Smart City Planning Research Articles

IoT Based Solar Powered Smart Waste Management System with Real Time Monitoring- An Advancement for Smart City Planning

In this paper, we proposed an IoT based solar-powered smart waste management system which is suitable for any kind city or town in both developed and developing countries that can ensure proper collection, transportation, and disposal of household and industrial waste with real-time remote monitoring. To maintain the green and clean environment around us, precise collection and disposal of garbage in a regular fashion are necessary. The primary goal of this research work is to provide a complete smart solution for waste collection and disposal hence ensuring a comfortable environment. The proposed system enables real-time remote monitoring of solar-powered several smart bins located in different points in the city which are connected to the control station through long-range (LoRa) communication device and also supervises the waste collector activities like collection and disposal time using Automated Vehicles Locating System (AVLS).

Research on Urban and Rural Planning and Smart City Construction in Big Data Era

Recent years have witnessed faster information development and the wide application of big data, Internet of Things (IoT) and other intelligent technologies, which has laid a solid foundation for the development of new technologies and new environment. [1] Countries and regions around the world are promoting the digital smart development strategy with the hope of building smart urban and rural areas. In big data era, the main task of smart city planning and construction is the integration of data resources and the open sharing of data information. Therefore, information technology is indispensable for the planning and construction. Proceeding from the big data era, the paper explores technical details related to smart city planning and construction, aiming to create a more comfortable and livable environment for people and promote the overall improvement of China’s urban construction. [2]

Analyzing expectation of landowners from Brownfield TOD project: A case study from MP Nagar, Bhopal

Population growth in urban areas induced the demand for housing, job opportunity, and education, thereby stretching the urban boundaries and thus resulting in urban sprawl. Affordable car prices, unsafe pedestrian pathways, and lack of accessibility, people prefer to travel in private vehicles than walking. Transit-Oriented Development (TOD) is an urban development concept which holds key to sustainable and smart city planning but a major challenge for TOD implementation has been the availability of land. The purpose of this study is to analyze the expectations of landowners of prospective TOD site and establish that the stakeholder and landowner consultation before preparing the plan for TOD areas, is the key to success. The study area is Maharana Pratap (M.P). Nagar, a commercial hub in the city of Bhopal, India. The mixed research methodology approach is adopted to extract the opinion of shop / land owners and people visiting the area, through survey questionnaires. The methods used in the analysis are the Chi-square test of association and regression analysis. Chi-square result outcome was not significant as there is no association between ownership and moe choice opted by them. Regression model includes Landscaped and Safe Street Network (LSSN), Quality of Living (QoL), Pedestrian and Cycling Pathway (PCP), and Trip Time for the workplace (TT_WP) best predictors for opting TOD by participants. Research result indicates that people are motivated to walk if provided with safe pedestrian movement along with landscaping and effective urban design of the area, and are willing to opt for TOD based development if these conditions are met. The research will help policymakers and researchers to build a land development model integrating transit and land parcels, based upon stakeholders expectations.

A modelling framework for integrated smart city planning and management

Observation of global smart city trends shows a shift in focus from sector-based interventions towards integrated decision-making informed by Big Data. This move towards integration is evident in the emergence of Integrated City Management Platforms (ICMPs). Despite the deluge of data generated by ICMPs and the accompanying growth in computing power, limited research has been conducted on exploring the use of this data to develop quantitative tools for integrated smart city planning and management. In this study, the Design Science Research process was followed to develop and evaluate a modelling framework aimed at exploring the use of ICMP data to identify synergies and dependencies across smart city sectors. This paper provides a summary of framework design and implementation and discusses the Design Science Knowledge gained from the exercise.

Data/infrastructure in the smart city: Understanding the infrastructural power of Citymapper app through technicity of data

Over the last few years, smart cities have been a focus of scholarly attention. Most of these critical studies concentrated on the multinational corporations’ discourses and their implications on urban policies. Besides these factors, however, the data-driven city develops within a complex web of entanglements whereby data-driven technologies modulate the urban infrastructure in a multitude of ways contingent upon the social, political, material and technical aspects. As such, this article attends to the infrastructural implications of a smart city product, Citymapper, which is a transport app built on open data available as part of London’s smart city planning. In order to establish the relationship between data and infrastructure, I use Gilbert Simondon’s notions of ‘transduction’, ‘individuation’ and ‘technicity’ to explore this relationship in a processual and relational way. In constructing this relationship as co-generative, whereby infrastructure and data transindividuate, I subsequently posit the term data/infrastructure. Against this theoretical background, I study the ways in which Citymapper individuates and thereby gains infrastructural power through technicity of data by studying the ways in which the users’ contribute to data generation that feed back into the app. Specifically, by following the transformation of the app from initially mediating the bus timetable to transducing users into environmental sensing nodes through which the app collects behavioural data, I foreground the epistemological, infrastructural and social consequences of Citymapper’s infrastructural power for the data-driven city.

Performance of Delay Tolerant Network Protocol in Smart City Scenario

The smart city is being developed in many big cities to support local governments for resources and infrastructure optimization in terms of enhancing services for the citizen. Large investment costs are required to implement a communication network for the smart city infrastructure (i.e. Internet of Things (IoT) devices) using 4G or Wi-Fi networks. This study aims to implement Delay Tolerant Network (DTN) as a low-cost alternative network for the smart city. We use Surabaya smart city plan scenario in which smart mobility services are provided in city transportation and infrastructure. The IoT devices are installed in numerous locations and continuously transmit messages to the monitoring server located in the city hall, bus and car employed as a DTN node. In this paper, as a preliminary research, we investigate the performance of well-known DTN routing protocols (Epidemic, Maxprop, and ProphetV2) with our proposed protocol called Spray and Hop Distance (SNHD). The evaluation is performed using the ONE simulator in terms of the number of delivered messages, the average latency, and the overhead ratio. The results show the performance of SNHD is better than the well-known DTN protocols in various conditions when the total size of delivered messages is large. Unfortunately, none of those routing protocols has a good performance in all of the scenario conditions. In the future, we desire to implement this system in real smart city system in Surabaya and do simulation with more realistic scenario such as real traffic-trace.

Canadian smart cities: Are we wiring new citizen‐local government interactions?

Governments around the world are developing smart city projects, with the aim to realize diverse goals of increased efficiency, sustainability, citizen engagement, and improved delivery of services. The processes through which these projects are conceptualized vary dramatically, with potential implications for how citizens are involved or engaged. This research examines the 20 finalists in the Canadian Smart Cities Challenge, a Canadian federal government contest held from 2017 to 2019 to disburse funding in support of smart city projects. We analyzed each of the finalist proposals, coding all instances of citizen engagement used to develop the proposal. A significant majority of the proposals used traditional types of citizen engagement, notably citizen meetings, round tables, and workshops, to develop their smart city plans. We also noted the use of transactional forms of citizen engagement, such as apps, and the use of social media. Despite the general rhetoric of innovation in the development of smart cities, this research finds that citizens are most commonly engaged in traditional ways. This research provides cues for governments that are developing smart city projects, placing an emphasis on the importance of the process of smart city development, and not simply the product.

A case-based reasoning recommender system for sustainable smart city development

With the deployment of information and communication technologies (ICTs) and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning (CBR) as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions.

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning.

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

Understanding the What, Why, and How of Becoming a Smart City: Experiences from Kakinada and Kanpur

Rapid urbanization can result in challenges, such as overcrowding, congestion, and a lack of urban services. To address these challenges, an increasing number of communities are exploring the concept of a smart city (SC). Although rapid urbanization is a problem for cities around the world, its consequences can be severe for those located in developing nations. While previous studies have focused on SCs that were built from the ground up, there is a critical need for studies that focus on how to advance SC initiatives in developing regions faced with limited land and resources. This study identified two proposed SCs in India—Kakinada and Kanpur—which are currently implementing SC projects to explore their SC transformation. This case study aims to explore how “smartness” is understood in these cities and examines the local conditions shaping SC objectives by studying the existing issues in the cities, the proposed projects, and the perception of SC experts on a) what they understand by “smartness”; b) why cities want to become smart; and c) how they will become smart. The study findings indicate that although the high-level goals of the proposed SCs in India are similar to those of existing SCs in developed nations, the underlying objectives and strategies vary and are shaped by the urbanization challenges facing the Indian cities. This research also highlights the key questions a SC planning effort should address, especially in a developing nation context.

Examining digital participatory planning: Maturity assessment in a Small Dutch city

This paper calls for a rational and gradual transition to Digital Participatory Planning (DPP) as part of the sustainable smart city model. In recent years, the role that Information and Communications Technology (ICT) could play in advancing participatory planning has been widely acknowledged. The literature suggests that several factors are affecting the introduction of DPP into smart city planning. This study examines organization-related and society-related factors, aiming at identifying the level of maturity towards the introduction of DPP in cities, with particular attention to small European cities. These two groups of factors were examined in Schiedam, Netherlands via a mixed-methods approach and analyzed using NVIVO and SPSS software. The results suggest that for cities to be mature enough to introduce DPP, there should be an existing good practice of conventional participatory planning where the concept is extensively practiced, as well as a relatively high trust in the community engagement processes and sufficiently high digital technology literacy among the residents. Thus, maturity levels in cities should be assessed and policies should be developed accordingly to ensure a successful transition toward sustainable smart cities, in light of the findings reported in this study.

Life is a scene and we are the actors: Assessing the usefulness of planning support theatres for smart city planning

There are numerous studies on the identification and explanation of the implementation gap of planning support systems (PSS), that support the smart planning process by analyzing and visualizing data, but which are currently not systematically used in urban planning practice. There is insufficient knowledge about how the PSS implementation gap can be bridged and how PSS can be embedded into the planning process. This study provides insight into how the City Analytics Lab (CAL) can contribute to the promise of smart planning. It aims to first determine how CAL can yield the promise of smart planning. Then it provides insight into how CAL can contribute to close the PSS implementation gap. Accordingly, we organized fifteen workshops and surveyed planning practitioners (N = 89) at CAL to assess its usefulness for smart planning as well as its contribution in bridging the PSS implementation gap. The results show that users are positive towards the usefulness and usability of the support tools in the CAL, in particular the collaborative nature of the lab through its interactive tables and software applications. With regards to the PSS implementation gap, we find that there is a continued and increasingly successful conversation between the CAL and it's PSS tools and planning practice (i.e. ‘technology-to-people’) by collaborating with local governments, industry partners and other universities. However, further refinement of the lab through continued conversation between users and developers (i.e. ‘people-to-people’) is necessary, where tools are codesigned and user preferences and participation protocols are further considered. More broadly this research suggests that Planning Support Theatres, such as CAL can assist in reducing the technology adoption barriers commonly experienced in the application of PSS in practice. Such Planning Support Theatres, also provide a space to support new ideas and interactions importantly between both the key actors and the technology and the actors themselves.

On the design of an urban data and modeling platform and its application to urban district analyses

An integrated urban platform is the essential software infrastructure for smart, sustainable and resilient city planning, operation and maintenance. Today such platforms are mostly designed to handle and analyze large and heterogeneous urban data sets from very different domains. Modeling and optimization functionalities are usually not part of the software concepts. However, such functionalities are considered crucial by the authors to develop transformation scenarios and to optimize smart city operation. An urban platform needs to handle multiple scales in the time and spatial domain, ranging from long term population and land use change to hourly or sub-hourly matching of renewable energy supply and urban energy demand.The paper discusses software architecture concepts for data and modeling urban platforms, which allow to analyze and optimize the urban infrastructure with their energy, water and further resources such as food or goods consumption. Building, commerce and industry as well as the transport sector are in the focus of the efficiency and renewable supply analysis. The main driver is to derive zero carbon strategies for cities while including all major sectors of CO2 generation.So far, two software architecture concepts have been implemented and tested, both using a 3D CityGML geometry data model: A workflow management system for city scale building energy modeling using a monthly energy balance calculation method and a micro service orchestration for dynamic building simulation modeling. The GIS based data analysis methodology and building energy workflow modeling method are applied to a case study district in New York City to demonstrate the implementation status and derive CO2 mitigation strategies. The results showed how data gathered from different sources for the relevant sectors can be translated into CO2-emissions. For a district in Brooklyn connected to a large electric substation with electricity monitoring data available, every individual building´s heating and cooling demand was simulated and resulted in a total annual heating demand of 1.72 TWh/a and 0.11 TWh/a of cooling. It could be shown that the building sector cooling demand can by reduced by 63% by a change of set point temperature for cooling and reduced infiltration rate, while reducing the heat demand by 12%. In addition, carbon accounting of the food and wastewater sector was done using the same GIS based modeling framework. The analysis showed that the food related electricity consumption for refrigeration corresponds to about 5% of the overall electricity consumption and requires 2.6 GWh/a for transport. Using food waste in co-digestion of wastewater treatment plants could contribute another 7.4 GWh/a of combined heat and power. Such quantification of demand and energy sources could contribute to prioritize actions for CO2 mitigation strategies in urban areas.

Dynamic Online 3D Visualization Framework for Real-Time Energy Simulation Based on 3D Tiles

Energy co-simulation can be used to analyze the dynamic energy consumption of a building or a region, which is essential for decision making in the planning and management of smart cities. To increase the accessibility of energy simulation results, a dynamic online 3D city model visualization framework based on 3D Tiles is proposed in this paper. Two types of styling methods are studied, attribute-based and ID map-based. We first perform the energy co-simulation and save the results in CityGML format with EnergyADE. Then the 3D geometry data of these city objects are combined with its simulation results as attributes or just with object ID information to generate Batched 3D Models (B3DM) in 3D Tiles. Next, styling strategies are pre-defined and can be selected by end-users to show different scenarios. Finally, during the visualization process, dynamic interactions and data sources are integrated into the styling generation to support real-time visualization. This framework is implemented with Cesium. Compared with existing dynamic online 3D visualization framework such as directly styling or Cesium Language (CZML), a JSON format for describing a time-dynamic graphical scene, primarily for display in a web browser running Cesium, the proposed framework is more flexible and has higher performance in both data transmission and rendering which is essential for real-time GIS applications.

Necessity of the Needs Map in the Service Design for Smart Cities.

Recently, the issue of how to involve actual service users in the service designs for Smart cities has grown in importance, as this is a key factor in determining the serviceability and sustainability of developed services. Reckless participation of users can make the service design process inefficient and cause a cost increase. So, in this study, we propose the needs map which can be applied to various stages of service development and help service designers to make efficient and reasonable decisions without being too time-consuming. The proposed needs map is a schematization of theoretical concepts for Smart city planning in accordance with the framework of service-technology-infrastructure-management (STIM). Through the needs map, various urban problems, statistical data, and users' needs in a smart city are classified and analyzed concept by concept, and these focused analysis results suggest a proper direction to researchers in the service development plan. To demonstrate experimentally the feasibility of the needs map, we applied it in practice to a service design for the Smart city experience zone located in Daejeon, South Korea.

Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing

This study pursues optima modification of heating, ventilating, and air conditioning (HVAC) systems embedded in residential buildings through predicting heating load (HL) and cooling load (CL). This purpose is carried out by employing four wise metaheuristic algorithms, namely wind-driven optimization (WDO), whale optimization algorithm (WOA), spotted hyena optimization (SHO), and salp swarm algorithm (SSA) synthesized with a multi-layer perceptron (MLP) neural work in order to overcome the computational shortcomings of this model. The used dataset consists of overall height, glazing area, orientation, relative compactness, wall area, glazing area distribution, roof area, and surface area as independent factors, and the HL and CL as target factors. The results indicated a high capability of all four metaheuristic ensembles for understanding the non-linear relationship between the mentioned factors. Meanwhile, a comparison between the used models revealed that SSA-MLP (ErrorHL = 1.9178 and ErrorCL = 2.1830) is the most accurate model, followed by WDO-MLP (ErrorHL = 1.9863 and ErrorCL = 2.2424), WOA-MLP (ErrorHL = 2.1921 and ErrorCL = 2.5390), and SHO-MLP (ErrorHL = 3.1092 and ErrorCL = 4.5930). Regarding the satisfying accuracy of the SSA-based ensemble, it can be a reliable tool for estimating the HL and CL for future smart city planning.

3D-4D visualisation of IoT data from Singapore’s National Science Experiment

ABSTRACT This paper introduces Singapore Views as a novel 3D-4D interface to visualize large space-time datasets with applications in smart cities. The engine enables a combination of geographic information system, geo-located data with high-resolution 3D models and multimedia. Space-time datasets from Singapore’s National Science Experiment (NSE) 2016 are used to test the functionality of Singapore Views. The findings from the user study revealed the new visualization engine was helpful in extracting insights from space-time data. Finally, this study also contributes to the current discussion of how to visualize knowledge thereby support the planning of smart cities.

Cross‐layer routing protocol for event‐driven M2M communication in IoT‐assisted Smart City Planning and Management: CWSN‐eSCPM

The robustness of wireless sensor networks (WSNs) has broadened the use of wireless communication systems for modern machine-to-machine (M2M) communication. Internet-of-Things (IoT) being one of the most explored technologies has gained wide attention due to low-cost WSN communication systems. IoT is one of the dominant technologies used in Smart City implementation demands better communication paradigm including mobility assisted transmission paradigms; however, native IEEE 802.15.4 standard does not have mobility provision that limits its use for real-time applications. On contrary, implementing mobility with classical WSN might impose significantly great topological changes and node or network condition variations which cannot be dealt with the traditional reactive routing approach. Incorporating node and/or network awareness with proactive network management can enable classical WSN to support mobility, which can be vital for IoT based Smart City Planning and Management (SCPM). With this motivation, in this study, a robust Cross-Layer Architecture based WSN Routing Protocol for Event-Driven M2M Communication in SCPM (CWSN-eSCPM) is developed. CWSN-eSCPM encompasses Proactive Node Management Strategy, Data-Centric Service Differentiation and Fair Resource Scheduling (DCSDFRS), Packet Velocity Estimation, Cumulative Congestion Estimation, Dynamic Link Assessment that enables optimal Best Forwarding Node selection for deadline sensitive and reliable data communication. CWSN-eSCPM applies dynamic link quality, cumulative congestion degree, and packet velocity of a node to enable optimal routing decision. DCSDFRS enables optimal resource provision to the real-time data while assuring maximum possible resource for non-real-time data that assures Quality of Service provision for event-driven critical communication in SCPM. Noticeably, CWSN-eSCPM protocol has been applied on top of IEEE 802.15.4 protocol standard, while preserving backward compatibility feature, it can be used for WSN assisted communication purposes. The authors' proposed protocol has performed better in terms of packet delivery ratio, packet loss ratio and deadline miss ratio for both real-time data as well as non-real-time data.

Can Smart City Development Promote Residents’ Emotional Well-Being? Evidence From China

The rapid development of smart cities has raised concerns about residents' emotional well-being (EWB). To enhance EWB when developing smart cities, policy-makers should take residents' perceptions and attitudes into account. How can residents' perceptions about smart city development help promote their EWB? In this paper, we constructed a comprehensive model to explore the effect mechanism of perceived smart city developments on residents' EWB by considering the factors of stress, convenience, and life satisfaction (LS). Using a sample of 428 urban residents in a Chinese smart city, the paper verified the conceptual model by using a structural equation model (SEM) analysis. The results showed that: (1) perceived smart public service (PSPS) can increase EWB by reducing stress; it can also increase EWB by the mechanism of improved LS, brought about by this reduced stress. Perceived smart infrastructure (PSI) will reduce EWB by increasing stress; it can also decrease EWB by the mechanism of reduced LS, brought about by increased stress. (2) Perceived smart public administration (PSPA), PSPS, PSI, and perceived smart environmental protection (PSEP) can enhance EWB via the mechanism of increasing convenience and thereby improving LS. (3) Finally, the statistically insignificant relationship between convenience and LS may be due to the interaction effect of stress and convenience, because the interaction term between convenience and stress has a significant effect on LS, resulting in four insignificant indirect effects involving PSPA, PSPS, PSI, PSEP and EWB vis-a-vis convenience. Overall, this paper contributes to cross-disciplinary research on residents' perceptions with respect to smart city development, and provides guidelines for governmental policy-making in the planning of smart cities.

SafeCity: Toward Safe and Secured Data Management Design for IoT-Enabled Smart City Planning

The interaction among different Internet of Things (IoT) sensors and devices become massive and insecure over the Internet as we probe to smart cities. These heterogeneous devices produce an enormous amount of data that is vulnerable to various malicious threats. The generated data need to be processed and analyzed in a secure fashion to make smart decisions. The smart urban planning is becoming a reality through the mass information generated by the Internet of Things (IoT). This paper exhibits a novel architecture, SafeCity, that limelight the ecosystem of smart cities consists of cameras, sensors, and other real-world physical devices. SafeCity is a three-layer architecture, i.e., a data security layer, a data computational layer, and a decision-making layer. At the first layer, payload-based symmetric encryption is used to secure the data from intruders by exchanging only the authentic data among the physical devices. The second layer is used for the computation of secured data. Finally, the third layer extracts visions from data. The secured exchange of data is ensured by using Raspberry Pi boards while the computation of data is tested on trustworthy datasets, using the Hadoop platform. The assessments disclose that SafeCity presents precious insights into a secured smart city in the context of sensors based IoT environment.