Licia Amichi, A. C. Viana, M. Crovella, A. Loureiro
Human mobility literature is limited in their ability to capture the novelty-seeking or the exploratory tendency of individuals. Mainly, the vast majority of mobility prediction models rely uniquely on the history of visited locations (as captured in the input dataset) to predict future visits. This hinders the prediction of new unseen places and reduces prediction accuracy. In this paper, we show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Further, we extract and analyze the mobility traits specific to each profile. We then investigate temporal and spatial patterns in each mobility profile and show the presence of recurrent visiting behavior of individuals even in their novelty-seeking moments. Our results unveil important novelty preferences of people, which are ignored by literature prediction models. Finally, we show that prediction accuracy is dramatically affected by exploration moments of individuals. We then discuss how our profiling methodology could be leveraged to improve prediction.
{"title":"Understanding individuals' proclivity for novelty seeking","authors":"Licia Amichi, A. C. Viana, M. Crovella, A. Loureiro","doi":"10.1145/3397536.3422248","DOIUrl":"https://doi.org/10.1145/3397536.3422248","url":null,"abstract":"Human mobility literature is limited in their ability to capture the novelty-seeking or the exploratory tendency of individuals. Mainly, the vast majority of mobility prediction models rely uniquely on the history of visited locations (as captured in the input dataset) to predict future visits. This hinders the prediction of new unseen places and reduces prediction accuracy. In this paper, we show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Further, we extract and analyze the mobility traits specific to each profile. We then investigate temporal and spatial patterns in each mobility profile and show the presence of recurrent visiting behavior of individuals even in their novelty-seeking moments. Our results unveil important novelty preferences of people, which are ignored by literature prediction models. Finally, we show that prediction accuracy is dramatically affected by exploration moments of individuals. We then discuss how our profiling methodology could be leveraged to improve prediction.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133971151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we propose a novel deep learning framework, called Attention-Based 2-layer Bi-ConvLSTM (denoted as Att-2BiConvLSTM) model, to predict the number of illegal-parking events in urban spaces. We model the research as a "next frame" prediction problem, which aims to improve urban transportation conditions and enhance the security and right-of-way for pedestrians. Various features in the prediction model are considered: some of them (e.g., hourly weather, traffic volumes) are dynamic every hour, while others (e.g., road network, point-of-interests) are static. To boost the effectiveness of static features, we propose a dynamic training process to transform the static features into dynamics. After that, all features can vary with time so that they are capable of handling a real-time prediction scenario. Moreover, we propose an attention mechanism for enhancing our bi-directional ConvLSTM model. With experimental verifications, we find that our proposed Att-2BiConvLSTM model can outperform other state-of-art and baseline methods. Besides, our model is useful for combining all features to make an accurate prediction.
{"title":"Detection of Illegal Parking Events Using Spatial-Temporal Features","authors":"Jiawei Jiang, Yu-Chen Chen, Hsun-Ping Hsieh","doi":"10.1145/3397536.3428350","DOIUrl":"https://doi.org/10.1145/3397536.3428350","url":null,"abstract":"In this work, we propose a novel deep learning framework, called Attention-Based 2-layer Bi-ConvLSTM (denoted as Att-2BiConvLSTM) model, to predict the number of illegal-parking events in urban spaces. We model the research as a \"next frame\" prediction problem, which aims to improve urban transportation conditions and enhance the security and right-of-way for pedestrians. Various features in the prediction model are considered: some of them (e.g., hourly weather, traffic volumes) are dynamic every hour, while others (e.g., road network, point-of-interests) are static. To boost the effectiveness of static features, we propose a dynamic training process to transform the static features into dynamics. After that, all features can vary with time so that they are capable of handling a real-time prediction scenario. Moreover, we propose an attention mechanism for enhancing our bi-directional ConvLSTM model. With experimental verifications, we find that our proposed Att-2BiConvLSTM model can outperform other state-of-art and baseline methods. Besides, our model is useful for combining all features to make an accurate prediction.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132453573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yixin Xu, L. Kulik, Renata Borovica-Gajic, Abdullah AlDwyish, Jianzhong Qi
On-demand ride-sharing services such as Uber and Lyft have gained tremendous popularity over the past decade, largely driven by the omnipresence of mobile devices. Ride-sharing services can provide economic and environmental benefits such as reducing traffic congestion and vehicle emissions. Multi-hop ride-sharing enables passengers to transfer between vehicles within a single trip, which significantly extends the benefits of ride-sharing and provides ride opportunities that are not possible otherwise. Despite its advantages, offering real-time multi-hop ride-sharing services at large scale is a challenging computational task due to the large combination of vehicles and passenger transfer points. To address these challenges, we propose exact and approximation algorithms that are scalable and achieve real-time responses for highly dynamic ride-sharing scenarios in large metropolitan areas. Our experiments on real-world datasets show the benefits of multi-hop ride-sharing services and demonstrate that our proposed algorithms are more than two orders of magnitude faster than the state-of-the-art. Our approximation algorithms offer a comparable trip quality to our exact algorithm, while improving the ride-sharing request matching time by another order of magnitude.
{"title":"Highly Efficient and Scalable Multi-hop Ride-sharing","authors":"Yixin Xu, L. Kulik, Renata Borovica-Gajic, Abdullah AlDwyish, Jianzhong Qi","doi":"10.1145/3397536.3422235","DOIUrl":"https://doi.org/10.1145/3397536.3422235","url":null,"abstract":"On-demand ride-sharing services such as Uber and Lyft have gained tremendous popularity over the past decade, largely driven by the omnipresence of mobile devices. Ride-sharing services can provide economic and environmental benefits such as reducing traffic congestion and vehicle emissions. Multi-hop ride-sharing enables passengers to transfer between vehicles within a single trip, which significantly extends the benefits of ride-sharing and provides ride opportunities that are not possible otherwise. Despite its advantages, offering real-time multi-hop ride-sharing services at large scale is a challenging computational task due to the large combination of vehicles and passenger transfer points. To address these challenges, we propose exact and approximation algorithms that are scalable and achieve real-time responses for highly dynamic ride-sharing scenarios in large metropolitan areas. Our experiments on real-world datasets show the benefits of multi-hop ride-sharing services and demonstrate that our proposed algorithms are more than two orders of magnitude faster than the state-of-the-art. Our approximation algorithms offer a comparable trip quality to our exact algorithm, while improving the ride-sharing request matching time by another order of magnitude.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Han Bao, Xun Zhou, Yingxue Zhang, Yanhua Li, Yiqun Xie
The COVID-19 pandemic has posed grand challenges to policy makers, raising major social conflicts between public health and economic resilience. Policies such as closure or reopen of businesses are made based on scientific projections of infection risks obtained from infection dynamics models. While most parameters in infection dynamics models can be set using domain knowledge of COVID-19, a key parameter - human mobility - is often challenging to estimate due to complex social contexts and limited training data under escalating COVID-19 conditions. To address these challenges, we formulate the problem as a spatio-temporal data generation problem and propose COVID-GAN, a spatio-temporal Conditional Generative Adversarial Network, to estimate mobility (e.g., changes in POI visits) under various real-world conditions (e.g., COVID-19 severity, local policy interventions) integrated from multiple data sources. We also introduce a domain-constraint correction layer in the generator of COVID-GAN to reduce the difficulty of learning. Experiments using urban mobility data derived from cell phone records and census data show that COVID-GAN can well approximate real-world human mobility responses, and that the proposed domain-constraint based correction can greatly improve solution quality.
{"title":"COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks","authors":"Han Bao, Xun Zhou, Yingxue Zhang, Yanhua Li, Yiqun Xie","doi":"10.1145/3397536.3422261","DOIUrl":"https://doi.org/10.1145/3397536.3422261","url":null,"abstract":"The COVID-19 pandemic has posed grand challenges to policy makers, raising major social conflicts between public health and economic resilience. Policies such as closure or reopen of businesses are made based on scientific projections of infection risks obtained from infection dynamics models. While most parameters in infection dynamics models can be set using domain knowledge of COVID-19, a key parameter - human mobility - is often challenging to estimate due to complex social contexts and limited training data under escalating COVID-19 conditions. To address these challenges, we formulate the problem as a spatio-temporal data generation problem and propose COVID-GAN, a spatio-temporal Conditional Generative Adversarial Network, to estimate mobility (e.g., changes in POI visits) under various real-world conditions (e.g., COVID-19 severity, local policy interventions) integrated from multiple data sources. We also introduce a domain-constraint correction layer in the generator of COVID-GAN to reduce the difficulty of learning. Experiments using urban mobility data derived from cell phone records and census data show that COVID-GAN can well approximate real-world human mobility responses, and that the proposed domain-constraint based correction can greatly improve solution quality.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126511927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. V. D. Kerkhof, I. Kostitsyna, M. V. Kreveld, M. Löffler, Tim Ophelders
We investigate a data-driven approach for road network generalization, where the input is a road network and a collection of routes or trajectories on these roads. The aim is to select a subset of the road network in which many routes of the collection are fully preserved. We formulate the problem and present several heuristic versions of it, as the general problem is NP-hard. We show the outcome of the versions on a data set for comparison purposes.
{"title":"Route-preserving Road Network Generalization","authors":"M. V. D. Kerkhof, I. Kostitsyna, M. V. Kreveld, M. Löffler, Tim Ophelders","doi":"10.1145/3397536.3422234","DOIUrl":"https://doi.org/10.1145/3397536.3422234","url":null,"abstract":"We investigate a data-driven approach for road network generalization, where the input is a road network and a collection of routes or trajectories on these roads. The aim is to select a subset of the road network in which many routes of the collection are fully preserved. We formulate the problem and present several heuristic versions of it, as the general problem is NP-hard. We show the outcome of the versions on a data set for comparison purposes.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the last few years, in the field of computer vision, sophisticated deep learning methods have been developed to accomplish semantic segmentation tasks of 3D point cloud data. Additionally, many researchers have extended the applicability of these methods, such as PointNet or PointNet++, beyond semantic segmentation tasks of indoor scene data to large-scale outdoor scene data observed using airborne laser scanning systems equipped with light detection and ranging (LiDAR) technology. Most extant studies have only investigated geometric information (x, y, and z or longitude, latitude, and height) and have omitted rich radiometric information. Therefore, we aim to extend the applicability of deep learning-based model from the geometric data into radiometric data acquired with airborne full-waveform LiDAR without converting the waveform into 2D images or 3D voxels. We simultaneously train two models: a local module for local feature extraction and a global module for acquiring wide receptive fields for the waveform. Furthermore, our proposed model is based on waveform-aware convolutional techniques. We evaluate the effectiveness of the proposed method using benchmark large-scale outdoor scene data. By integrating the two outputs from the local module and the global module, our proposed model had achieved higher mean recall value 0.92 than previous methods and higher F1 scores for all six classes than the other 3D Deep Learning method. Therefore, our proposed network consisting of the local and global module successfully resolves the semantic segmentation task of full-waveform LiDAR data without requiring expert knowledge.
{"title":"Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction","authors":"T. Shinohara, H. Xiu, M. Matsuoka","doi":"10.1145/3397536.3422209","DOIUrl":"https://doi.org/10.1145/3397536.3422209","url":null,"abstract":"During the last few years, in the field of computer vision, sophisticated deep learning methods have been developed to accomplish semantic segmentation tasks of 3D point cloud data. Additionally, many researchers have extended the applicability of these methods, such as PointNet or PointNet++, beyond semantic segmentation tasks of indoor scene data to large-scale outdoor scene data observed using airborne laser scanning systems equipped with light detection and ranging (LiDAR) technology. Most extant studies have only investigated geometric information (x, y, and z or longitude, latitude, and height) and have omitted rich radiometric information. Therefore, we aim to extend the applicability of deep learning-based model from the geometric data into radiometric data acquired with airborne full-waveform LiDAR without converting the waveform into 2D images or 3D voxels. We simultaneously train two models: a local module for local feature extraction and a global module for acquiring wide receptive fields for the waveform. Furthermore, our proposed model is based on waveform-aware convolutional techniques. We evaluate the effectiveness of the proposed method using benchmark large-scale outdoor scene data. By integrating the two outputs from the local module and the global module, our proposed model had achieved higher mean recall value 0.92 than previous methods and higher F1 scores for all six classes than the other 3D Deep Learning method. Therefore, our proposed network consisting of the local and global module successfully resolves the semantic segmentation task of full-waveform LiDAR data without requiring expert knowledge.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130396547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database counterparts. Numerous attempts have been explored to extend learned indexes to the multi-dimensional space. This makes learned indexes potentially suitable for spatial databases. The goal of this tutorial is to provide up-to-date coverage of learned indexes both in the single and multi-dimensional spaces. The tutorial covers over 25 learned indexes. The tutorial navigates through the space of learned indexes through a taxonomy that helps classify the covered learned indexes both in the single and multi-dimensional spaces.
{"title":"A Tutorial on Learned Multi-dimensional Indexes","authors":"Abdullah Al-Mamun, Hao Wu, Walid G. Aref","doi":"10.1145/3397536.3426358","DOIUrl":"https://doi.org/10.1145/3397536.3426358","url":null,"abstract":"Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database counterparts. Numerous attempts have been explored to extend learned indexes to the multi-dimensional space. This makes learned indexes potentially suitable for spatial databases. The goal of this tutorial is to provide up-to-date coverage of learned indexes both in the single and multi-dimensional spaces. The tutorial covers over 25 learned indexes. The tutorial navigates through the space of learned indexes through a taxonomy that helps classify the covered learned indexes both in the single and multi-dimensional spaces.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sameera Kannangara, Hairuo Xie, E. Tanin, A. Harwood, S. Karunasekera
We study the problem of tracking the movement of groups using sparse trajectory data extracted from Location Based Social Networks (LBSNs). Tracking group movement using LBSN data is challenging because the data may contain a large amount of noise due to the lack of stability in group entity, spatial extent and posting time. We propose a first-of-its-kind solution, Group Kalman Filter (GKF), which aims to improve the effectiveness of group tracking by predicting the spatial properties of groups with a group movement model. Our experiments with real LBSN data and synthetic LBSN data show that GKF can detect groups and predict group movement with a high level of accuracy and efficiency.
{"title":"Tracking Group Movement in Location Based Social Networks","authors":"Sameera Kannangara, Hairuo Xie, E. Tanin, A. Harwood, S. Karunasekera","doi":"10.1145/3397536.3422211","DOIUrl":"https://doi.org/10.1145/3397536.3422211","url":null,"abstract":"We study the problem of tracking the movement of groups using sparse trajectory data extracted from Location Based Social Networks (LBSNs). Tracking group movement using LBSN data is challenging because the data may contain a large amount of noise due to the lack of stability in group entity, spatial extent and posting time. We propose a first-of-its-kind solution, Group Kalman Filter (GKF), which aims to improve the effectiveness of group tracking by predicting the spatial properties of groups with a group movement model. Our experiments with real LBSN data and synthetic LBSN data show that GKF can detect groups and predict group movement with a high level of accuracy and efficiency.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115562257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Location services are one of the most used applications today on mobile devices. The vast majority of localization systems propose solutions for locating the user in a 2D single floor environment. However, accurate estimation of the user's floor level, in tall multistory buildings, is a crucial basis for many applications, especially for emergency services. This paper presents a fingerprinting-based system that provides a low-cost floor localization service using the ubiquitous cellular signals received by the user's cell phone. Specifically, a convolutional neural network is trained to map the sequential change of the received cellular signals to the corresponding floor. Evaluation using different Android phones shows that the proposed system can track the user floor with at least 95.9% accuracy in different scenarios. This demonstrates the superiority of the system compared to the state-of-the-art systems in all experiments.
{"title":"Deep Learning-based Floor Prediction Using Cell Network Information","authors":"K. Alkiek, Aya Othman, Hamada Rizk, M. Youssef","doi":"10.1145/3397536.3428349","DOIUrl":"https://doi.org/10.1145/3397536.3428349","url":null,"abstract":"Location services are one of the most used applications today on mobile devices. The vast majority of localization systems propose solutions for locating the user in a 2D single floor environment. However, accurate estimation of the user's floor level, in tall multistory buildings, is a crucial basis for many applications, especially for emergency services. This paper presents a fingerprinting-based system that provides a low-cost floor localization service using the ubiquitous cellular signals received by the user's cell phone. Specifically, a convolutional neural network is trained to map the sequential change of the received cellular signals to the corresponding floor. Evaluation using different Android phones shows that the proposed system can track the user floor with at least 95.9% accuracy in different scenarios. This demonstrates the superiority of the system compared to the state-of-the-art systems in all experiments.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121231342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Konstantina Mellou, Luke Marshall, Krishna Chintalapudi, Patrick Jaillet, Ishai Menache
Large food-service companies typically support a wide range of operations (catering, vending machines, repairs), each with different operational characteristics (manpower, vehicles, tools, timing constraints, etc.). While the advances in Internet-based technologies facilitate the adoption of automated scheduling systems, the complexity and heterogeneity of the different operations hinders the design of comprehensive optimization solutions. Indeed, our collaboration with Compass Group, one of the largest food-service companies in the world, reveals that many of its workforce assignments are done manually due to the lack of scheduling solutions that can accommodate the complexity of operational constraints. Further, the diversity in the nature of operations prevents collaboration and sharing of resources among various services such as catering and beverage distribution, leading to an inflated fleet size. To address these challenges, we design a unified optimization framework, which can be applied to various food-service operations. Our design combines neighborhood search methods and Linear Programming techniques. We test our framework on real food-service request data from a large Compass Group customer, the Puget-Sound Microsoft Campus. Our results show that our approach scales well while yielding fleet size reductions of around 2x. Further, using our unified framework to simultaneously schedule the operations of two different divisions (catering, water distribution) yields 20% additional savings.
大型食品服务公司通常支持范围广泛的业务(餐饮、自动售货机、维修)，每个业务都有不同的操作特征(人力、车辆、工具、时间限制等)。虽然基于互联网的技术进步促进了自动化调度系统的采用，但不同操作的复杂性和异质性阻碍了综合优化解决方案的设计。事实上，我们与Compass集团(世界上最大的食品服务公司之一)的合作表明，由于缺乏能够适应操作约束复杂性的调度解决方案，它的许多劳动力分配都是手动完成的。此外，运营性质的多样性阻碍了餐饮和饮料分销等各种服务之间的协作和资源共享，从而导致机队规模膨胀。为了应对这些挑战，我们设计了一个统一的优化框架，该框架可应用于各种食品服务运营。我们的设计结合了邻域搜索方法和线性规划技术。我们在Compass Group的大客户普吉特海湾微软校园(Puget-Sound Microsoft Campus)的真实餐饮服务请求数据上测试了我们的框架。我们的研究结果表明，我们的方法可以很好地扩展，同时使车队规模减少约2倍。此外，使用我们的统一框架同时安排两个不同部门(餐饮，供水)的运营，可额外节省20%。
{"title":"Optimizing Onsite Food Services at Scale","authors":"Konstantina Mellou, Luke Marshall, Krishna Chintalapudi, Patrick Jaillet, Ishai Menache","doi":"10.1145/3397536.3422266","DOIUrl":"https://doi.org/10.1145/3397536.3422266","url":null,"abstract":"Large food-service companies typically support a wide range of operations (catering, vending machines, repairs), each with different operational characteristics (manpower, vehicles, tools, timing constraints, etc.). While the advances in Internet-based technologies facilitate the adoption of automated scheduling systems, the complexity and heterogeneity of the different operations hinders the design of comprehensive optimization solutions. Indeed, our collaboration with Compass Group, one of the largest food-service companies in the world, reveals that many of its workforce assignments are done manually due to the lack of scheduling solutions that can accommodate the complexity of operational constraints. Further, the diversity in the nature of operations prevents collaboration and sharing of resources among various services such as catering and beverage distribution, leading to an inflated fleet size. To address these challenges, we design a unified optimization framework, which can be applied to various food-service operations. Our design combines neighborhood search methods and Linear Programming techniques. We test our framework on real food-service request data from a large Compass Group customer, the Puget-Sound Microsoft Campus. Our results show that our approach scales well while yielding fleet size reductions of around 2x. Further, using our unified framework to simultaneously schedule the operations of two different divisions (catering, water distribution) yields 20% additional savings.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121474669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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