Advancing the circular economy agenda in smart cities; • Artificial intelligence and machine learning in smart cities; • Blockchain technologies in smart cities; • Green economy of smart cities; • Green infrastructure in smart cities; • Green living environments in smart cities; • Smart cities and the sustainable environment; • Smart cities and the use of data-driven technologies; • Smart cities and the use of the Internet of Things (IoT); • Sustainable energy of smart cities; • Sustainable financing for infrastructural development in smart cities; • Sustainable housing in smart cities; • Sustainable transportation in smart cities; • Sustainable tourism in smart cities; • Technological innovation and climate change for smart cities; • Technological innovation and the green economy of smart cities; • Technological innovation and the renewable energy in smart cities; • Technological innovation and urban resilience of smart cities; • Technological innovation for the infrastructural development of smart cities; • The accessibility and protection of the cultural heritage in smart cities; • The planning and design of smart cities; • The quality of life of the citizens and communities living in smart cities; • Urban innovation in smart cities; • Urban planning that integrates the smart city development with the greening of the environment; • Urban planning and data driven technologies of smart cities.peer-reviewe

Roadmaps to Utopia: Tales of the Smart City

Notions of the Smart City are pervasive in urban development discourses. Various frameworks for the development of smart cities, often conceptualized as roadmaps, make a number of implicit claims about how smart city projects proceed but the legitimacy of those claims is unclear. This paper begins to address this gap in knowledge. We explore the development of a smart transport application, MotionMap, in the context of a £16M smart city programme taking place in Milton Keynes, UK. We examine how the idealized smart city narrative was locally inflected, and discuss the differences between the narrative and the processes and outcomes observed in Milton Keynes. The research shows that the vision of data-driven efficiency outlined in the roadmaps is not universally compelling, and that different approaches to the sensing and optimization of urban flows have potential for empowering or disempowering different actors. Roadmaps tend to emphasize the importance of delivering quick practical results. However, the benefits observed in Milton Keynes did not come from quick technical fixes but from a smart city narrative that reinforced existing city branding, mobilizing a growing network of actors towards the development of a smart region. Further research is needed to investigate this and other smart city developments, the significance of different smart city narratives, and how power relationships are reinforced and constructed through them

Smart mobility: opportunity or threat to innovate places and cities

The concept of the “smart mobility” has become something of a buzz phrase in the planning and transport fields in the last decade. After a fervent first phase in which information technology and digital data were considered the answer for making mobility more efficient, more attractive and for increasing the quality of travel, some disappointing has grown around this concept: the distance between the visionarypotentialthatsmartness is providingis too far from the reality of urban mobility in cities. We argue in particular that two main aspects of smart mobility should be eluded: the first refers to the merely application to technology on mobility system, what we called the techo-centric aspect; the second feature is the consumer-centric aspect of smart mobility, that consider transport users only as potential consumers of a service. Starting from this, the study critics the smart mobility approach and applications and argues on a“smarter mobility” approach, in which technologies are only oneaspects of a more complex system. With a view on the urgency of looking beyond technology and beyond consumer-oriented solutions, the study arguments the need for a cross-disciplinary and a more collaborative approach that could supports transition towards a“smarter mobility” for enhancing the quality of life and the development ofvibrant cities. The article does not intend to produce a radical critique of the smart mobility concept,denying a priori its utility. Our perspectiveisthat the smart mobility is sometimes used as an evocativeslogan lacking some fundamental connection with other central aspect of mobility planning and governance. Main research questions are: what is missing in the technology-oriented or in the consumers-oriented smart mobility approach? What are the main risks behind these approaches? To answer this questions the paper provides in Section 2 the rationale behind the paper;Section 3 provides a literature review that explores the evolution on smart mobility paradigm in the last decades analysing in details the “techno-centric”and the “consumer-centric” aspects. Section 4proposes an integrated innovative approach for smart mobility, providing examples and some innovative best practices in Belgium. Some conclusions are finally drawnin Section 5, based on the role of smart mobility to create not only virtual platforms but high quality urban places

Integrating big data into a sustainable mobility policy 2.0 planning support system

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations

Managing ubiquitous eco cities: the role of urban telecommunication infrastructure networks and convergence technologies

A successful urban management system for a Ubiquitous Eco City requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism and necessary infrastructure and technologies. Rapidly developing information and telecommunication technologies and their platforms in the late 20th Century improves urban management and enhances the quality of life and place. Telecommunication technologies provide an important base for monitoring and managing activities over wired, wireless or fibre-optic networks. Particularly technology convergence creates new ways in which the information and telecommunication technologies are used. The 21st Century is an era where information has converged, in which people are able to access a variety of services, including internet and location based services, through multi-functional devices such as mobile phones and provides opportunities in the management of Ubiquitous Eco Cities. This paper discusses the recent developments in telecommunication networks and trends in convergence technologies and their implications on the management of Ubiquitous Eco Cities and how this technological shift is likely to be beneficial in improving the quality of life and place. The paper also introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for Ubiquitous Eco Cities

Transport and traffic analytics in smart cities

Vast generation of high resolution spatial and temporal data, particularly in urban settings, started revolution in mobility and human behavior related research. However, after initial wave of first data oriented insights their integration into ongoing, and traditionally used, planning and decision making processes seems to be hindered by still opened challenges. These challenges suggest need for stronger integration between data analytics and dedicated domain knowledge. Special session on Transport and Traffic Analytics in Smart Cities tackles these challenges from transport planners’ point of view. Collection of papers aims at identifying the existing gaps and bridging between related disciplines with aspiration to foster faster integration of data driven insights into smart cities’ dedicated planning

Forecasting transport mode use with support vector machines based approach

The paper explores potential to forecast what transport mode one will use for his/her next trip. The support vector machines based approach learns from individual's behavior (validated GPS tracks) to support smart city transport planning services. The overall success rate, in forecasting the transport mode, is 82 %, with lower confusion for private car, bike and walking