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Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction [article]

Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, Zhenhui Li
2020 arXiv   pre-print
Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm.  ...  The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network, which can be effectively adapted to target cities.  ...  ACKNOWLEDGMENTS We thank Xiuwen Yi for feedback on an early draft of this paper. The work was supported in part by NSF awards #1652525, #1618448, and #1639150.  ... 
arXiv:1901.08518v3 fatcat:7zklfbdhtrbaznovztimoac66u

CityNet: A Multi-city Multi-modal Dataset for Smart City Applications [article]

Xu Geng, Yilun Jin, Zhengfei Zheng, Yu Yang, Yexin Li, Han Tian, Peibo Duan, Leye Wang, Jiannong Cao, Hai Yang, Qiang Yang, Kai Chen
2021 arXiv   pre-print
In addition, to facilitate the use of CityNet, we carry out extensive machine learning experiments, including spatio-temporal predictions, transfer learning, and reinforcement learning.  ...  In this paper, we present CityNet, a multi-modal urban dataset containing data from 7 cities, each of which coming from 3 data sources.  ...  In this paper, we present CityNet, a dataset with data from multiple cities and sources for smart city applications.  ... 
arXiv:2106.15802v1 fatcat:xwhu6pnkerbspewywxcaix26yu

Transforming Future Cities: Smart City

Dhananjay Singh, Antonio J. Jara
2022 Electronics  
The primitive elements of city transformation include the integration of urban infrastructure and artificial intelligence and cutting edge IoT technologies [...]  ...  An autoencoder was adopted to learn the water pattern, which naturally tries to learn the identity function and makes predictions of future patterns.  ...  Data from sensors and public places can be exploited to study and conduct assessment analysis-'big data' and smart civic-analytics for cities is just around the corner, with a major transformation for  ... 
doi:10.3390/electronics11101534 fatcat:4kxqi7gytfgvlam6ziielcnlke

Artificial Intelligence Applications to Smart City and Smart Enterprise

Donato Impedovo, Giuseppe Pirlo
2020 Applied Sciences  
Smart cities work under a more resource-efficient management and economy than ordinary cities.  ...  Published works refer to the following areas of interest: vehicular traffic prediction; social big data analysis; smart city management; driving and routing; localization; and safety, health, and life  ...  More specifically, different machine-learning approaches are combined to obtain a meta-machine-learning model that further aids in maximizing prediction accuracy.  ... 
doi:10.3390/app10082944 fatcat:rbz3kszunzfyvmleusroxm7hhy

A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling [article]

Gaode Chen, Yijun Su, Xinghua Zhang, Anmin Hu, Guochun Chen, Siyuan Feng, Ji Xiang, Junbo Zhang, Yu Zheng
2022 arXiv   pre-print
Concretely, CcFTL transfers the relational knowledge from multiple rich-data source cities to the target city.  ...  To address the above challenging problems, we propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems.  ...  . • MAML [15] : a competitive and classic meta-learning method, which learns initialization parameters from multiple tasks based on a source city. • Meta-MaxUp [16] : A meta-learning approach that explores  ... 
arXiv:2206.00007v1 fatcat:fwvyabxm3bavpmrcebameds3ui

The Futures of the City Region

Michael Neuman, Angela Hull
2009 Regional studies  
Just as there has not been a single dominant paradigm in the past for city regions -regional science competed with the descriptive approach of Patrick Geddes, for example -there is clearly not one paradigm  ...  This type of governance learning is emergent, evolving from the collaboration of a wide variety of interests (INNES et al., 1994; HEALEY, 2006) .  ... 
doi:10.1080/00343400903037511 fatcat:64fvkowyffevrct63q3izzmj4y

An Anthropocentric and Enhanced Predictive Approach to Smart City Management

Davide Carneiro, António Amaral, Mariana Carvalho, Luís Barreto
2021 Smart Cities  
To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support.  ...  Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables.  ...  Acknowledgments: This work was supported by FCT-Fundação para a Ciência e a Tecnologia, through project UIDB/04728/2020. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/smartcities4040072 fatcat:kcalfur6fndo5j5xgik2d5vp6y

Disaster Management in Smart Cities

Luís B. Elvas, Bruno Miguel Mataloto, Ana Lúcia Martins, João C. Ferreira
2021 Smart Cities  
The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures.  ...  The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.  ...  The limited area models (LAMs) are NWP models at high spatial-temporal resolution providing a reaction time that goes from 3 to 6 h up to 72 h, while precipitation nowcasting provides a reaction time between  ... 
doi:10.3390/smartcities4020042 fatcat:bisk5r6i7vfljlju63wacqxe7q

Semantic Trails of City Explorations: How Do We Live a City [article]

Diego Monti, Enrico Palumbo, Giuseppe Rizzo, Raphaël Troncy, Thibault Ehrhart, Maurizio Morisio
2019 arXiv   pre-print
We finally present an application using these datasets to build a recommender system meant to guide tourists while exploring a city.  ...  In this paper, we fill the gaps in defining what is a semantic trail of city exploration and how it can be generated by integrating different data sources.  ...  In order to analyze the check-ins of the two datasets from a spatial point of view, we considered the distributions of the number of check-ins for each city.  ... 
arXiv:1812.04367v2 fatcat:2cg2ffwx6je4hlgsnc2afccnze

III. Domain-Specific Narratives of Change [chapter]

2020 The Redundant City  
Approaching Pruitt-Igoe today, for the purpose of learning from our past mistakes, the initial question would need extending: What can we learn from the failure and what can we learn from the myth of Pruitt-Igoe  ...  Filling the spatial and temporal gap (German 'Lücke') of a redevelopment site, the intervention provided a platform for planned and spontaneous encounters, discussions and play.  ... 
doi:10.14361/9783839451144-004 fatcat:tos4sxpk3zcshdg7aevy7tsuea

CityFlow: Exploiting Edge Computing for Large Scale Smart City Applications

Nam Ky Giang, Victor C.M. Leung, Makoto Kawano, Takuro Yonezawa, Jin Nakazawa, Rodger Lea, Matt Broadbent
2019 2019 IEEE International Conference on Big Data and Smart Computing (BigComp)  
This paper presents an approach to supporting the development process for large-scale smart city applications that leverage edge computing resources.  ...  For the evaluation, a lab-based setup and a real world deployment were executed and are presented.  ...  We assume that the covariate shift exists in the city, as the distribution of the data from the city often varies significantly in spatial and in temporal domains, namely, a spatial-temporal covariate  ... 
doi:10.1109/bigcomp.2019.8679234 dblp:conf/bigcomp/GiangLKYNLB19 fatcat:mudo4ppl5bfujj6wg3jmdlsv6y

Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework [article]

Leye Wang, Di Chai, Xuanzhe Liu, Liyue Chen, Kai Chen
2021 arXiv   pre-print
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches  ...  spatial and temporal factors, aiming to make different application-driven approaches comparable; (ii) we design a spatio-temporal meta-model, called STMeta, which can flexibly integrate generalizable  ...  Guideline 3: Our proposed STMeta can be used as a meta-model to integrate multiple temporal and spatial knowledge.  ... 
arXiv:2009.09379v3 fatcat:f3qzl6rpbvezre2gswfwa55fyi

Sensors and Actuators in Smart Cities

Mohammad Hammoudeh, Mounir Arioua
2018 Journal of Sensor and Actuator Networks  
Acknowledgments: This publication was made possible thanks to NPRPGrant #[6-1508-2-616] from the Qatar National Research Fund (a member of the Qatar Foundation).  ...  Author Contributions: Alex Adim Obinikpo and Burak Kantarci conceived and pursued the literature survey on deep learning techniques on big sensed data for smart health applications, reviewed the state  ...  ) and (ii) related segments (spatial prediction).  ... 
doi:10.3390/jsan7010008 fatcat:pt7nkf4oaraijkmsndohahqtnq

Sensing places' life to make city smarter

Stéphane Roche, Abbas Rajabifard
2012 Proceedings of the ACM SIGKDD International Workshop on Urban Computing - UrbComp '12  
Indeed, in this context, Spatial Data Infrastructure plays an important role and acts as an enabling platform linking governments authoritative spatial information with crowd sourced, voluntary information  ...  Categories and Subject Descriptors -Urban sensing and city dynamics sensing -Smart recommendations in urban spaces See also  ...  as an indivisible whole, as a single coherent and predictable unit.  ... 
doi:10.1145/2346496.2346503 dblp:conf/kdd/RocheR12 fatcat:zl5me372czfadkftwwtudjxoue

Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities

Asma Belhadi, Youcef Djenouri, Kjetil Nørvåg, Heri Ramampiaro, Florent Masseglia, Jerry Chun-Wei Lin
2020 Engineering applications of artificial intelligence  
We have divided the existing approaches into three main categories depending on the type of clustering results.  ...  A B S T R A C T This paper provides a short overview of space-time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping  ...  Multiple and heterogeneous space-time series are first created from the spatio temporal data.  ... 
doi:10.1016/j.engappai.2020.103857 fatcat:oojlhynaq5c53jsipnximndvl4
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