ACM Transactions on Intelligent Systems and Technology, Vol. 5(3), 2014.
1. Urban Computing: Concepts, Methodologies, and Applications
Yu Zheng; Licia Capra; Ouri Wolfson; Hai Yang
Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geographical data. Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety & security, presenting representative scenarios in each category. Thirdly, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we outlook the future of urban computing, suggesting a few research topics that are somehow missing in the community.
Editorial comments: "The first article, entitled “Urban Computing: Concepts, Methodologies, and Applications” by Zheng et al., introduces the concept, general framework and key challenges of urban computing from the perspective of computer sciences. The article surveys the representative research in seven categories of applications. The typical technologies that are needed in urban computing are also proposed, with a few potential research topics suggested at the end."
2. Model-based count series clustering for Bike Sharing System usage mining, a case study with the V´elib’ system of Paris
Come Etienne; Oukhellou Latifa
Today, more and more bicycle sharing systems (BSS) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomena linked to city dynamics. This paper presents a statistical model to automatically analyze the trips data of a bike sharing system. The proposed solution partitions (i.e. cluster) the stations according to their usage profiles. To do so, count series describing the stations’ usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor which handles the differences between the stations’ global usage. Differences between weekday and weekend usage are also taken into account. Thismodel identifies the latent factors which shape the geography of trips and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas which induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris V’elib’ large-scale bike sharing system.
Editorial comments: "The second article, entitled “Model-based count series clustering for Bike Sharing System usage mining, a case study with the Velib' system of Paris” by Come et al., presents a statistical model to automatically partition the stations in terms of their temporal dynamics over the day with respect to the number of rented and returned bikes. The results produced by such an approach give insights on the relationships between stations neighborhood type and the generated mobility patterns, inducing specific usage patterns in the Bike-Sharing-System data."
3. Mining User Check-in Behavior with a Random Walk for Urban Point-of-interest Recommendations
Josh Jia-ching Ying; Eric Hsueh-chan Lu; Wen-Ning Kuo; Vincent S. Tseng
In recent years, research into the mining of user check-in behavior for point-of-interest (POI) recommendations has attracted a lot of attention. Existing studies on this topic mainly treat such
recommendations in a traditional manner, i.e., they treat POIs as items and check-ins as ratings. However, users usually visit a place for reasons other than to simply say that they have visited.
In this paper, we propose an approach called Urban POI-Walk (UPOI-Walk), which takes into account a user’s Socialtriggered Intentions, Preference-triggered Intentions, and Popularity-triggered
Intentions, to estimate the probability of a user checking-in to a POI. The core idea of UPOI-Walk involves building a HITS-based random walk on the normalized check-in network, thus supporting
the prediction of POI properties related to each user’s preferences. To achieve this goal, we define several user-POI graphs to capture the key properties of the check-in behavior motivated by
user-intentions. In our UPOI-Walk approach, we propose a new kind of random walk model named Dynamic HITS-based Random Walk, which comprehensively considers the relevance between POIs and users
from different aspects. On the basis of similitude, we make an online recommendation as to the POI the user intends to visit. To the best of our knowledge, this is the first work on urban POI
recommendations that considers user check-in behavior motivated by Social-triggered Intentions, Preference-triggered Intentions, and Popularity-triggered Intentions in location-based social
network data. Through comprehensive experimental evaluations on two real datasets, the proposed UPOI-Walk is shown to deliver excellent performance.
Editorial comments: "The third article, entitled “Mining User Check-in Behavior with a Random Walk for Urban Point-of-interest Recommendations” by Ying et al., proposes a location recommendation approach concerning a user’s preferences and the properties of a location, using the check-in data from location-based social networks."
4. Using Digital Footprints for a City-scale Traffic Simulation
Gavin McArdle; Eoghan Furey; Aonghus Lawlor; Alexei Pozdnoukhov
This article introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic
simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling
framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of
communities in a country-wide mobile phone network to account for socially related journeys. These data-sets were used to calibrate a variant of a radiation model of spatial choice, which we
introduced in order to drive individuals’ decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a
comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed micro-simulation reproduces not only the
journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.
Editorial comments: "The fourth article, entitled “Using Digital Footprints for a City-scale Traffic Simulation” by McArdle et al., presents a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland, using the digital footprints of city inhabitants on services, like Twitter and Foursquare, rather than conventional methods, such as a population census and dedicated road surveys."
5. Charging and Storage Infrastructure Design for Electric Vehicles
Marjan Momtazpour; Patrick Butler; M. Shahriar Hossain; Mohammad C. Bozchalui; Ratnesh Sharma; Naren Ramakrishnan
Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems
that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy
networks and processes. Next generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in
which they operate. In this paper we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work
with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the
deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple
factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban
electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.
Editorial comments: "The fifth article, entitled “Charging and Storage Infrastructure Design for Electric Vehicles” by Momtazpour et al., present a framework to support placement of charging stations for an electrical vehicle deployment scenario, by modeling and analyzing networks of interactions between electric systems and urban populations."
6. Object-oriented Travel Package Recommendation
Chang Tan; Qi Liu; Enhong Chen; Hui Xiong; Xiang Wu
Providing better travel services for tourists is one of the important applications in urban computing. Though many recommender systems have been developed for enhancing the quality of travel
service, most of them lack a systematic and open framework to dynamically incorporate multiple types of additional context information existing in the tourism domain, such as the travel area,
season, and price of the travel packages. To that end, in this paper, we propose an open framework, Objected-oriented Recommender System (ORS), for the developers performing personalized travel
package recommendation to the tourists. This framework has the ability to import all the available additional context information to the travel package recommendation process in a cost-effective
way. Specifically, the different types of additional information are extracted and uniformly represented as feature-value pairs. Then, we define the Object, which is the collection of the
feature-value pairs. We propose two models which can be used in the ORS framework for extracting the implicit relationships among Objects. Objected-oriented Topic Model (OTM) can extract the
topics conditioned on the intrinsic feature-value pairs of the Objects. Objected-oriented Bayesian Network (OBN) can effectively infer the co-travel probability of two tourists by calculating the
co-occurrence time of feature-value pairs belonging to different kinds of Objects. Based on the relationships mined by OTM or OBN, the recommendation list is generated by the collaborative
filtering method. Finally, we evaluate these two models and the ORS framework on real-world travel package data, and the experimental results show that the ORS framework is more flexible in terms
of incorporating additional context information, and thus leads to better performances for travel package recommendation. Meanwhile, for feature selection in ORS, we define the feature
information entropy, and the experimental results demonstrate that using features with lower entropies usually lead to better recommendation results.
Editorial comments: "The sixth article, entitled “Object-oriented Travel Package Recommendation” by Tan et al., offers tourists proper travel packages that is comprised of a set of selected landscapes, concerning additional contextual information, such as price, time, and route constraints. The proposed method was evaluated based on real travel package data sources."
7. Traffic Information Publication with Privacy Preservation
Sashi Gurung; Dan Lin; Wei Jiang; Ali Hurson; Rui Zhang
We are experiencing the expanding use of location-based services such as AT&T TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which have great potential for statistical usage in applications like traffic flow analysis, infrastructure planning and advertisement dissemination. The key challenge is how to wisely use the data without violating each user’s location privacy concerns. In this paper, we first identify a new privacy problem, namely inference-route problem, and then present our anonymization algorithms for privacy preserving trajectory publishing. The experimental results have demonstrated that our approach outperforms the latest related work in terms of both efficiency and effectiveness.
Editorial comments: "The seventh article, entitled “Traffic Information Publication with Privacy Preservation” by Gurung et al., proposes a privacy-preserving algorithm for publishing spatial trajectories, which are widely generated when people use location-based services, such as an online navigation system."
8. Measuring and Recommending Time-Sensitive Routes from Location-based Data
Hsun-ping Hsieh; Cheng-te Li; Shou-de Lin
Location-based services allow users to perform geo-spatial recording actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale time-stamped location sequence data (e.g. check-ins and GPS traces). We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a route goodness function which aims to measure the quality of a route. Equipped with the route goodness, we recommend time-sensitive routes for two scenarios. The first is about constructing the route based on the user-specified source location with the starting time. The second is about composing the route between the specified source location and the destination location given a starting time. To handle these queries, we propose a search method, Guidance Search, which consists of a novel heuristic satisfaction function which guides the search towards the destination location, and a backward checking mechanism to boost the effectiveness of the constructed route. Experiments on the Gowalla check-in datasets demonstrate the effectiveness of our model on detecting real routes and performing cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.
Editorial comments: "The eighth article, entitled “Measuring and Recommending Time-Sensitive Routes from Location-based Data” by Hsieh et al., recommends time-sensitive routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale time-stamped location sequence data (e.g. check-ins from Gowalla) and the user-specified source and the destination."
9. Check-ins in Blau space: Applying Blau's macro-sociological theory to foursquare check-ins from New York City
Kenneth Joseph; Kathlen M. Carley; Jason I. Hong
Peter Blau was one of the first to define a latent social space and utilize it to provide concrete hypotheses. Blau defines social structure via social “parameters” (constraints). Actors that are
closer together (more homogenous) in this social parameter space are more likely to interact. One of Blau’s most important hypotheses resulting from this work was that the consolidation of
parameters could lead to isolated social groups. For example, the consolidation of race and income might lead to segregation. In the present work, we use foursquare data from New York city to
explore evidence of homogeneity along certain social parameters and consolidation that breeds social isolation in communities of locations checked-in to by similar users.
More specifically, we first test the extent to which communities detected via Latent Dirichlet Allocation are homogenous across a set of four social constraints - racial homophily, income homophily, personal interest homophily and physical space. Using a bootstrapping approach, we find that fourteen (of twenty) communities are statistically, and all but one qualitatively, homogenous along one of these social constraints, showing the relevance of Blau’s latent space model in venue communities determined via user check-in behavior. We then consider the extent to which communities with consolidated parameters, those homogenous on more than one parameter, represent socially isolated populations. We find communities homogenous on multiple parameters, including a homosexual community and a “hipster” community, that show support for Blau’s hypothesis that consolidation breeds social isolation. We consider these results in the context of mediated communication, in particular in the context of self representation on social media.
Editorial comments: "The ninth article, entitled “Check-ins in Blau space: Applying Blau's macro-sociological theory to foursquare check-ins from New York City” by Joseph et al., uses Latent Dirichlet Allocation to cluster Foursquare users in New York City into different types, e.g. tourists and athletics, using geo-spatial location, time, users’ friends on social networking sites and venue function, etc."