2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献

A LSTM and Graph CNN Combined Network for Community House Price Forecasting 基于LSTM和Graph CNN的社区房价预测网络

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-15

Chuancai Ge

引用次数: 6

Golang-Based POI Discovery and Recommendation in Real Time 基于golang的POI实时发现与推荐

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00114

Qing Fan, Lang Jiao, Chengcheng Dai, Ziqiang Deng, Rui Zhang

引用次数: 5

Temporal Graph Convolutional Networks for Traffic Speed Prediction Considering External Factors 考虑外部因素的时间图卷积网络交通速度预测

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-52

Liang Ge, Hang Li, Junling Liu, Aoli Zhou

{"title":"Temporal Graph Convolutional Networks for Traffic Speed Prediction Considering External Factors","authors":"Liang Ge, Hang Li, Junling Liu, Aoli Zhou","doi":"10.1109/MDM.2019.00-52","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-52","url":null,"abstract":"Traffic speed prediction is an important part of intelligent transportation systems (ITS). If road traffic speed is predicted accurately, we can provide not only evidence for urban traffic managers, but also support for other road services such as path planning. Traditional prediction models usually ignore the spatio-temporal dependencies of the traffic dynamics and influences of external factors. This paper proposes a Temporal Graph Convolutional Networks (GTCN) which is composed of spatio-temporal component and external component to solving the traffic speed prediction problem. The spatio-temporal component integrates k-order spectral graph convolution and dilated casual convolution to capture the spatio-temporal dependencies. The external component takes social factors such as day of the week into account. To further improve the prediction accuracy, we consider the road structure features and point of interest (POI) during the construction of the sensor station graph. We evaluate the prediction model on two datasets from the Caltrans Performance Measurement System (CalTrans PeMS). Experiments show that the proposed GTCN model obtains high accuracy and outperforms state-of-the-art baselines.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128244955","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: 29

Optimal Delivery Routing in Road Network With Occupancy Detection 考虑占用率检测的路网最优配送路径

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00122

Shan-Yun Teng, Szu-Chan Wu, Kun-Ta Chuang

引用次数: 1

Construction of Elderly Mutual Aid Time Bank Based on Blockchain 基于区块链的老年互助时间银行构建

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.000-1

L. Cui, Kehong Yuan, Xiaoyu Zhao, Larry Y. D. Mou

引用次数: 8

MapReuse: Recycling Routing API Queries MapReuse:回收路由API查询

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-47

R. Stanojevic, Sofiane Abbar, M. Mokbel

{"title":"MapReuse: Recycling Routing API Queries","authors":"R. Stanojevic, Sofiane Abbar, M. Mokbel","doi":"10.1109/MDM.2019.00-47","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-47","url":null,"abstract":"Commercial maps often offer traffic awareness which is critical for many location based services. On the other hand free and open map services (such as government maps or OSM) are traffic oblivious and hence are of limited value for such services. In this paper we show that coarse information available from a commercial map routing API, can be dissected into fine-grained per-road-segment traffic information which can be reused in any application requiring traffic-awareness. Our system MapReuse queries a commercial map for a (relatively small) number of routes, and uses the returned routes and expected travel times, to infer travel time on each individual edge of the road network. Such fine-grained travel time information can be used not only to infer travel time on any given route but also to compute complex spatial queries (such as traffic-aware isochrone map) for free. We test our system on four representative metropolitan areas: Bogota, Doha, NYC and Rome, and report very encouraging results. Namely, we observe the median and mean percentage errors of MapReuse, measured against the travel times reported by the commercial map, to be in the range of 4% to 8%, implying that MapReuse is capable to accurately reconstruct the traffic conditions in all four studied cities.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115390891","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: 3

Efficient Photo Crowdsourcing in Delay-Tolerant Networks with Evolving POIs 具有演化poi的时延容忍网络中的高效照片众包

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-62

Shudip Datta, S. Madria

{"title":"Efficient Photo Crowdsourcing in Delay-Tolerant Networks with Evolving POIs","authors":"Shudip Datta, S. Madria","doi":"10.1109/MDM.2019.00-62","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-62","url":null,"abstract":"In a disaster or battlefield zone, the rescue workers, soldiers and other survivors (referred to as nodes) may need to survey the damages and send images to the command and control center (the server) in a hop by hop fashion in the absence of any communication infrastructure. The server considers some area/landmark as the point of interest (POI), and distributes the request to the nodes to collect more information about them. Nodes take photos of POIs and share them among each other using store and forward paradigm, called Delay-tolerant Networks (DTNs) to send them to the server. Due to highly intermittent contact characteristics of nodes in a DTN network, and bandwidth and storage limitations, redundant photos need to be omitted in this forwarding technique whereas photos that cover different angles and views of the targets need to be shared. Another challenge is that, over time, some server-listed POIs may not be of importance whereas some new POIs might be of interest. In this work, we propose a scheme that is able to dynamically update the list of POIs based on the current photo metadata, with reduced consumption of the bandwidth, energy and the storage at nodes by sending only important photos of POIs. We compare our proposed schemes with a related well-known scheme [21] to show the scalability of our approaches which provide the same level of photo coverage, but consumes much less energy and bandwidth.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126143556","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: 3

PTGF: Public Transport General Framework for Identifying Transport Modes Based on Cellular Data PTGF:基于蜂窝数据识别交通方式的公共交通通用框架

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00120

Xiaochuan Gou, Chih-Chieh Hung, Guanyao Li, Wen-Chih Peng

{"title":"PTGF: Public Transport General Framework for Identifying Transport Modes Based on Cellular Data","authors":"Xiaochuan Gou, Chih-Chieh Hung, Guanyao Li, Wen-Chih Peng","doi":"10.1109/MDM.2019.00120","DOIUrl":"https://doi.org/10.1109/MDM.2019.00120","url":null,"abstract":"Public transportation is beating heart of a city. Understanding how citizens utilize public transportation can be used to optimize many applications such as traffic planning, crowd flow prediction, and location-based marketing. However, obtaining how citizens used transportation is not a trivial task. It is almost not possible to ask citizens to report their exact location and their transportation mode; moreover, there are usually various public transportation that move along the similar paths. These increase challenges to identify people's transport modes. To address these issues, this paper proposes Public Transport General Framework (PTGF) to identify people's transport modes by their cellular data in both offline and online manners. Regarding the offline phase, given historical cellular data of people and urban transportation networks, PTGF derives cellular data into trajectories, to match each trajectory to public transportation networks to find the most possible transport modes for sub-trajectories of a trajectory. In the online phase, given streaming trajectories, PTGF identifies the transport modes of each location by an LSTM which are trained by historical trajectories with transport modes annotated in the offline phase. Extensive experiments are conducted by using both synthetic and real datasets. The experimental results show that the accuracy of PTGF in offline phase around 80% and that in online phase F1-score around 0.7, which could prove that the effectiveness of the proposed framework PTGF.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122882520","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

Detecting Mobile Crowdsensing Context in the Wild 在野外检测移动众感环境

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-60

R. Agarwal, Shaan Chopra, V. Christophides, N. Georgantas, V. Issarny

引用次数: 3

Deep Learning-Based Spatial Analytics for Disaster-Related Tweets: An Experimental Study 基于深度学习的灾害相关推文空间分析:一项实验研究

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-40

Shayan Shams, S. Goswami, Kisung Lee

引用次数: 4