Taiwan Taoyuan international airport has been devoted to becoming a green airport in recent years, aiming to reduce carbon dioxide emissions (CDEs) from ground handling services. Refuelling operations play a pivotal role in airport operations and, given the short and frequent nature of flight takeoffs and landings, demand substantial work. Different aircraft types and flight lengths require varying refuelling operations, and the inefficient and unrealistic dispatch of aviation-fuel vehicles can lead to flight delays. In this study, time–space network flow techniques were used to formalise the flow of aviation-fuel vehicles in both space and time. Based on the problem characteristics and by adding appropriate constraints, the aim of this work was to minimise CDEs and construct an optimal model for the refuelling operation scheduling of aviation-fuel vehicles. The proposed model was evaluated through a case study, and conclusions and recommendations are presented.
{"title":"The optimal dispatch model for aviation fuel vehicles in refueling operations","authors":"Chun-Ying Chen, I-Ren Huang","doi":"10.1680/jtran.23.00041","DOIUrl":"https://doi.org/10.1680/jtran.23.00041","url":null,"abstract":"Taiwan Taoyuan international airport has been devoted to becoming a green airport in recent years, aiming to reduce carbon dioxide emissions (CDEs) from ground handling services. Refuelling operations play a pivotal role in airport operations and, given the short and frequent nature of flight takeoffs and landings, demand substantial work. Different aircraft types and flight lengths require varying refuelling operations, and the inefficient and unrealistic dispatch of aviation-fuel vehicles can lead to flight delays. In this study, time–space network flow techniques were used to formalise the flow of aviation-fuel vehicles in both space and time. Based on the problem characteristics and by adding appropriate constraints, the aim of this work was to minimise CDEs and construct an optimal model for the refuelling operation scheduling of aviation-fuel vehicles. The proposed model was evaluated through a case study, and conclusions and recommendations are presented.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135585231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ferry services that connect a huge number of islands and mainlands are vital transportation methods in several nations. However, a major disadvantage of ferry services is that they are crucially affected by weather conditions. Informing customers about regular ferry service operations is thus very important. With this in mind, the aim of this study was to predict whether ferry services can be provided in a timely manner through machine learning approaches with meteorological (6–48 h prior) and operation data sets. It was found that the random forest classifier achieved accuracy levels of 90.50% (6 h prior) and 88.78% (48 h prior) in predicting ferry services, which were greater than regulation-oriented determination. Both implications and limitations are presented based on the findings of this study.
{"title":"A machine learning approach to predict timely ferry services using integrated meteorological datasets","authors":"Seongkyu Ko, Junyeop Cha, Eunil Park","doi":"10.1680/jtran.23.00054","DOIUrl":"https://doi.org/10.1680/jtran.23.00054","url":null,"abstract":"Ferry services that connect a huge number of islands and mainlands are vital transportation methods in several nations. However, a major disadvantage of ferry services is that they are crucially affected by weather conditions. Informing customers about regular ferry service operations is thus very important. With this in mind, the aim of this study was to predict whether ferry services can be provided in a timely manner through machine learning approaches with meteorological (6–48 h prior) and operation data sets. It was found that the random forest classifier achieved accuracy levels of 90.50% (6 h prior) and 88.78% (48 h prior) in predicting ferry services, which were greater than regulation-oriented determination. Both implications and limitations are presented based on the findings of this study.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"180 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The beam structure is the main load-bearing structure of engineering projects. High-order shear beams are widely used in engineering. Therefore, damage identification of beam structures is important to guarantee project quality and life safety. To identify the location and depth of cracks in a beam structure, a genetic algorithm (GA) and a damage identification model are combined. This method optimises the back-propagation neural network by using the ability of the GA to find the global optimal solution. The natural frequency (NF) of the cracked beam is obtained through finite-element analysis, and the NF is taken as the input of the model, and the crack location and depth are taken as the outputs of the model. In the experiment, it is found through regression analysis that the predicted output value of the model has a high coincidence with the real value, and its regression coefficient reaches 0.99842. Through an example analysis, the sum of squares of the prediction error of the model is 5.6. The average relative errors of the beam crack location and crack depth are 0.54 and 4.15%, respectively. The experimental results show that the proposed model has a high prediction accuracy and can accurately identify damage to the beam structure.
{"title":"Structural damage identification of high-order shear beams based on genetic algorithm","authors":"Peng Yao, Mengyang Lu","doi":"10.1680/jsmic.23.00011","DOIUrl":"https://doi.org/10.1680/jsmic.23.00011","url":null,"abstract":"The beam structure is the main load-bearing structure of engineering projects. High-order shear beams are widely used in engineering. Therefore, damage identification of beam structures is important to guarantee project quality and life safety. To identify the location and depth of cracks in a beam structure, a genetic algorithm (GA) and a damage identification model are combined. This method optimises the back-propagation neural network by using the ability of the GA to find the global optimal solution. The natural frequency (NF) of the cracked beam is obtained through finite-element analysis, and the NF is taken as the input of the model, and the crack location and depth are taken as the outputs of the model. In the experiment, it is found through regression analysis that the predicted output value of the model has a high coincidence with the real value, and its regression coefficient reaches 0.99842. Through an example analysis, the sum of squares of the prediction error of the model is 5.6. The average relative errors of the beam crack location and crack depth are 0.54 and 4.15%, respectively. The experimental results show that the proposed model has a high prediction accuracy and can accurately identify damage to the beam structure.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sustainable Development Goal 8 (SDG 8) Decent Work and Economic Growth is one of the 17 United Nations SDGs, focusing on the high-quality development of the tourism economy. This study addresses the development of the tourism economy in the Yellow River Basin by emphasizing the importance of a high-quality, comprehensive transportation system. This study aimed to analyze the characteristics of the coupling and dynamic interactions between the two systems at the macro and micro levels using the data fitting method, coupling model, and Haken model together. The results revealed the temporal trends and spatial heterogeneity of the comprehensive transportation and tourism economic systems. The comprehensive development score of the tourism economic system indicated a nearly linear trend. In most provinces, the development of a comprehensive transportation system lagged behind that of the tourism economy. Also, the coupling coordination degree (CCD) for all provinces ranged between 0.1 and 0.3, indicating a stage of imbalance and recession. The CCD of the middle and lower reaches indicated a slight increasing trend, and the CCD of the upper reaches exhibited a slight decreasing trend in the Yellow River Basin. Furthermore, the dynamic interactions between the two systems were unveiled. The average synergy level between the two systems was within the middle stage. In the synergy development process, the tourism economic system was an order parameter that controlled the path and direction of the coordinated evolution of the two systems for the nine provinces and three reaches, respectively. The evolution of both the comprehensive transportation system and the tourism economic system contributed to negative feedback on the tourism and transport system, ultimately enhancing the system order. In conclusion, this study offers insights and recommendations for policymakers on promoting the coordinated evolution of the comprehensive transportation and tourism economy for sustainable development.
{"title":"Revealing evolution of comprehensive transportation and tourism economic systems: evidence from the Yellow River Basin in China","authors":"Wei Wang, Lei Guan, Liang Gong, Hailun Han","doi":"10.1680/jtran.23.00072","DOIUrl":"https://doi.org/10.1680/jtran.23.00072","url":null,"abstract":"Sustainable Development Goal 8 (SDG 8) Decent Work and Economic Growth is one of the 17 United Nations SDGs, focusing on the high-quality development of the tourism economy. This study addresses the development of the tourism economy in the Yellow River Basin by emphasizing the importance of a high-quality, comprehensive transportation system. This study aimed to analyze the characteristics of the coupling and dynamic interactions between the two systems at the macro and micro levels using the data fitting method, coupling model, and Haken model together. The results revealed the temporal trends and spatial heterogeneity of the comprehensive transportation and tourism economic systems. The comprehensive development score of the tourism economic system indicated a nearly linear trend. In most provinces, the development of a comprehensive transportation system lagged behind that of the tourism economy. Also, the coupling coordination degree (CCD) for all provinces ranged between 0.1 and 0.3, indicating a stage of imbalance and recession. The CCD of the middle and lower reaches indicated a slight increasing trend, and the CCD of the upper reaches exhibited a slight decreasing trend in the Yellow River Basin. Furthermore, the dynamic interactions between the two systems were unveiled. The average synergy level between the two systems was within the middle stage. In the synergy development process, the tourism economic system was an order parameter that controlled the path and direction of the coordinated evolution of the two systems for the nine provinces and three reaches, respectively. The evolution of both the comprehensive transportation system and the tourism economic system contributed to negative feedback on the tourism and transport system, ultimately enhancing the system order. In conclusion, this study offers insights and recommendations for policymakers on promoting the coordinated evolution of the comprehensive transportation and tourism economy for sustainable development.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135274185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fog is a weather condition which significantly threaten expressway safety and efficiency by reducing natural visibility and road friction coefficient. Variable speed limit (VSL) strategy is an effective traffic management method for harmonizing vehicle speed and reducing rear-end crash potential under foggy conditions. As two key inputs, driver sight distance and road friction coefficient determine the reliability of VSL calculation. However, driver sight distance in the running vehicle (dynamic sight distance) is significantly lower than that at rest. Additionally, the dust-water coupling film on the expressway surface will also lead to the dynamic friction coefficient. In this paper, based on stopping sight model, a new VSL strategy is proposed, which considers dynamic sight distance, dynamic friction coefficient, alignment, and slope. The results obtained from simulation based on CarSim and Simulink represent that under the foggy conditions with natural visibility is 200 m, the speeds calculated by proposed VSL strategy in straight and curve road segments are 70km/h and 67km/h, 16% and 11% higher than China's legal speed limit respectively. Braking experiment on Wentai Expressway confirm those speeds are safe enough and can improve efficiency. The research results can provide theoretical basis and support for management of expressway under foggy conditions.
{"title":"Variable speed limit strategy of expressway under foggy conditions considering dynamic sight distance and dynamic friction coefficient","authors":"Qiu Hao, Zhu Wenfeng, Wang Jiaheng, Zeng Zhixuan","doi":"10.1680/jtran.23.00065","DOIUrl":"https://doi.org/10.1680/jtran.23.00065","url":null,"abstract":"Fog is a weather condition which significantly threaten expressway safety and efficiency by reducing natural visibility and road friction coefficient. Variable speed limit (VSL) strategy is an effective traffic management method for harmonizing vehicle speed and reducing rear-end crash potential under foggy conditions. As two key inputs, driver sight distance and road friction coefficient determine the reliability of VSL calculation. However, driver sight distance in the running vehicle (dynamic sight distance) is significantly lower than that at rest. Additionally, the dust-water coupling film on the expressway surface will also lead to the dynamic friction coefficient. In this paper, based on stopping sight model, a new VSL strategy is proposed, which considers dynamic sight distance, dynamic friction coefficient, alignment, and slope. The results obtained from simulation based on CarSim and Simulink represent that under the foggy conditions with natural visibility is 200 m, the speeds calculated by proposed VSL strategy in straight and curve road segments are 70km/h and 67km/h, 16% and 11% higher than China's legal speed limit respectively. Braking experiment on Wentai Expressway confirm those speeds are safe enough and can improve efficiency. The research results can provide theoretical basis and support for management of expressway under foggy conditions.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"5 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135274047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study constructs a funnel analysis model by collecting relevant data to achieve fault monitoring. BP neural networks are also used to identify structural damage in construction projects, and GA is used to optimize BP to improve issues such as slow convergence and long time consumption. The results indicate that the difference between third-order frequency and first-order curvature mode is the most suitable indicator for damage warning and identification. The difference in the first-order curvature mode of adjacent measurement points of the damaged component increases with the increase of the degree of damage. Compared with Genetic Algorithm-BP neural network(GA-BP) and BP neural network(BP), the former has a smaller error in identification and better performance. The maximum and minimum relative errors of GA-BP in identifying the damage degree of the structure are 8.06% and 1.61%, meeting the accuracy requirements of the project. The identification of key factors in construction projects based on the funnel analysis model is beneficial for identifying structural damage and ensuring the safety of engineering projects.
{"title":"Application analysis of funnel analysis model in key factor identification of construction projects","authors":"Xiaoqing Cai, Liang Kong","doi":"10.1680/jsmic.23.00019","DOIUrl":"https://doi.org/10.1680/jsmic.23.00019","url":null,"abstract":"This study constructs a funnel analysis model by collecting relevant data to achieve fault monitoring. BP neural networks are also used to identify structural damage in construction projects, and GA is used to optimize BP to improve issues such as slow convergence and long time consumption. The results indicate that the difference between third-order frequency and first-order curvature mode is the most suitable indicator for damage warning and identification. The difference in the first-order curvature mode of adjacent measurement points of the damaged component increases with the increase of the degree of damage. Compared with Genetic Algorithm-BP neural network(GA-BP) and BP neural network(BP), the former has a smaller error in identification and better performance. The maximum and minimum relative errors of GA-BP in identifying the damage degree of the structure are 8.06% and 1.61%, meeting the accuracy requirements of the project. The identification of key factors in construction projects based on the funnel analysis model is beneficial for identifying structural damage and ensuring the safety of engineering projects.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"18 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developing a safety evaluation model for construction is of utmost importance due to the increasing prevalence of safety issues on construction sites in a rapidly growing sector. Consequently, this research integrates Clonal Genetic Algorithm (CGA) and Bayesian Network (BN) into the current BIM technology for building construction to establish a comprehensive safety evaluation model for building construction. To develop a framework for assessing building safety, the study initially filters the factors impacting building safety through an advanced evolutionary algorithm. Subsequently, a Bayesian network is employed to understand the structure and parameters of the model. When compared to both the backpropagation neural network (BPNN) model and the genetic algorithm (GA) optimized neural network model, the CGA-BPNN model showed a network training error of approximately 0.09%. Additionally, the target error value was observed to be around 0.02%, and the genetic crossover probability of the CGA-BPPN model amounted to 0.6629. These results indicate small algorithm error and appropriate training time of the model, as well as higher accuracy. The CGA-BPNN model filters the evaluation indexes in the Bayesian network and assigns appropriate weights to accurately assess the safety status of the construction project.
{"title":"Application of BN and genetic algorithm in building construction safety evaluation","authors":"Hongju Hu, Youlin Liao","doi":"10.1680/jsmic.22.00034","DOIUrl":"https://doi.org/10.1680/jsmic.22.00034","url":null,"abstract":"Developing a safety evaluation model for construction is of utmost importance due to the increasing prevalence of safety issues on construction sites in a rapidly growing sector. Consequently, this research integrates Clonal Genetic Algorithm (CGA) and Bayesian Network (BN) into the current BIM technology for building construction to establish a comprehensive safety evaluation model for building construction. To develop a framework for assessing building safety, the study initially filters the factors impacting building safety through an advanced evolutionary algorithm. Subsequently, a Bayesian network is employed to understand the structure and parameters of the model. When compared to both the backpropagation neural network (BPNN) model and the genetic algorithm (GA) optimized neural network model, the CGA-BPNN model showed a network training error of approximately 0.09%. Additionally, the target error value was observed to be around 0.02%, and the genetic crossover probability of the CGA-BPPN model amounted to 0.6629. These results indicate small algorithm error and appropriate training time of the model, as well as higher accuracy. The CGA-BPNN model filters the evaluation indexes in the Bayesian network and assigns appropriate weights to accurately assess the safety status of the construction project.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ampol Karoonsoontawong, Arkar Than Win, Kunnawee Kanitpong, Siradol Siridhara
Shared autonomous vehicles (SAVs) are a promising first-mile mode to access the public transportation. This study focuses on the mode choice analysis assuming that SAVs are introduced in Bangkok, Thailand. Stated preference experiments based on efficient design were conducted. The rescaled logit model was obtained from the nested logit trick on the combined revealed-preference and stated-preference data. The results showed that with the introduction of SAVs, the shares of existing taxi, motorcycle taxi (MC), and light public transit (LPT) modes will decrease by approximately 28.24%, 27.50% and 21.93%, respectively. These indicate that people who are accustomed to using taxi and MC as their first-mile services are respective most and second most likely to change their choices to SAVs. The values of times for income groups were estimated. Service providers should consider reducing travel cost to attract more lower-income users, and reducing travel time to attract more higher-income users. The average elasticities and simulation results imply that the improvement of the travel cost of SAVs should be a priority over travel time in order to effectively attract more individuals to the SAVs mode, and the improvement of travel time of SAVs leads to the most shift from taxi and MC.
{"title":"Mode choice analysis of shared autonomous vehicles as first-mile service in Bangkok, Thailand","authors":"Ampol Karoonsoontawong, Arkar Than Win, Kunnawee Kanitpong, Siradol Siridhara","doi":"10.1680/jtran.23.00052","DOIUrl":"https://doi.org/10.1680/jtran.23.00052","url":null,"abstract":"Shared autonomous vehicles (SAVs) are a promising first-mile mode to access the public transportation. This study focuses on the mode choice analysis assuming that SAVs are introduced in Bangkok, Thailand. Stated preference experiments based on efficient design were conducted. The rescaled logit model was obtained from the nested logit trick on the combined revealed-preference and stated-preference data. The results showed that with the introduction of SAVs, the shares of existing taxi, motorcycle taxi (MC), and light public transit (LPT) modes will decrease by approximately 28.24%, 27.50% and 21.93%, respectively. These indicate that people who are accustomed to using taxi and MC as their first-mile services are respective most and second most likely to change their choices to SAVs. The values of times for income groups were estimated. Service providers should consider reducing travel cost to attract more lower-income users, and reducing travel time to attract more higher-income users. The average elasticities and simulation results imply that the improvement of the travel cost of SAVs should be a priority over travel time in order to effectively attract more individuals to the SAVs mode, and the improvement of travel time of SAVs leads to the most shift from taxi and MC.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is an increased focus on how to improve tourism quality in rural areas. To provide reference and guidance for both tourists and tourism departments, a multi-dimensional tourism suitability model is proposed. Meteorological, travel and other data related to a tourism area were analysed, and a tourism suitability evaluation architecture model was developed. From February to September, when temperature fluctuations were more clearly defined, the prediction accuracy of the model was higher, while the test results of the root mean square error and other indicators of the model in meteorological prediction were good. The model had the highest prediction accuracy of 96.8% under multi-dimensional conditions. The model could provide accurate guidance for tourists to choose travel dates and destinations, further promoting rural tourism.
{"title":"Prediction of rural tourism suitability based on multi-dimensional evaluation model","authors":"Yue Li","doi":"10.1680/jsmic.23.00014","DOIUrl":"https://doi.org/10.1680/jsmic.23.00014","url":null,"abstract":"There is an increased focus on how to improve tourism quality in rural areas. To provide reference and guidance for both tourists and tourism departments, a multi-dimensional tourism suitability model is proposed. Meteorological, travel and other data related to a tourism area were analysed, and a tourism suitability evaluation architecture model was developed. From February to September, when temperature fluctuations were more clearly defined, the prediction accuracy of the model was higher, while the test results of the root mean square error and other indicators of the model in meteorological prediction were good. The model had the highest prediction accuracy of 96.8% under multi-dimensional conditions. The model could provide accurate guidance for tourists to choose travel dates and destinations, further promoting rural tourism.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Improving the efficiency of operations is a major challenge in facility management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in silos that are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails or bureaucratic processes. Digital twins represent an emerging approach to returning awareness and control to facility managers by automating all levels of information access (from granular data to defined key performance indicators and reports) and actuation. This paper proposes a low-latency data integration method that supports actuation and decision making in facility management, including construction, operation and maintenance data, and Internet of things. The method uses federated data models and semantic web ontologies, and it is implemented within a data lake architecture with connections to siloed data to keep the delegation of responsibilities of data owners. A case study in the Alan Reece Building (Cambridge, UK) demonstrates the approach by enabling fault detection and diagnosis of the heating, ventilation and air-conditioning system for facility management.
{"title":"Data Integration for Digital Twins in the built environment based on federated data models","authors":"Jorge Merino, Xiang Xie, Nicola Moretti, Janet Yoon Chang, Ajith Parlikad","doi":"10.1680/jsmic.23.00002","DOIUrl":"https://doi.org/10.1680/jsmic.23.00002","url":null,"abstract":"Improving the efficiency of operations is a major challenge in facility management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in silos that are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails or bureaucratic processes. Digital twins represent an emerging approach to returning awareness and control to facility managers by automating all levels of information access (from granular data to defined key performance indicators and reports) and actuation. This paper proposes a low-latency data integration method that supports actuation and decision making in facility management, including construction, operation and maintenance data, and Internet of things. The method uses federated data models and semantic web ontologies, and it is implemented within a data lake architecture with connections to siloed data to keep the delegation of responsibilities of data owners. A case study in the Alan Reece Building (Cambridge, UK) demonstrates the approach by enabling fault detection and diagnosis of the heating, ventilation and air-conditioning system for facility management.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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