Proceedings of the 29th International Conference on Advances in Geographic Information Systems最新文献_第3页

Integration Model for Estimated Time of Arrival 估计到达时间的集成模型

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3488374

Xuewei Guo, Shenglong Zhang

{"title":"Integration Model for Estimated Time of Arrival","authors":"Xuewei Guo, Shenglong Zhang","doi":"10.1145/3474717.3488374","DOIUrl":"https://doi.org/10.1145/3474717.3488374","url":null,"abstract":"Estimated Time of Arrival (ETA) plays a vital role in many application scenarios. For example, in various scenarios such as online car-hailing order distribution, price estimation, mid-trip estimation, and route decision-making. Accurate arrival time estimation can help the platform improve efficiency. However, accurate arrival time estimation is affected by static information and dynamic information, and the estimated arrival time has high technical difficulties and challenges. The organizers of this competition provided departure time and date, itinerary, road conditions, as well as topological structure data and weather information of the city's road network. At the same time, according to the characteristics of the given data, rich feature processing methods such as statistical features, category features, graph features, embedding features, and sequence features are used to provide massive feature information for model learning. One of the most important points is the application of \"future data\". Of course, in addition to the features, a lot of work has been done on the model structure and model fusion through the combination of machine learning and deep learning, ensuring the accuracy and stability of the model. The ETA is a typical time series problem. Therefore, In the deep learning section, we choose DCN [3] model and WDR [4] model as the basis, and the model distillation is combined on this as the deep learning part of the integrated model. At the same time, traditional machine learning is also used as a part of the integrated model, through a large number of different dimensions of feature engineering, to make up for the machine learning model's inability to better express the time series problem, and build a machine learning model with higher accuracy. Finally, through the fusion of the deep learning model and the machine learning model, extremely high accuracy is achieved in the ETA problem.","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":"130229377","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}

引用次数: 0

JUST-Traj: A Distributed and Holistic Trajectory Data Management System JUST-Traj:分布式整体轨迹数据管理系统

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483990

Huajun He, Ruiyuan Li, Jie Bao, Tianrui Li, Yu Zheng

引用次数: 4

A Semantic Segmentation based POI Coordinates Generating Framework for On-demand Food Delivery Service 基于语义分割的按需送餐服务POI坐标生成框架

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483986

Yatong Song, Jiawei Li, Liying Chen, Shuiping Chen, Renqing He, Zhizhao Sun

{"title":"A Semantic Segmentation based POI Coordinates Generating Framework for On-demand Food Delivery Service","authors":"Yatong Song, Jiawei Li, Liying Chen, Shuiping Chen, Renqing He, Zhizhao Sun","doi":"10.1145/3474717.3483986","DOIUrl":"https://doi.org/10.1145/3474717.3483986","url":null,"abstract":"Nowadays, on-demand food delivery service has become fashionable in China. The efficiency of food delivery relies heavily on accurate coordinates of destination Points of Interest (POI). However, the coordinates of the destination POIs from the existing geospatial data warehouses still have many problems that perplex couriers severely. The major problems can be concluded in two categories: 1) the deviation of POI coordinates; 2) the lack of POI coordinates. To address these problems, we propose a POI-coordinate-generating framework based on couriers' and users' behavioral data of historical waybills. In particular, we start with a combinatorial strategy to assign waybills to Areas of Interest (AOI). Second, we generate a destination POI name by processing the user address for each waybill, and all waybills are grouped by the corresponding POI name. Then, a data density image of the behavioral data is generated for each group, with the ground-truth location of the POI labeled. Finally, a U-Net is trained by using the images generated in the previous step to infer locations of the POIs. We evaluated this framework by launching experiments and case studies on large-scale datasets, and the result shows our framework can predict coordinates of POIs accurately. These predicted coordinates can be used to calibrate deviated coordinates of many POIs and complement the geospatial data warehouse.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"39 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":"116548083","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}

引用次数: 6

Towards Quantum Computing for Location Tracking and Spatial Systems 面向位置跟踪和空间系统的量子计算

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483958

A. Shokry, M. Youssef

引用次数: 7

A Learned Query Optimizer for Spatial Join 空间连接的学习查询优化器

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484217

Tin Vu, A. Belussi, S. Migliorini, A. Eldawy

{"title":"A Learned Query Optimizer for Spatial Join","authors":"Tin Vu, A. Belussi, S. Migliorini, A. Eldawy","doi":"10.1145/3474717.3484217","DOIUrl":"https://doi.org/10.1145/3474717.3484217","url":null,"abstract":"The importance and complexity of spatial join resulted in many join algorithms, some of which run on big-data platforms such as Hadoop and Spark. This paper proposes the first machine-learning-based query optimizer for spatial join operation which can accommodate the skewness of the spatial datasets and the complexity of the different algorithms. The main challenge is how to develop portable cost models that take into account the important input characteristics such as data distribution, spatial partitioning, logic of spatial join algorithms, and the relationship between the two datasets. The proposed system defines a set of features that can all be computed efficiently for the data to catch the intricate aspects of spatial join. Then, it uses these features to train three machine learning models that capture several metrics to estimate the cost of four spatial join algorithms according to user requirements. The first model can estimate the cardinality of spatial join algorithm. The second model can predict the number of rough comparisons for a specific join algorithm. Finally, the third model is a classification model that can choose the best join algorithm to run. Experiments on large scale synthetic and real data show the efficiency of the proposed models over baseline methods.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"58 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":"129362363","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}

引用次数: 6

CityOutlook CityOutlook

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483945

Soto Anno, K. Tsubouchi, M. Shimosaka

{"title":"CityOutlook","authors":"Soto Anno, K. Tsubouchi, M. Shimosaka","doi":"10.1145/3474717.3483945","DOIUrl":"https://doi.org/10.1145/3474717.3483945","url":null,"abstract":"Early crowd dynamics forecasting, such as one week in advance, plays an important role in risk-aware decision-making in urban regions such as congestion mitigation or crowd control for public safety. Although previous approaches have addressed crowd dynamics prediction, they have failed to deal with the scarcity of anomalous events, which results in a large model bias and could not quantify the number of visitors in anomalous crowd gathering. To provide an elaborate early forecast, we focus on the successive properties of importance weighting (IW) to penalize the anomalous data in terms of model bias; however, leveraging the concept of IW is challenging because dividing dataset into normal and abnormal sets is difficult. Motivated by these challenges, we propose CityOutlook, a novel forecasting model based on unbiased regression with importance-based reweighting. To make IW applicable to our approach, we design an anomaly-aware data annotation scheme by utilizing the heterogeneous property of mobility data to determine the data anomaly. We evaluate CityOutlook using the datasets of large-scale mobility and transit search logs. The experimental results show that CityOutlook outperforms the state-of-the-art models on crowd anomaly forecast, providing the same level accuracy in forecasting normal dynamics.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"36 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":"116671075","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}

引用次数: 4

An Interpretable Deep Learning Framework for Assessing Financial Potential of Urban Spaces 用于评估城市空间财务潜力的可解释深度学习框架

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3486810

Yu-En Chang, Hsun-Ping Hsieh

引用次数: 0

On Practical Nearest Sub-Trajectory Queries under the Fréchet Distance 基于区间距离的实用最近邻子轨迹查询

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484264

Joachim Gudmundsson, Martin P. Seybold, John Pfeifer

引用次数: 4

DetectorNet DetectorNet

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483920

He Li, Shiyu Zhang, Xuejiao Li, Liangcai Su, Hongjie Huang, D. Jin, Linghao Chen, Jianbin Huang, Jaesoo Yoo

{"title":"DetectorNet","authors":"He Li, Shiyu Zhang, Xuejiao Li, Liangcai Su, Hongjie Huang, D. Jin, Linghao Chen, Jianbin Huang, Jaesoo Yoo","doi":"10.1145/3474717.3483920","DOIUrl":"https://doi.org/10.1145/3474717.3483920","url":null,"abstract":"Detectors with high coverage have direct and far-reaching benefits for road users in route planning and avoiding traffic congestion, but utilizing these data presents unique challenges including: the dynamic temporal correlation, and the dynamic spatial correlation caused by changes in road conditions. Although the existing work considers the significance of modeling with spatial-temporal correlation, what it has learned is still a static road network structure, which cannot reflect the dynamic changes of roads, and eventually loses much valuable potential information. To address these challenges, we propose DetectorNet enhanced by Transformer. Differs from previous studies, our model contains a Multi-view Temporal Attention module and a Dynamic Attention module, which focus on the long-distance and short-distance temporal correlation, and dynamic spatial correlation by dynamically updating the learned knowledge respectively, so as to make accurate prediction. In addition, the experimental results on two public datasets and the comparison results of four ablation experiments proves that the performance of DetectorNet is better than the eleven advanced baselines.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"41 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":"122700755","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}

引用次数: 11

Online Route Replanning for Scalable System-Optimal Route Planning 基于可扩展系统最优路由规划的在线路由重规划

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484262

R. Fitzgerald, F. Kashani

引用次数: 1