The increasing demand for airline services amidst limited resources results in passenger dissatisfaction and dwindling revenue for airports and airlines. The dynamics of service demand and resource supply results in c...The increasing demand for airline services amidst limited resources results in passenger dissatisfaction and dwindling revenue for airports and airlines. The dynamics of service demand and resource supply results in check-in issues for the stakeholders in the commercial aviation industry. This has the effect of impacting negatively on service performance, cost of operations, customer satisfaction, and overall throughput. Hence, this paper modelled the travelers’ check-in process at the “D Wing” of the Departure Section of Murtala Muhammed International Airport (MMIA), Ikeja, using Arena Software Tool. The work was carried out by determining the parameters of the queues at designated service points in the check-in process. The primary data required to develop the model were acquired by direct observation of passenger flow and oral interview. Thus, the average check-in time was determined. Thereafter, a model of the international check-in system of the MMIA was developed using Arena software in combination with Microsoft Office tools. The data collected were therefore inputted into the model and simulated;the real result was compared with the simulation result of 133 completions and there was no significant difference. The result showed that the model is a representation of the real system under study. Further work will be tailored towards simulation (<em>i.e</em>. the model will be subjected to experimentation in order to have different scenario).展开更多
With the current situation of insecurity in Nigeria and the worsening condition of Nigerian roads, there is increasing demand for air travel. This increasing demand for airline services amidst limited resources result...With the current situation of insecurity in Nigeria and the worsening condition of Nigerian roads, there is increasing demand for air travel. This increasing demand for airline services amidst limited resources results in passenger dissatisfaction and reduction of revenue for airports and airlines. The dynamics of service demand and resource supply result in check-in issues for all the stakeholders in the commercial aviation industry. Hence, this research simulated the developed model for travelers’ check-in process at the “D Wing” of the Departure Section of Murtala Muhammed International Airport (MMIA), Ikeja, using Arena Software Tool. The simulation of the developed model was carried out by varying five different configurations of the servers with respect to the baggage weighing machines and passenger profiling devices to obtain the minimum average check-in time (ACT) for the process, with each configuration undergoing 133 completions of simulation runs. The result of the experimentation revealed that the 3 × 3 × 4 configuration of servers produced the smallest ACT of 18.25 minutes. This translates to a difference of about 58 minutes from the 76.16 minutes ACT of the real system;representing about 76% improvement in the check-in time of passengers. This study contributed to knowledge by revealing that the main choke points in the MMIA check-in system occur at the baggage weighing and passenger profiling areas as against the check-in counter sub-section as advanced by previous works. Furthermore, the research added value to knowledge by creating a balance between customer satisfaction and cost of operations thereby accommodating the interests of the passenger and the operator who are the two main stakeholders in the commercial aviation industry.展开更多
This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China....This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas.We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.展开更多
With the rapid development of location-based networks, point-of-interest(POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o...With the rapid development of location-based networks, point-of-interest(POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity(GTSCP) to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
For any hotel guest reception technology is an important factor in the quality service process,financial-economic efficiency,and management of the hotel industry.The service process in the same type hotel is standard,...For any hotel guest reception technology is an important factor in the quality service process,financial-economic efficiency,and management of the hotel industry.The service process in the same type hotel is standard,meanwhile the service process technology undergoes certain alterations according to the hotel’s size,structure,category,and market segment orientation.Hotel customer service technology is characterized by cycling phases—consistent repeat of the service process before the departure of the guest from the hotel.The article discusses the four-phase cycle of guest reception,related to the hotel services and transactions.Each phase of the cycle consists of standard transactions/phases,which are carried out between hotel clients and the hotel itself.展开更多
Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks.Most existing studies focused only on the popularity of trips.In this paper,we consider furt...Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks.Most existing studies focused only on the popularity of trips.In this paper,we consider further the usability of trip recommendation results through spatial diversification.We thereby formulate a new type of queries named spatial diversified top-κroutes(SDκR)query.This type of queries finds k trip routes with the highest popularity,each of which starts at a given starting point,consumes travel time within a given time budget,and passes through points of interest(POIs)of given categories.Any two trip routes returned are diversified to a certain degree defined by the spatial distance between the two routes.We show that the SDkR problem is NP-hard.We propose two precise algorithms to solve the problem.The first algorithm starts with identifying all candidate routes that satisfy the query constraints,and then searches for theκ-route combination with the highest popularity.The second algorithm identifies the candidate routes and builds up the optimalκ-route combination progressively at the same time.Further,we propose an approximate algorithm to obtain even higher query efficiency with precision bounds.We demonstrate the effectiveness and efficiency of the proposed algorithms on real datasets.Our experimental results show that our algorithms find popular routes with diversified POI locations.Our approximate algorithm saves up to 90%of query time compared with the baseline algorithms.展开更多
Linking user accounts belonging to the same user across different platforms with location data has received significant attention,due to the popularization of GPS-enabled devices and the wide range of applications ben...Linking user accounts belonging to the same user across different platforms with location data has received significant attention,due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage(e.g.,cross-platform user profiling and recommendation).Different from most existing studies which only focus on user account linkage across two platforms,we propose a novel model ULMP(i.e.,user account linkage across multiple platforms),with the goal of effectively and efficiently linking user accounts across multiple platforms with location data.Despite of the practical significance brought by successful user linkage across multiple platforms,this task is very challenging compared with the ones across two platforms.The major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of platforms.To tackle the problem,a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures.Then,in the pruned space,a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user accounts.The extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.展开更多
文摘The increasing demand for airline services amidst limited resources results in passenger dissatisfaction and dwindling revenue for airports and airlines. The dynamics of service demand and resource supply results in check-in issues for the stakeholders in the commercial aviation industry. This has the effect of impacting negatively on service performance, cost of operations, customer satisfaction, and overall throughput. Hence, this paper modelled the travelers’ check-in process at the “D Wing” of the Departure Section of Murtala Muhammed International Airport (MMIA), Ikeja, using Arena Software Tool. The work was carried out by determining the parameters of the queues at designated service points in the check-in process. The primary data required to develop the model were acquired by direct observation of passenger flow and oral interview. Thus, the average check-in time was determined. Thereafter, a model of the international check-in system of the MMIA was developed using Arena software in combination with Microsoft Office tools. The data collected were therefore inputted into the model and simulated;the real result was compared with the simulation result of 133 completions and there was no significant difference. The result showed that the model is a representation of the real system under study. Further work will be tailored towards simulation (<em>i.e</em>. the model will be subjected to experimentation in order to have different scenario).
文摘With the current situation of insecurity in Nigeria and the worsening condition of Nigerian roads, there is increasing demand for air travel. This increasing demand for airline services amidst limited resources results in passenger dissatisfaction and reduction of revenue for airports and airlines. The dynamics of service demand and resource supply result in check-in issues for all the stakeholders in the commercial aviation industry. Hence, this research simulated the developed model for travelers’ check-in process at the “D Wing” of the Departure Section of Murtala Muhammed International Airport (MMIA), Ikeja, using Arena Software Tool. The simulation of the developed model was carried out by varying five different configurations of the servers with respect to the baggage weighing machines and passenger profiling devices to obtain the minimum average check-in time (ACT) for the process, with each configuration undergoing 133 completions of simulation runs. The result of the experimentation revealed that the 3 × 3 × 4 configuration of servers produced the smallest ACT of 18.25 minutes. This translates to a difference of about 58 minutes from the 76.16 minutes ACT of the real system;representing about 76% improvement in the check-in time of passengers. This study contributed to knowledge by revealing that the main choke points in the MMIA check-in system occur at the baggage weighing and passenger profiling areas as against the check-in counter sub-section as advanced by previous works. Furthermore, the research added value to knowledge by creating a balance between customer satisfaction and cost of operations thereby accommodating the interests of the passenger and the operator who are the two main stakeholders in the commercial aviation industry.
基金the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services%sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry(grant number 50-20150618)%National Natural Science Foundation of China (grant numbers 41001220, 51378512, 41571397, and 41501442)This work was also supported by the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund
文摘This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas.We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘With the rapid development of location-based networks, point-of-interest(POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity(GTSCP) to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
文摘For any hotel guest reception technology is an important factor in the quality service process,financial-economic efficiency,and management of the hotel industry.The service process in the same type hotel is standard,meanwhile the service process technology undergoes certain alterations according to the hotel’s size,structure,category,and market segment orientation.Hotel customer service technology is characterized by cycling phases—consistent repeat of the service process before the departure of the guest from the hotel.The article discusses the four-phase cycle of guest reception,related to the hotel services and transactions.Each phase of the cycle consists of standard transactions/phases,which are carried out between hotel clients and the hotel itself.
基金the National Key Research and Development Program of China under Grant No.2018YFB1003404the National Natural Science Foundation of China under Grant Nos.61872070,U1435216,U1811261,and 61602103the Fundamental Research Funds for the Central Universities of China under Grant No.N171605001.
文摘Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks.Most existing studies focused only on the popularity of trips.In this paper,we consider further the usability of trip recommendation results through spatial diversification.We thereby formulate a new type of queries named spatial diversified top-κroutes(SDκR)query.This type of queries finds k trip routes with the highest popularity,each of which starts at a given starting point,consumes travel time within a given time budget,and passes through points of interest(POIs)of given categories.Any two trip routes returned are diversified to a certain degree defined by the spatial distance between the two routes.We show that the SDkR problem is NP-hard.We propose two precise algorithms to solve the problem.The first algorithm starts with identifying all candidate routes that satisfy the query constraints,and then searches for theκ-route combination with the highest popularity.The second algorithm identifies the candidate routes and builds up the optimalκ-route combination progressively at the same time.Further,we propose an approximate algorithm to obtain even higher query efficiency with precision bounds.We demonstrate the effectiveness and efficiency of the proposed algorithms on real datasets.Our experimental results show that our algorithms find popular routes with diversified POI locations.Our approximate algorithm saves up to 90%of query time compared with the baseline algorithms.
基金supported by Australian Research Council under Grant No.DP190101985the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant Nos.19KJA610002 and 19KJB520050the National Natural Science Foundation of China under Grant No.61902270.
文摘Linking user accounts belonging to the same user across different platforms with location data has received significant attention,due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage(e.g.,cross-platform user profiling and recommendation).Different from most existing studies which only focus on user account linkage across two platforms,we propose a novel model ULMP(i.e.,user account linkage across multiple platforms),with the goal of effectively and efficiently linking user accounts across multiple platforms with location data.Despite of the practical significance brought by successful user linkage across multiple platforms,this task is very challenging compared with the ones across two platforms.The major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of platforms.To tackle the problem,a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures.Then,in the pruned space,a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user accounts.The extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.