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Spatial Big Data, BIM and advanced GIS for Smart Transformation:...
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Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (15 January 2020) | Viewed by 43583

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Sara Shirowzhan

E-Mail Website
Guest Editor
City Futures Research Centre, School of Built Environment, University of New South Wales, Sydney, Kensington, NSW 2052, Australia
Interests: sensing technologies; AI; machine learning; advanced GIS; BIM; digital twins; city analytics methods; digital construction; smart cities; smart construction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Willie Tan

E-Mail Website
Guest Editor
Department of the Built Environment, College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
Interests: megaprojects; project finance; geomatic engineering; urban management
Special Issues, Collections and Topics in MDPI journals
Dr. Samad Sepasgozar

E-Mail Website
Guest Editor
School of Built Environment, UNSW Sydney, Sydney, NSW 1466, Australia
Interests: sustainability; energy efficiency; artificial intelligence; smart city; digital twin; applications of the internet of things; advanced GIS; LiDAR; BIM; digital technology in infrastructure; mixed reality applications; information and communication technology; spatial analysis and visualization; authentic education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

The United Nations Economic Commission initiated a program investigating the United Smart Cities (see https://sustainabledevelopment.un.org/partnership/?p=10009 ). There are many other initiatives of smart cities across the world. However, the main requirement of a successful smart city in the implementation phase depends on the availability of the appropriate digital information systems. This special issue invites all researchers to share their scholarly work concerning the development of advanced technologies which may help the implementation of smart cities.

One of the key messages of the Habitat III regional report on housing and urban development is that the digital revolution brings “massive opportunities and challenges to cities.” (see http://radar.gsa.ac.uk/5268/ ) Since the technology is rapidly advancing, the applications are not fully identified, and all challenges are not accurately addressed. The Special Issue welcomes all technical endeavours, technology case studies, and experimentations related to Geospatial Information Systems and other compatible technologies carried out to address one of the many challenges encountered by smart cities, smart construction, infrastructure maintenance, and disaster management.

The International Telecommunication Union (ITU) as the United Nations specialized agency in the field of telecommunications, information, and communication technologies (ICTs) defines a “smart city” as

"A smart sustainable city is an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operation and services, and competitiveness…".

This Special Issue invites all researchers to share their scholarly work concerning the development of advanced technologies that may facilitate the implementation of smart cities. It will cover topics such as:

  • Use of Geo-ICT for planning smart cities
  • Geospatial data acquisition for smart cities
  • Geospatial database management
  • Big data analytics for smart cities
  • Real-time location intelligence
  • Use of geospatial data for smart urban management, particularly infrastructure planning, construction, and maintenance
  • Real-time monitoring of urban environment including air, water, and noise
  • Use of geospatial data for planning and building resilient cities, including security and disaster responses.

Dr. Sara Shirowzhan
Prof. Willie Tan
Dr. Samad M. E. Sepasgozar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cloud based GIS
  • BIM
  • big data GIS applications
  • Lidar
  • geospatial artificial intelligence
  • deep and machine learning
  • spatiotemporal data analysis
  • imaging and scanning data in GIS
  • location tracking
  • global positioning system
  • smart city, infrastructure and construction
  • disaster management
  • building Information systems
  • emergency responses

Published Papers (9 papers)

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Editorial

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11 pages, 2418 KiB  
Editorial
Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities
by Sara Shirowzhan, Willie Tan and Samad M. E. Sepasgozar
ISPRS Int. J. Geo-Inf. 2020, 9(4), 240; https://doi.org/10.3390/ijgi9040240 - 12 Apr 2020
Cited by 90 | Viewed by 11256
Abstract
Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the [...] Read more.
Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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Research

Jump to: Editorial

23 pages, 10245 KiB  
Article
An Efficient Staged Evacuation Planning Algorithm Applied to Multi-Exit Buildings
by Litao Han, Huan Guo, Haisi Zhang, Qiaoli Kong, Aiguo Zhang and Cheng Gong
ISPRS Int. J. Geo-Inf. 2020, 9(1), 46; https://doi.org/10.3390/ijgi9010046 - 15 Jan 2020
Cited by 17 | Viewed by 4049
Abstract
When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this [...] Read more.
When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this paper, a novel evacuation planning algorithm applied to multi-exit buildings is proposed, which is based on an indoor route network model. Firstly, evacuees are grouped by their location proximity, then all groups are approximately equally classified into several evacuation zones, each of which has only one safe exit. After that, all evacuation groups in the same zone are sorted by their shortest path length, then the time window of each evacuation group occupying the safe exit is calculated in turn. In the case of congestion at the safe exit, the departure time of each evacuation group is delayed in its arrival order. The objectives of the proposed algorithm include minimizing the total evacuation time of all evacuees, the travel time of each evacuee, avoiding traffic congestion, balancing traffic loads among different exits, and achieving high computational efficiency. Case studies are conducted to examine the performance of our algorithm. The influences of group number, group size, evacuation speed on the total evacuation time are discussed on a single-exit network, and that of partitioning methods and evacuation density on the performance and applicability in different congestion levels are also discussed on a multi-exit network. Results demonstrate that our algorithm has a higher efficiency and performs better for evacuations with a large occupant density. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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17 pages, 7618 KiB  
Article
A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe Networks
by Shaohua Wang, Yeran Sun, Yinle Sun, Yong Guan, Zhenhua Feng, Hao Lu, Wenwen Cai and Liang Long
ISPRS Int. J. Geo-Inf. 2019, 8(10), 441; https://doi.org/10.3390/ijgi8100441 - 08 Oct 2019
Cited by 5 | Viewed by 3503
Abstract
Three-dimensional (3D) pipe network modeling plays an essential part in high performance-based smart city applications. Given that massive 3D pipe networks tend to be difficult to manage and to visualize, we propose in this study a hybrid framework for high-performance modeling of a [...] Read more.
Three-dimensional (3D) pipe network modeling plays an essential part in high performance-based smart city applications. Given that massive 3D pipe networks tend to be difficult to manage and to visualize, we propose in this study a hybrid framework for high-performance modeling of a 3D pipe network, including pipe network data model and high-performance modeling. The pipe network data model is devoted to three-dimensional pipe network construction based on network topology and building information models (BIMs). According to the topological relationships of the pipe point pipelines, the pipe network is decomposed into multiple pipe segment units. The high-performance modeling of 3D pipe network contains a spatial 3D model, the instantiation, adaptive rendering, and combination parallel computing. Spatial 3D model (S3M) is proposed for spatial data transmission, exchange, and visualization of massive and multi-source 3D spatial data. The combination parallel computing framework with GPU and OpenMP was developed to reduce the processing time for pipe networks. The results of the experiments showed that the hybrid framework achieves a high efficiency and the hardware resource occupation is reduced. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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21 pages, 6384 KiB  
Article
Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks
by Tianyang Dong, Lulu Yuan, Yuehui Shang, Yang Ye and Ling Zhang
ISPRS Int. J. Geo-Inf. 2019, 8(9), 379; https://doi.org/10.3390/ijgi8090379 - 29 Aug 2019
Cited by 12 | Viewed by 2541
Abstract
Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. The existing [...] Read more.
Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. The existing methods of CKNN queries often recommend K objects to users based on distance, but they do not consider the moving directions of objects in a road network. Although a few CKNN query methods consider the movement directions of moving objects in Euclidean space, no efficient direction determination algorithm has been applied to CKNN queries over data streams in spatial road networks until now. In order to find the top K-nearest objects move towards the query object within a period of time, this paper presents a novel algorithm of direction-aware continuous moving K-nearest neighbor (DACKNN) queries in road networks. In this method, the objects’ azimuth information is adopted to determine the moving direction, ensuring the moving objects in the result set towards the query object. In addition, we evaluate the DACKNN query algorithm via comprehensive tests on the Los Angeles network TIGER/LINE data and compare DACKNN with other existing algorithms. The comparative test results demonstrate that our algorithm can perform the direction-aware CKNN query accurately and efficiently. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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16 pages, 6025 KiB  
Article
The Distribution Pattern of the Railway Network in China at the County Level
by Minmin Li, Renzhong Guo, You Li, Biao He and Yong Fan
ISPRS Int. J. Geo-Inf. 2019, 8(8), 336; https://doi.org/10.3390/ijgi8080336 - 30 Jul 2019
Cited by 7 | Viewed by 4313
Abstract
Evaluation of the railway network distribution and its impacts on social and economic development has great significance for building an efficient and comprehensive railway system. To address the lack of evaluation indicators to assess the railway network distribution pattern at the macro scale, [...] Read more.
Evaluation of the railway network distribution and its impacts on social and economic development has great significance for building an efficient and comprehensive railway system. To address the lack of evaluation indicators to assess the railway network distribution pattern at the macro scale, this study selects eight indicators—railway network density, railway network proximity, the shortest travel time, train frequency, population, Gross Domestic Product (GDP), the gross industrial value above designated size, and fixed asset investment—as the basis of an integrated railway network distribution index which is used to characterize China’s railway network distribution using geographical information system (GIS) technology. The research shows that, in 2015, the railway network distribution was low in almost half of China’s counties and that there were obvious differences in distribution between counties in the east and west. In addition, multiple dense areas of railway network distribution were identified. The results suggest that it might be advisable to strengthen the connections between large and small cities in the eastern region and that the major urban agglomerations in the midwest could focus on strengthening the construction of railway facilities to increase the urban vitality of the western region. This study can be used to guide the optimization of railway network structures and provide a macro decision-making reference for the planning and evaluation of major railway projects in China. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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16 pages, 6401 KiB  
Article
Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone
by Federico Rupi, Cristian Poliziani and Joerg Schweizer
ISPRS Int. J. Geo-Inf. 2019, 8(8), 322; https://doi.org/10.3390/ijgi8080322 - 25 Jul 2019
Cited by 35 | Viewed by 4295
Abstract
This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle [...] Read more.
This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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16 pages, 8419 KiB  
Article
An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion
by Hao Wu, Lingbo Liu, Yang Yu, Zhenghong Peng, Hongzan Jiao and Qiang Niu
ISPRS Int. J. Geo-Inf. 2019, 8(7), 313; https://doi.org/10.3390/ijgi8070313 - 23 Jul 2019
Cited by 27 | Viewed by 4455
Abstract
The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, [...] Read more.
The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents’ travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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15 pages, 8484 KiB  
Article
Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China
by Zhihui Gu, Yong Zhu, Yan Zhang, Wanyu Zhou and Yu Chen
ISPRS Int. J. Geo-Inf. 2019, 8(5), 239; https://doi.org/10.3390/ijgi8050239 - 21 May 2019
Cited by 11 | Viewed by 3854
Abstract
Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic [...] Read more.
Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic bike optimization algorithm (HBOA) to determine the optimal supply and distribution of bikes considering the effect of bicycle cycling. In this approach, the different bike trips with separate bikes can be connected in space and time and converted into a continuous trip chain for a single bike. To improve this cycling efficiency, it is important to properly design the bicycle distribution. Taking Shenzhen as an example, we implement the algorithm with OD matrix data from Mobike and Ofo, the two large bike sharing companies which account for 80% of the shared bike market in Shenzhen, over two days. The HBOA results are as follows. 1) Only one-fifth of the bike supply is needed to meet the current usage demand if the bikes are used efficiently, which means a large number of shared bikes in Shenzhen remain in an idle state for long periods. 2) Although the cycling demand is high in many areas, it does not mean that large numbers of bikes are needed because the continuous inflow caused by the cycling effect of bikes will meet most of the demand by itself. 3) The areas with the highest demands for optimal bikes are residential, followed by industrial, public transportation, official and commercial areas, on both working and non-working days. This algorithm can be an objective basis for city related departments to manage station-free BSSs and be applied to design the layout of bikes in small-scale spatial units to help station-free BSSs operate efficiently and minimize the need to relocate the bikes without reducing the level of user satisfaction. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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18 pages, 3121 KiB  
Article
An Occupancy Simulator for a Smart Parking System: Developmental Design and Experimental Considerations
by Germán Martín Mendoza-Silva, Michael Gould, Raul Montoliu, Joaquín Torres-Sospedra and Joaquín Huerta
ISPRS Int. J. Geo-Inf. 2019, 8(5), 212; https://doi.org/10.3390/ijgi8050212 - 07 May 2019
Cited by 8 | Viewed by 4152
Abstract
This paper presents the development of a parking occupancy simulator to support a smart parking system. The simulator uses an agent-based approach to model drivers who follow activity plans and who may or may not use the smart parking system. We illustrate how [...] Read more.
This paper presents the development of a parking occupancy simulator to support a smart parking system. The simulator uses an agent-based approach to model drivers who follow activity plans and who may or may not use the smart parking system. We illustrate how the process of developing our simulator helped in the design and implementation of the smart parking system components. The paper also shows how the simulator was used to study the possible usage of the smart parking system in a university campus, foreseeing (1) support for the smart parking system’s overall suitability, (2) reservation guarantee violation problems, and (3) the value of using total traveled distance as a metric for the smart parking evaluation. The experience presented in this paper may prove valuable to teams planning the development of a smart parking system for similar contexts. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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