Proceedings of the 29th International Conference on Advances in Geographic Information Systems最新文献

{"title":"From movement purpose to perceptive spatial mobility prediction","authors":"Licia Amichi, A. C. Viana, M. Crovella, A. Loureiro","doi":"10.1145/3474717.3484220","DOIUrl":"https://doi.org/10.1145/3474717.3484220","url":null,"abstract":"A major limiting factor for prediction algorithms is the forecast of new or never before-visited locations. Conventional personal models utterly relying on personal location data perform poorly when it comes to discoveries of new regions. The reason is explained by the prediction relying only on previously visited/seen (or known) locations. As a side effect, locations that were never visited before (or explorations) by a user cause disturbance to known location's prediction. Besides, such explorations cannot be accurately predicted. We claim the tackling of such limitation first requires identifying the purpose of the next probable movement. In this context, we propose a novel framework for adjusting prediction resolution when probable explorations are going to happen. As recently demonstrated [3, 15], there exist regularities in returning and exploring visits. Moreover, the geographical occurrences of explorations are far from being random in a coarser-grained spatial resolution. Exploiting these properties, instead of directly predicting a user's next location, we design a two-step predictive framework. First, we infer an individual's next type of transition: (i) a return, i.e., a visit to a previously known location, or (ii) an exploration, i.e., a discovery of a new place. Next, we predict the next location or the next coarse-grained zone depending on the inferred type of movement. We conduct extensive experiments on three real-world GPS mobility traces. The results demonstrate substantial improvements in the accuracy of prediction by dint of fruitfully forecasting coarse-grained zones used for exploration activities. To the best of our knowledge, we are the first to propose a framework solely based on personal location data to tackle the prediction of visits to new places.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126264847","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}

{"title":"Multiple Views of Semantic Trajectories in Indoor and Outdoor Spaces","authors":"Hassan Noureddine, C. Ray, Christophe Claramunt","doi":"10.1145/3474717.3483915","DOIUrl":"https://doi.org/10.1145/3474717.3483915","url":null,"abstract":"Exploiting semantic information related to human mobility is particularly useful in crowd-sourcing environments where multidimensional data represent human trajectories and contextual information arising in indoor and outdoor spaces. This paper introduces a modelling approach and data manipulation mechanisms that represent semantic trajectories at different levels of abstraction. The objective is to produce a hybrid spatial representation for continuous mobility patterns emerging in indoor and outdoor spaces. This approach is based on a multi-layered graph that represents trajectories derived on the fly according to some given spatio-temporal constraints. The multi-layer graph provides a hierarchy of semantic places that offers several data manipulation capabilities according to given contextual and user-defined criteria.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133520352","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}

{"title":"Accelerated Assessment of Critical Infrastructure in Aiding Recovery Efforts During Natural and Human-made Disaster","authors":"Gautam S. Thakur, Kelly M. Sims, Chantelle Rittmaier, Joseph Bentley, Debraj De, Junchuan Fan, Tao Liu, R. Palumbo, Jesse McGaha, P. Nugent, Bryan Eaton, Jordan Burdette, Tyler Sheldon, Kevin A. Sparks","doi":"10.1145/3474717.3483947","DOIUrl":"https://doi.org/10.1145/3474717.3483947","url":null,"abstract":"Relief and recovery from disasters (both natural and human-made) require a coordinated approach across several federal and state government agencies. In order to achieve optimal resource allocation and deployment of first responders, accurate and timely assessment of the impact and extent of destruction are the cornerstones to any recovery effort. Ideally, this knowledge should be gathered and shared within the first 0-24 hours (termed as \"Acute Phase\" by the U.S. CDC guideline) for informed decision-making. But achieving this poses significant challenges for the data collection and data harmonization processes, particularly when voluminous data are being generated from diverse and distributed sources during the disaster responses. To this end, this work developed a scalable and efficient workflow to dynamically collect and harmonize crowd-sourced geographic multi-modal data, and then assess critical infrastructure (CI) damaged during disaster events. We demonstrate the application of our framework with two real-world experiences in addressing post-disaster recovery efforts - for the Bahamas (Natural - due to Hurricane Dorian, 2019) and Beirut (Human-made - due to explosion caused by the ammonium nitrate stored in a warehouse, 2020). We have illustrated that a coordinated effort is needed for planning as well as for execution to achieve informed decision making.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053985","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}

{"title":"RACER","authors":"Rohit Verma, J. Brazauskas, Vadim Safronov, Matthew Danish, Ian Lewis, R. Mortier","doi":"10.1145/3474717.3484270","DOIUrl":"https://doi.org/10.1145/3474717.3484270","url":null,"abstract":"As smart environments become laden with more and more sensors, there has been a need to develop systems that could derive useful information from these sensors and make the smart environments smarter. Complex Event Processing (CEP) has emerged as a popular strategy to identify crucial events from sensor data. However, the existing CEP strategies overlook the relationship with other sensors in the spatial vicinity and understate the temporal variation of sensor data. In this paper, we develop RACER, which is an end-to-end complex event processing system that takes into consideration both the spatial location of the sensor in observation and the varying impact of temporal changes in the sensor data. Experiments performed for a duration of five months over both collected and live streaming data shows that RACER fares well compared to the other state-of-the-art approaches.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"38 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114021990","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}

{"title":"Reinforced Feature Extraction and Multi-Resolution Learning for Driver Mobility Fingerprint Identification","authors":"Mahan Tabatabaie, Suining He, Xi Yang","doi":"10.1145/3474717.3483911","DOIUrl":"https://doi.org/10.1145/3474717.3483911","url":null,"abstract":"Taking into account the availability of the historical GPS trajectories of drivers, given a new GPS trajectory, Driver mobility fingerprint (DMF) identification aims at (i) determining whether a generated trajectory belongs to a potential driver, and (ii) detecting if a trajectory is likely anomalous based on a driver's historical data. Prior studies often consider hand-crafted feature engineering techniques to extract DMFs while contextual factors like weather and points-of-interest (POIs) are hardly accounted for, which might not achieve satisfactory identification results. To address above, we propose RM-Drive, a novel framework based on reinforced feature extraction and multi-resolution learning. Specifically, we first employ spatio-temporal inverse reinforcement learning (ST-IRL) to extract DMFs from historical trajectories. Then, we generate trajectory embeddings by fusing the extracted DMFs and the contextual factors using the multi-resolution trajectory embedding network (MTE-Net). Our proposed MTE-Net consists of multi-resolution convolutional neural network (MR-CNN), which enables the model to learn the multi-resolution features of the DMFs. Finally, we leverage the trajectory embeddings for the driver classification and anomaly detection. We have conducted extensive evaluation studies upon RM-Drive with two real-world datasets, and our results demonstrate the performance improvements from the state-of-the-art of driver classification and anomaly detection respectively by 21% and 11% on average based on several evaluation metrics, including accuracy, precision, and recall, etc.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717103","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}

{"title":"Simulating Human Mobility with Agent-based Modeling and Particle Filter Following Mobile Spatial Statistics","authors":"Mingfei Cai, Y. Pang, Takehiro Kashiyama, Y. Sekimoto","doi":"10.1145/3474717.3484203","DOIUrl":"https://doi.org/10.1145/3474717.3484203","url":null,"abstract":"Human mobility datasets collected from various sources are indispensable for analyzing, predicting, and solving emerging urbanization and population issues. However, such datasets are only available to the public after aggregation and anonymous processing. In recent years, agent-based modeling approaches have addressed this problem by reproducing synthetic human mobility data through simulation. However, the development of such agent models typically requires a large amount of personal location histories as training data for parameter learning, leading to cost and privacy concerns. To overcome this disadvantage, we attempted to explore optimal parameters using a particle filter to alleviate the strict requirement of the data. We tested our method in a local city in Japan using aggregated real-time observation data collected from mobile phone service companies. The results show that the proposed model can achieve satisfactory accuracy using low-resolution data and can therefore be easily used by local governments for municipal applications.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213031","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}

{"title":"Learning Decision Making Strategies of Non-experts: A NEXT-GAIL Model for Taxi Drivers","authors":"Menghai Pan, Xin Zhang, Yanhua Li, Xun Zhou, Jun Luo","doi":"10.1145/3474717.3483924","DOIUrl":"https://doi.org/10.1145/3474717.3483924","url":null,"abstract":"Thanks to the rapid development of mobile sensing techniques, massive human-generated spatial-temporal data (HSTD) are generated from the urban areas, e.g., passenger-seeking trajectories from taxi drivers, and public transit trips from urban dwellers. These HSTD record sequential decisions made by human agents. Studying human behavior from HSTD provides benefits to many aspects, for example, studying passenger-seeking strategies from experienced taxi drivers can help improve the operation efficiencies of those new drivers. One common method to analyze human behavior from HSTD is Imitation Learning (IL). Existing IL approaches rely on data collected from experts. However, human agents who generate HSTD may have diverse expertise levels across geographical regions, i.e., with good policies in some regions and poor policies in less experienced regions. The problem of how to infer the optimal policy for agents in their unfamiliar or less-experienced regions remains open. In this paper, we propose the novel Generative Adversarial Imitation Learning for Non-experts (NEXT-GAIL) framework to first disentangle expert knowledge, which is irrelevant to spatial-temporal regions, from the demonstration data. Then, such knowledge can be transferred to regions, where the agent does not possess an expert policy. We take the real-world taxi trajectory data as an example to evaluate the performance of our proposed framework. The comparison results illustrate that our proposed NEXT-GAIL outperforms existing state-of-the-art approaches regarding the accuracy of the inferred optimal policy for non-experts.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123559549","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}

{"title":"A-GWR: Fast and Accurate Geospatial Inference via Augmented Geographically Weighted Regression","authors":"Mohammad Reza Shahneh, Samet Oymak, A. Magdy","doi":"10.1145/3474717.3484260","DOIUrl":"https://doi.org/10.1145/3474717.3484260","url":null,"abstract":"Geographically Weighted Regression (GWR) is a seminal technique with rich applications in geospatial data analysis. However, it has critical drawbacks in the age of big data in terms of expressiveness, i.e., predictive power, and scalability. This work proposes Augmented GWR (A-GWR) that alleviates these drawbacks. A-GWR adapts a novel technique, Stateless-MGWR or S-MGWR, that enriches the predictive power by allowing different training data features to influence at different spatial scales. S-MGWR uses a customized black-box optimization approach for discovering optimal parameters in a fast and efficient way. In addition, A-GWR modularly combines S-MGWR with versatile models such as random forest models. Moreover, A-GWR enables scalability by operating on partitioned data to adapt to tight computational budgets. Our extensive experiments on various real and synthetic datasets demonstrate the scalability and accuracy benefits of the proposed techniques over state-of-the-art competitors.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125513257","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}

{"title":"Managing Trajectories and Interactions During a Pandemic: A Trajectory Similarity-based Approach (Demo Paper)","authors":"E. Buckland, E. Tanin, N. Geard, C. Zachreson, Hairuo Xie, H. Samet","doi":"10.1145/3474717.3484206","DOIUrl":"https://doi.org/10.1145/3474717.3484206","url":null,"abstract":"COVID-19 has brought about substantial social, economic and health related burdens, motivating different control measures from policy makers worldwide. Contact tracing plays a pivotal role in the COVID-19 era. However, contact tracing is by nature entirely retrospective: it can only identify contacts of known or suspected cases. Our proposed system is prospective, aiming to 'create' networks that will ultimately make contact tracing and pandemic management easier. As contact tracing seeks to reconstruct the underlying interaction network, we can improve the process by reducing the complexity of contact network structure; we introduce a method for reducing contact network complexity through strategic scheduling. The method functions through pairwise comparison of individual trajectories in a coordinate space of activities, locations, and time intervals. We demonstrate the method through a simulated scenario where individuals (students) register for activities using a mobile application in a campus. The application then applies our algorithm to provide individuals with schedules that reduce the complexity of the overall network, without compromising individual privacy.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"451 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129435351","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}

{"title":"Vehicle Trajectory Recovery on Road Network Based on Traffic Camera Video Data","authors":"Zongyu Lin, Guozhen Zhang, Zhiqun He, Jie Feng, Wei Wu, Yong Li","doi":"10.1145/3474717.3483987","DOIUrl":"https://doi.org/10.1145/3474717.3483987","url":null,"abstract":"A large-scale system for obtaining fine-grained vehicle trajectories is becoming increasingly important because it lays a solid foundation for a wide range of downstream applications, such as urban traffic optimization, road network profiling, route planning, etc. Traditional methods recover the trajectories from GPS data from apps or coarse-grained traces collected from base stations, which are costly and, more importantly, only cover limited vehicles on the road. Thus, they are not applicable to downstream tasks. To fill this gap, we explore the possibility of recovering vehicle trajectories from the video data recorded by widely deployed traffic cameras. The major challenges lie in the quality of the captured image, low sampling rate, and unbalanced temporal and spatial distribution. To address these challenges, we propose a general system to recover vehicle trajectories at the level of the road intersection, where a novel iterative framework is developed to combine both vehicle clustering and trajectory recovery tasks, which improve their performance simultaneously. The key motivation is that vehicle clustering based on visual features can provide essential discrete points for trajectory recovery, while the recovered routes can introduce spatial-temporal constraints to the initial vehicle clusters for de-noising the false results and complement the missing results. To prove the feasibility of our framework, we collect and plan to release a city-scale traffic camera dataset consisting of 24 hours of videos from 673 cameras across 1,106 intersections. To the best of our knowledge, this benchmark is the first to contain the ground truth of vehicle trajectories with a wide range of spatial and temporal coverage in an urban environment. We conduct extensive experiments and analysis on datasets of different scales to demonstrate the robustness of our framework. Last but not least, we have already deployed the whole system in the business applications of SenseTime, China, including traffic signal control and traffic flow analysis. We highly expect this dataset to further facilitate the research in this field and contribute more to traffic optimization systems in the real world.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200530","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}