Proceedings of the 28th International Conference on Advances in Geographic Information Systems最新文献_第10页

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems 拼车系统的时空层次自适应调度

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-09-04 DOI: 10.1145/3397536.3422212

Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai, Qun Li, Cheng Zhang, Kehua Sheng, Guobin Wu, X. Qie, Xinbing Wang

{"title":"Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems","authors":"Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai, Qun Li, Cheng Zhang, Kehua Sheng, Guobin Wu, X. Qie, Xinbing Wang","doi":"10.1145/3397536.3422212","DOIUrl":"https://doi.org/10.1145/3397536.3422212","url":null,"abstract":"Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the entire city at a uniform time interval. However, the uneven spatio-temporal order distributions in real-world ridesharing systems indicate that such an approach is suboptimal in practice. Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time. Specifically, we propose a hierarchical approach, which generates clusters of geographical areas suitable to share the same dispatching intervals, and then makes online decisions of selecting the appropriate time instances for order dispatch within each spatial cluster. Technically, we prove the impossibility of designing constant-competitive-ratio algorithms for the online adaptive interval problem, and propose online algorithms under partial or even zero future order knowledge that significantly improve the platform's profit over existing approaches. We conduct extensive experiments with a large-scale ridesharing order dataset, which contains all of the over 3.5 million ridesharing orders in Beijing, China, received by Didi Chuxing from October 1st to October 31st, 2018. The experimental results demonstrate that our proposed algorithms outperform existing approaches.","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":"2020-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169956","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: 1

Interactive and Explainable Point-of-Interest Recommendation using Look-alike Groups 使用相似组的交互式和可解释的兴趣点推荐

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-08-31 DOI: 10.1145/3397536.3422238

Behrooz Omidvar-Tehrani, Sruthi Viswanathan, J. Renders

{"title":"Interactive and Explainable Point-of-Interest Recommendation using Look-alike Groups","authors":"Behrooz Omidvar-Tehrani, Sruthi Viswanathan, J. Renders","doi":"10.1145/3397536.3422238","DOIUrl":"https://doi.org/10.1145/3397536.3422238","url":null,"abstract":"Recommending Points-of-Interest (POIs) is surfacing in many location-based applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make recommendations. However these systems still lack customizability and contextuality particularly in cold start situations. In this paper, we propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets. LikeMind reformulates the problem of POI recommendation, as recommending explainable look-alike groups (and their POIs) which are in line with user's interests. LikeMind frames the task of POI recommendation as an exploratory process where users interact with the system by expressing their favorite POIs, and their interactions impact the way look-alike groups are selected out. Moreover, LikeMind employs \"mindsets\", which capture actual situation and intent of the user, and enforce the semantics of POI interestingness. In an extensive set of experiments, we show the quality of our approach in recommending relevant look-alike groups and their POIs, in terms of efficiency and effectiveness.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125624174","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

Towards A Personal Shopper's Dilemma: Time vs Cost 面对个人购物者的困境:时间vs成本

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-08-26 DOI: 10.1145/3397536.3422276

Samiul Anwar, Francesco Lettich, M. Nascimento

引用次数: 0

Spatial Privacy Pricing: The Interplay between Privacy, Utility and Price in Geo-Marketplaces 空间隐私定价:地理市场中隐私、效用和价格之间的相互作用

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-08-25 DOI: 10.1145/3397536.3422213

Kien Nguyen, John Krumm, C. Shahabi

引用次数: 3

Reimagining City Configuration: Automated Urban Planning via Adversarial Learning 重新构想城市结构:通过对抗性学习实现的自动化城市规划

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-08-22 DOI: 10.1145/3397536.3422268

Dongjie Wang, Yanjie Fu, Pengyang Wang, B. Huang, Chang-Tien Lu

{"title":"Reimagining City Configuration: Automated Urban Planning via Adversarial Learning","authors":"Dongjie Wang, Yanjie Fu, Pengyang Wang, B. Huang, Chang-Tien Lu","doi":"10.1145/3397536.3422268","DOIUrl":"https://doi.org/10.1145/3397536.3422268","url":null,"abstract":"Urban planning refers to the efforts of designing land-use configurations. Effective urban planning can help to mitigate the operational and social vulnerability of a urban system, such as high tax, crimes, traffic congestion and accidents, pollution, depression, and anxiety. Due to the high complexity of urban systems, such tasks are mostly completed by professional planners. But, human planners take longer time. The recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration, so human planners can finally adjust machine-generated plans for specific needs? To this end, we formulate the automated urban planning problem into a task of learning to configure land-uses, given the surrounding spatial contexts. To set up the task, we define a land-use configuration as a longitude-latitude-channel tensor, where each channel is a category of POIs and the value of an entry is the number of POIs. The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area. In particular, we first characterize the contexts of surrounding areas of an unplanned area by learning representations from spatial graphs using geographic and human mobility data. Second, we combine each unplanned area and its surrounding context representation as a tuple, and categorize all the tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to distinguish among positive and negative samples. Finally, we devise two new measurements to evaluate the quality of land-use configurations and present extensive experiment and visualization results to demonstrate the effectiveness of our method.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122836545","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: 17

TA-Dash: An Interactive Dashboard for Spatial-Temporal Traffic Analytics TA-Dash:用于时空交通分析的交互式仪表板

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-07-31 DOI: 10.1145/3397536.3422344

Nicolas Tempelmeier, Anzumana Sander, U. Feuerhake, Martin Löhdefink, Elena Demidova

{"title":"TA-Dash: An Interactive Dashboard for Spatial-Temporal Traffic Analytics","authors":"Nicolas Tempelmeier, Anzumana Sander, U. Feuerhake, Martin Löhdefink, Elena Demidova","doi":"10.1145/3397536.3422344","DOIUrl":"https://doi.org/10.1145/3397536.3422344","url":null,"abstract":"In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these models is often diminished due to the lack of accessible user interfaces to view and analyse prediction results. In this paper, we present the Traffic Analytics Dashboard (TA-Dash), an interactive dashboard that enables the visualisation of complex spatial-temporal urban traffic patterns. We demonstrate the utility of TA-Dash at the example of two recently proposed spatial-temporal models for urban traffic and urban road infrastructure analysis. In particular, the use cases include the analysis, prediction and visualisation of the impact of planned special events on urban road traffic as well as the analysis and visualisation of structural dependencies within urban road networks. The lightweight TA-Dash dashboard aims to address non-expert users involved in urban traffic management and mobility service planning. The TA-Dash builds on a flexible layer-based architecture that is easily adaptable to the visualisation of new models.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127306120","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: 2

Dynamic Population Estimation Using Anonymized Mobility Data 基于匿名流动数据的动态人口估计

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-06-24 DOI: 10.1145/3397536.3422203

Xiang Liu, Philo Pöllmann

引用次数: 1

Succinct Trit-array Trie for Scalable Trajectory Similarity Search 用于可扩展轨迹相似度搜索的简洁三列矩阵

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-05-21 DOI: 10.1145/3397536.3422210

Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei

{"title":"Succinct Trit-array Trie for Scalable Trajectory Similarity Search","authors":"Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei","doi":"10.1145/3397536.3422210","DOIUrl":"https://doi.org/10.1145/3397536.3422210","url":null,"abstract":"Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Locality sensitive hashing (LSH) is a powerful technique for fast similarity searches. Recent methods employ LSH and attempt to realize an efficient similarity search of trajectories; however, those methods are inefficient in terms of search time and memory when applied to massive datasets. To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie. We also present two novel techniques that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is a space-efficient representation that leverages the idea behind succinct data structures (i.e., a compressed data structure supporting fast data operations). We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly in comparison to state-of-the-art similarity search methods.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112466","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

Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization 站到用户迁移学习:利用潮汐正则化非负矩阵分解的潜在行程特征实现可解释的用户聚类

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-04-27 DOI: 10.1145/3397536.3422250

Liming Zhang, Andreas Zufle, D. Pfoser

{"title":"Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization","authors":"Liming Zhang, Andreas Zufle, D. Pfoser","doi":"10.1145/3397536.3422250","DOIUrl":"https://doi.org/10.1145/3397536.3422250","url":null,"abstract":"Urban areas provide us with a treasure trove of available data capturing almost every aspect of a population's life. This work focuses on mobility data and how it will help improve our understanding of urban mobility patterns. Readily available and sizable farecard data captures trips in a public transportation network. However, such data typically lacks temporal signatures and as such the task of inferring trip semantics, station function, and user clustering is quite challenging. While existing approaches either focus on station-level or user-level signals only, we propose a Station-to-User (S2U) transfer learning framework, which augments user-level learning with shared temporal patterns learned from station-level signals. Our framework is based on a novel, so-called \"Tidal-Regularized Non-negative Matrix Factorization\" method, which incorporates a-priori tidal traffic patterns in generic Non-negative Matrix Factorization. To evaluate our model performance, a user clustering stability test based on the classical Rand Index is introduced as a metric to benchmark different unsupervised learning models. Using this metric, quantitative evaluations on three real-world datasets show that S2U outperforms two baselines methods by 7-21%. We also provide a qualitative analysis of the user clustering and station functions for the Washington D.C. metro and show how S2U can support spatiotemporal urban analytics.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124038551","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: 2

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting 面向PM2.5预测的领域知识增强图神经网络

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-02-10 DOI: 10.1145/3397536.3422208

Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao

引用次数: 66