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Tackling Air Pollution Using Big Data

Refer to Urban Air’s homepage for more details.

A. Real-Time Air Quality Inference

Public Website: http://urbanair.msra.cn/ Install Windows phone App here.

Goal: We infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs).

Methodology: We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location.

Publication:

[1] Yu Zheng, Furui Liu, Hsun-Ping Hsie. U-Air: When Urban Air Quality Inference Meets Big Data. 19th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2013).

[2] Yu Zheng, Xuxu Chen, Qiwei Jin, Yubiao Chen, Xiangyun Qu, Xin Liu, Eric Chang, Wei-Ying Ma, Yong Rui, Weiwei Sun. A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality. MSR-TR-2014-40.

B. Forecast Fine-Grained Air Quality

Goal: We forecast the reading of an air quality monitoring station in the next 48 hours, using a data-driven method that considers the current meteorological data, weather forecasts, and the air quality data of the station and that of other stations within a few hundred kilometers to the station.

快帆翻国外加速软件 Our predictive model is comprised of four major components: 1) a linear regression-based temporal predictor to model the local factor of air quality, 2) a neural network-based spatial predictor modeling the global factors, 3) a dynamic aggregator combining the predictions of the spatial and temporal predictors according to the meteorological data, and 4) an inflection predictor to capture the sudden changes of air quality.

Publication:

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快帆 - 帖子 | Facebook:2021-12-27 · 快帆. 13,324 次赞 · 58 人在谈论. 「快帆」是一款帮助海外华人用户高速访问中国网络的加速软件，可众让境外华人从国外直连到中国国内。

Publication:

[1] Hsun-Ping Hsieh*, Shou-De Lin, Yu Zheng. 快帆加速器 - Chrome Web Store:快帆加速器 是一款免费网络加速器，一键帮助海外华人翻墙回国，畅享中国精彩网络生活 - 看剧、听歌、玩游戏，给您最好的体验，是海外华人必备网络工具。 快帆加速器 功能列表: * 加速国内主流音视频平台和网游平台，提供智能和全局加速功能 * 全球部署. In the Proceeding of the 21th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2015).

D. Identifying the Root Cause of Air Pollution

The research identifies the spatio-temporal causality between air pollutants of different cities, suggesting the root cause of air pollution based on AI  and big data technology.

Publication:

[1] Julie Yixuan Zhu, Chao Zhang, Huichu Zhang, Shi Zhi, Victor O.K. Li, Jiawei Han, and 快帆翻国外加速软件, pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data. IEEE Transactions on Big Data. DOI: 10.1109/TBDATA.2017.2723899

Media Coverage:

[1] Analyzing newly available data about the intricacies of urban life could make cities better.” 快帆翻国外加速软件. 2013.8.21

[2] Interviewed by IFeng.com. Big data can predict air quality. 2013.11.29 (In Chineses)

[3] ComputerWorld: Microsoft predicts China’s air pollution with data analysis, 2015.6.11

[4] Ming Pao (HK): Microsoft predicts air quality with big data, 2015.6.10

[5] GeekWire Reporter: What Microsoft Research is doing to help Beijing air pollution. 2015.11.30

[6] NBC News: Microsoft, IBM Eye Big Business Opportunity in China’s Air Pollution. 2015.12.28

[7] Reuters: 玲珑网游加速器_360百科:2021-3-2 · 玲珑网游加速器,玲珑网游加速器由珑凌科技有限公司研发。从事国际网游加速专业，产品经历了四年的市场考验，尤其针对台服魔兽，暗黑3与bf3等游戏，效果显著。全国各一线城市均设有节点，辐射周边能够彻底解决网游延迟过高，登录困难，频繁掉线等问题，彻底实现了电信，网通，铁通，教育 ..., 2015.12.28

[8] China Daily: 快帆 - 快帆Windows、Mac实现全局翻墙方法教程... | Facebook:快帆Windows、Mac实现全局翻墙方法教程 亲爱的饭团伔，由于经常收到你伔的反馈，快帆电脑版无法实现全局模式，导致无法使用酷狗、爱奇艺、优酷等客户端，为了饭团伔更好的体验，我伔整理热心饭团【Mc 再续辉煌丶】分享的方法供大家使用~ 使用方法： 1、在电脑上使用浏览器打开快帆官网.... 2016.1.19

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Diagnosing Urban Noise Using Big Data

Refer to the homepage of CityNoise for details.

Goal: we infer the fine-grained noise situation (consisting of a noise pollution indicator and the composition of noises) of different times of day for each region of NYC, by using the 311 complaint data together with social media, road network data, and Points of Interests (POIs).

Methodology: We model the noise situation of NYC with a three dimension tensor, where the three dimensions stand for regions, noise categories, and time slots, respectively. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we recover the noise situation throughout NYC. The information can inform people and officials’ decision making.

Publication:

[1] Yu Zheng, Tong Liu, Yilun Wang, Yanchi Liu, Yanmin Zhu, Eric Chang. Diagnosing New York City’s Noises with Ubiquitous Data. In Proceedings of the 16th ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014).

[2] Tong Liu, Yu Zheng, Lubin Liu, Yanchi Liu, Yanmin Zhu. Methods for Sensing Urban Noises. MSR-TR-2014-66, May 2014.

[3] Yilun Wang, Yu Zheng, Tong Liu. A Noise Map of New York City. In Proc. of Ubicomp 2014, Demo.

Data and 免费翻国外墙的app Released.

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Predicting Urban Water Quality Based on Big Data

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. In this work, we forecast the water quality of a station over the next few hours, using a multitask multi-view learning method to fuse multiple datasets from different domains. In particular, our learning model comprises two alignments. The first alignment is the spaio-temporal view alignment, which combines local spatial and temporal information of each station. The second alignment  is the prediction alignment among stations, which captures their spatial correlations and performs co-predictions by incorporating these correlations. This approach has been evaluated based on the data of Shenzhen city in China.

Publication:

[1] Ye Liu, 快帆翻国外加速软件, Yuxuan Liang, Shuming Liu, David S. Rosenblum, Urban Water Quality Prediction based on Multi-task Multi-view Learning , in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016)

萤火虫加速器下载地址-快连加速器app

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Abstract: We present a large-scale taxi ridesharing service, which efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. We first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxies that could satisfy a user query. A schedule allocation algorithm is then proposed to check each candidate taxi so as to insert the user’s trip into the schedule of the taxi. Our service can serve 40% additional taxi users while saving 15% travel distance over no ridesharing on average.

Publications:

[1] Shuo Ma, Yu Zheng, Ouri Wolfson. T-Share: A Large-Scale Dynamic Taxi Ridesharing Service. IEEE International Conference on Data Engineering (ICDE 2013) Best Paper Runner-up Award.

[2] Shuo Ma*, Yu Zheng, Ouri Wolfson. Top 10 Apps like 快帆-海外华人加速器 in 2021 for iPhone & iPad:Discover the Top 10 Apps like 快帆-海外华人加速器 in 2021 for iPhone & iPad. ... 全球1100万海外华人在用，无需注册，免费线路，一键回国！ 1. 内置免费线路，供你自由加速到国内网络 2.. IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 27, No. 7, July 2015. [Codes] [Data]

Travel Time Estimation of a Path Based on Trajectories

Abstract: We propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in a city instantly, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. The problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem. Third, we need to instantly answer users’ queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation.

Publications:

[1] Yilun Wang, Yu Zheng, Yexiang Xue. Travel Time Estimation of a Path using Sparse Trajectories. In Proceedings of the 20th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2014). (Data and codes)

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在国内怎么翻国外app: In this research, we aim to mine the time-dependent and practically quickest driving route for end users using GPS-equipped taxicabs traveling in a city.

Insight: The time that a driver traverses a route depends on three aspects: 1) The physical feature of a route, such as distance, the number of traffic lights and direction turns; 2) The time-dependent traffic flow on the route; 3) A user’s drive behavior. Thus, a good routing service should consider these three aspects (routes, traffic and drivers), which are far beyond the scope of the shortest path computing.

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. Consequently, the trajectories of taxicabs already have the knowledge of experienced drivers, physical routes and traffic conditions.

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快帆翻国外加速软件

[1] Jing Yuan, Yu Zheng, et al. T-Drive: Driving Directions Based on Taxi Trajectories. In ACM SIGSPATIAL GIS 2010, The 快帆翻国外加速软件.

[2] Jing Yuan, Yu Zheng, et al, T-Drive: Enhancing Driving Directions with Taxi Drivers’ Intelligence. Transactions on Knowledge and Data Engineering (TKDE).

Media Reports

[1] Adding cabbie know-how to online maps, MIT Technology Review, 2010.11.6

[2] Follow that cab! Racing Google Maps on city streets, NewScientist, 2010.11.5

在国内怎么翻国外app: Later, we expanded this research by considering the drive behavior and traffic prediction as well as other factors affecting driving, such as weather conditions. Specifically, we proposed a model incorporating day of the week, time of day, weather conditions, and individual driving strategies (both of the taxi drivers and of the end user for whom the route is being computed). Using this model, our system predicts the traffic conditions of a future time (when the computed route is actually driven) and performs a self-adaptive driving direction service for a particular user. This service gradually learns a user’s driving behavior from the user’s GPS logs and customizes the fastest route for the user. Refer to the following publication for details.

Publications

[1] Jing Yuan, Yu Zheng, et al. Driving with Knowledge from the Physical World. 17th SIGKDD conference on Knowledge Discovery and Data Mining (快帆翻国外加速软件).

Media Reports

[1] “A driving route made just for you“, MIT Technology Review, 2011.8.30.

A Passenger-Cabbie Recommender System

Abstract: We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers’ mobility patterns and 2) taxi drivers’ pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations (and the routes to these locations), towards which they are more likely to pick up passengers quickly (during the routes or at the parking places) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we propose a parking place detection algorithm and learn the above knowledge (represented by probabilities) from trajectories. Then, we feed the knowledge into a probabilistic model which estimates the profit of a parking place for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using trajectories generated by 12,000 taxis in 110 days.

Publications

[1] Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie. Where to Find My Next Passenger? , 13th ACM International Conference on Ubiquitous Computing (UbiComp 2011).

[2] Nicholas Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie. T-Finder: A Recommender System for Finding Passengers and Vacant Taxis. accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).

Traffic Prediction in a Bike Sharing System

在国内怎么翻国外app: Bike-sharing systems are widely deployed in many major cities, providing a conven­ient tra­n­s­­por­tation mode for citizens’ comm­utes. As the rents/returns of bikes at different stations in different periods are unbalanced, the bikes in a system need to be rebalanced frequently. In this paper, we propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed in advance.

Methodology: We first propose a bipartite clustering algori­thm to cluster bike stations into groups, formula­ting a two-level hier­archy of stations. The total number of bikes that will be rent in a city is predicted by a Gradient Boosting Regression Tree (GBRT). Then a multi-similarity-based infer­en­ce model is propo­sed to predi­ct the rent propor­tion across clusters and the inter-cluster transition, based on which the number of bikes rent from/ returned to each cluster can be easily inferred.

Publication:

[1] Yexin Lee, Yu Zheng, Huichu Zhang, Lei Chen. Traffic Prediction in a Bike Sharing System, In Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems (ACM SIGSPATIAL 2015) (Data)(在国内怎么翻国外app)

在国内怎么翻国外app

萤火虫加速器下载地址-快连加速器app

Discovering Region of Different Functions in a City Using Human Mobility and POIs

Goal: We propose a framework (titled DRoF) that Discovers Regions of different Functions, such as educational areas and business districts, in a city using both human mobility among regions and points of interests (POIs) located in a region. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations.

Insight: We segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns.

Publications:

[1] Jing Yuan, Yu Zheng, Xing Xie. 工信部回应VPN指导意见:不会影响国内外用户正常使用 ...:2021-1-30 · 中国网1月30日讯国务院新闻办1月30日举行新闻发布会,华尔街日报记者就VPN相关情况在会上进行提问,"请您介绍一下于1月27日发布的关于VPN的指导意见 .... 18th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2012).

[2] Nicholas Jing Yuan, 快帆翻国外加速软件, Xing Xie, Yingzi Wang, Kai Zheng, Hui Xiong. Discovering Urban Functional Zones Using Latent Activity Trajectories, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2016.

Glean the underlying problems in a City’s Road Network

Abstract: Urban computing for city planning is one of the most significant applications in Ubiquitous computing. In this paper we detect flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas. The detected results consist of 1) pairs of regions with salient traffic problems and 2) the linking structure as well as correlation among them. These results can evaluate the effectiveness of the carried out planning, such as a newly built road and subway lines in a city, and remind city planners of a problem that has not been recognized when they conceive future plans. We conduct our method using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and evaluate our results with the real urban planning of Beijing.

Publications

[1] Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie, 免费翻国外墙的app, 13th ACM International Conference on Ubiquitous Computing (UbiComp 2011), Beijing, China, Sep. 2011. The best paper nominee award.

[2] A technical report describing the map segmentation and trajectory projection details.

Media Reports

[1] “Taxicab data helps ease traffic“. Future of Technology on NBCNEWS.com. 2011.9.30

[2] “GPS Data on Beijing Cabs Reveals the Cause of the Traffic Jams“. MIT Technology Review, 2011.9.27. Featured on the first page.

[3] “Urban computing based on taxicabs”. Reported by ACM TechNews. 2011.9.27

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在国内怎么翻国外app: An urban black hole is a subgraph (or a region) that has the overall inflow greater than the overall outflow by a threshold, while a volcano is a subgraph with the overall outflow greater than the overall inflow by a threshold. The online detection of black holes/volcanos can timely reflect anomalous events, such as disasters, catastrophic accidents, and therefore help keep public safety. The patterns of black holes/volcanos and the relations between them reveal human mobility patterns in a city, thus help formulate a better city planning or improve a system’s operation efficiency.

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[1] Liang Hong, 免费翻国外墙的app, Duncan Yung, Jingbo Shang, Lei Zou. PC下载网—官方软件下载大全|绿色免费软件下载网站:PC下载网提供最新的绿色免费软件官方下载服务，打造绿色免费软件下载基地， 满足用户在电脑办公、手机应用、影音娱乐、网络游戏等全方位的软件下载需求。 常用软件下载，来PC下载网就够了. In Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems (ACM SIGSPATIAL 2015). (Codes) (Data)

Planning Bike Lanes based on Sharing-Bikes’ Trajectories

Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this project, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.

Publications:

[1] Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, Yu Zheng. Planning bike lanes based on Sharing-bike’s trajectories. GOGO加速器下载|海外专用视频加速器_最火手机站:2021-12-14 · GOGO加速器app手机版方便海外用户翻墙观看国内影视视频众及各类直播，也能轻松玩国内游戏，全程网络稳定，一键加速更方便，轻松去除手机垃圾，让手机内存更快，方便在国外也能轻松上网，解决大家出国在外，没有网络，无法观看国内视频众及上网问题，一键让你轻松翻墙，喜欢的小伙伴快 …KDD 2017).

萤火虫加速器下载地址-快连加速器app

在国内怎么翻国外app

在国内怎么翻国外app We instantly infers the gas consumption and pollution emission of vehicles traveling on a city’s road network in a current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge can be used to suggest cost-efficient driving routes as well as identifying road segments where gas has been wasted significantly.

Methodology: We propose a Travel Speed Estimation (TSE) model based on a context-aware matrix factorization approach. TSE leverages features learned from other data sources, e.g., map data and historical trajectories, to deal with the data sparsity problem. We then propose a Traffic Volume Inference (TVI) model to infer the number of vehicles passing each road segment per minute. TVI is an unsupervised Bayesian Network that incorporates multiple factors, such as travel speed, weather conditions and geographical features of a road. Given the travel speed and traffic volume of a road segment, gas consumption and emissions are calculated based on existing environmental theories. Refer to publication [1] for details.

免费翻国外墙的app

We propose a step toward real-time sensing of refueling behavior and citywide petrol consumption. We use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits, measure the time spent, and estimate overall demand. For times and stations with sparse data, we use collaborative filtering to estimate conditions. Our system provides real-time estimates of gas stations’ wait times, from which recommendations could be made, an indicator of overall gas usage, from which macro-scale economic decisions could be made, and a geographic view of the efficiency of gas station placement. refer to publication [2] and [3] for details.

Publications:

[1] Jingbo Shang*, Yu Zheng, Wenzhu Tong, Eric Chang. Inferring Gas Consumption and Pollution Emission of Vehicles throughout a City. In the Proceeding of the 20th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2014).

[2] Fuzhen Zhang, David Wilkie, Yu Zheng, Xing Xie. Sensing the Pulse of Urban Refueling Behavior. 15th ACM International Conference on Ubiquitous Computing (在国内怎么翻国外app)

[3] Fuzheng Zhang*, Nicholas Jing Yuan, David Wilkie, Yu Zheng, Xing Xie. Sensing the Pulse of Urban Refueling Behavior: A Perspective from Taxi Mobility. ACM Transaction on Intelligent Systems and Technology (ACM TIST). 2015.

Media coverage: featured by United Nations.

萤火虫加速器下载地址-快连加速器app

A. Forecasting Flow of Crowds Based on Big Data

Abstract: Predicting the movement of crowds in a city is strategically important for traffic management, risk assessment, and public safety. In this project, we propose predicting two types of flows of crowds in every region of a city based on big data, including human mobility data, weather conditions, and road network data. To develop a practical solution for citywide traffic prediction, we first partition the map of a city into regions using both its road network and historical records of human mobility. Our problem is different than the predictions of each individual’s movements and each road segment’s traffic conditions, which are computationally costly and not necessary from the perspective of public safety on a citywide scale. To model the multiple complex factors affecting crowd flows, we decompose flows into three components: seasonal (periodic patterns), trend (changes in periodic patterns), and residual flows (instantaneous changes). The seasonal and trend models are built as intrinsic Gaussian Markov random fields which can cope with noisy and missing data, whereas a residual model exploits the spatio-temporal dependence among different flows and regions, as well as the effect of weather.

Publications

[1] Minh X. Hoang, Yu Zheng, Ambuj K. Singh. FCCF: Forecasting Citywide Crowd Flows Based on Big Data. in Proceedings of the 24th ACM International Conference on Advances in Geographical Information Systems (ACM SIGSPATIAL 2016) (PPT)

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B. Deep Learning-Based Crowd Flow Prediction

Abstract: Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design the end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the framework of the residual neural networks to model the temporal smoothness, period, and trend properties of crowd traffic, respectively. For each property, we design a branch of residual convolutional blocks, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of week, to predict the final traffic of crowds in each and every region. We evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding its performance outperforming the state-of-the-art.

Publications:

[1] Junbo Zhang, Yu Zheng, Dekang Qi. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017) [code][data][system]

[2] Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi. DNN-Based Prediction Model for Spatial-Temporal Data. in Proceedings of the 24th ACM International Conference on Advances in Geographical Information Systems (ACM SIGSPATIAL 2016) Demo Paper (arXiv version)

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C. Detect Collective Anomalies from Cross-Domain Data

Abstract: The collective anom­aly denotes a collection of nearby locations that are anomalous during a few consecutive time intervals in terms of phenomena collectively witnessed by multiple datasets. The collective anoma­lies suggest there are underlying problems that may not be identified based on a single data source or in a single location. It also assoc­iates individual locations and time intervals, formulating a panora­mic view of an event. To detect a collective anomaly is very challe­n­ging, however, as different data­sets have diff­erent densities, distributions, and scales. Additional­ly, to find the spatio-temporal scope of a collective anomaly is time consu­ming as there are many ways to combine regions and time slots.

Publications

[1] Yu Zheng, Huichu Zhang, Yong Yu. Detecting Collective Anomalies from Multiple Spatio-Temporal Datasets across Different Domains. In Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems (免费翻国外墙的app). (Data) (快帆翻国外加速软件)

d. Crowd Sensing of Traffic Anomalies in a City

网络博客 - 第 4 页 | 郑永博客:软件介绍： 「快帆」是一款免费帮助海外华人用户翻墙回国内高速访问中国网络的VPN加速软件，可众让境外华人从国外直连到中国国内，无论在世界何处，都可众无任何限制的使用优酷、爱奇艺、、酷狗、B站 …: We detect anomalies in a city according to the taxi trajectories. The anomaly could be caused by unexpected or sudden accidents, such as traffic control, protests, concerts, parades, celebrations, and large-scale sale promotion. In many cases, the anomaly occurs before the corresponding accident actually happens. If detecting the unusual mobility pattern of people in this region in advance, we can solve the problem early and avoid the happening of the tragedy.

[a] Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan and Xing Xie. Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams. In KDD 2011.

[b] Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu, and Yu Zheng. On Mining Anomalous Patterns in Road Traffic Streams. In the 7th International Conference on Advanced Data Mining and Applications (ADMA 2011). The best paper award

[2] Diagnose and Describe Traffic Anomalies: In publication , we identify the source traffic flow that results in an anomaly. In publication [d], we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media.

Sanjay Chawla, Yu Zheng, and Jiafeng Hu. Inferring the root cause in road traffic anomalies, IEEE International Conference on Data Mining (ICDM 2012).

[d] Bei Pan, Yu Zheng, David Wilkie, and Cyrus Shahabi. Crowd Sensing of Traffic Anomalies based on Human Mobility and Social Media. 免费翻国外墙的app GIS 2013

Ranking Real Estates Using Big Data

Abstract: Ranking residential real estate based on a diversity of big data around a real estate: 1) geographical data, such as road network and points of interests (POI), 2) human mobility data, like public commuting data and taxi traces, and 3) people’s comments and reviews on these real estates and their surrounding POIs in online services. These datasets reveal the popularity and quality of the location where a real estate is located.

Publications:

[1] Yanjie Fu, Hui Xiong, Yong Ge, Zijun Yao, Yu Zheng. Exploiting Geographic Dependencies for Real Estate Appraisal: A Mutual Perspective of Ranking and Clustering. In the Proceeding of the 20th SIGKDD conference on Knowledge Discovery and Data Mining (在国内怎么翻国外app).

[2] Yanjie Fu, Yong Ge, Yu Zheng, Zijun Yao, Yanchi Liu, Hui Xiong, Nicholas Jing Yuan. Sparse Real Estate Ranking with Online User Reviews and Offline Moving Behaviors. IEEE International Conference on Data Mining (ICDM 2014).

Large-Scale and Dynamic City Express

Abstract: Due to the large number of requirements for city express services in recent years, the current city express system is found to be unsatisfactory for both the service providers and customers. In this paper, we are the first to systematically study the large-scale dynamic city express problem. We aim to increase both the effectiveness and the efficiency of the scheduling algorithm. The challenges of the problem stem from the highly dynamic environment, the NP-completeness with respect to the number of requests, and real-time demands for the scheduling result. our solution can save 37.5% manpower to achieve 80% satisfaction ratio compared to the basic solution.

Publications:

[1] Siyuan Zhang, Lu Qin, Yu Zheng, and Hong Cheng. Effective and Efficient: Large-scale Dynamic City Express. IEEE Transactions on Data Engineering (TKDE)

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Abstract: The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions.

Publications:

[1] Dongyu Liu, Di Weng, Yuhong Li, Yingcai Wu, Jie Bao, Yu Zheng, Huaming Qu, “SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations”, in The IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2016).

Constructing Popular Routes from User Check-in Data

Abstract: We present a Route Inference framework based on Collective Knowledge (RICK) to construct the popular routes from uncertain trajectories, e.g., a user’s check-in sequence in FourSquare, geo-tagged photos in Flickr, or the migratory trails of a bird. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain + uncertain → certain). Our work can benefit trip planning, traffic management, and animal movement studies.

Publications:

[1] Ling-Yin Wei, Yu Zheng, Wen-Chih Peng, Constructing Popular Routes from Uncertain Trajectories. 18th SIGKDD conference on Knowledge Discovery and Data Mining (快帆翻国外加速软件). (快帆翻国外加速软件)

[2] Hechen Liu, Ling-Yin We, Yu Zheng, Markus Schneider, Wen-Chih Peng. Route Discovery from Mining Uncertain Trajectories. Demo Paper, in IEEE International Conference on Data Mining (ICDM 2011).