1,561 research outputs found
QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis
[EN] Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Projects 20813/PI/18 and 20530/PDC/18 and by the Spanish Ministry of Science, Innovation and Universities under Grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5.Terroso-Saenz, F.; Muñoz-Ortega, A.; Cecilia-Canales, JM. (2019). QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis. Sensors. 19(22):1-22. https://doi.org/10.3390/s19224882S1221922Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., … Schwartz, J. D. (2017). Air Pollution and Mortality in the Medicare Population. New England Journal of Medicine, 376(26), 2513-2522. doi:10.1056/nejmoa1702747Li, B., Cai, Z., Jiang, L., Su, S., & Huang, X. (2019). Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression. Cities, 87, 68-86. doi:10.1016/j.cities.2018.12.033Yang, Y., Yuan, Z., Fu, X., Wang, Y., & Sun, D. (2019). Optimization Model of Taxi Fleet Size Based on GPS Tracking Data. Sustainability, 11(3), 731. doi:10.3390/su11030731Smith, A. W., Kun, A. L., & Krumm, J. (2017). Predicting taxi pickups in cities. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. doi:10.1145/3123024.3124416Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., & Lin, L. (2019). Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3875-3887. doi:10.1109/tits.2019.2915525Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. doi:10.1080/15230406.2014.890072James, N. A., Kejariwal, A., & Matteson, D. S. (2016). Leveraging cloud data to mitigate user experience from ‘breaking bad’. 2016 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2016.7841013Kuang, L., Yan, X., Tan, X., Li, S., & Yang, X. (2019). Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning. Remote Sensing, 11(11), 1265. doi:10.3390/rs11111265Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., … Li, L.-J. (2016). YFCC100M. Communications of the ACM, 59(2), 64-73. doi:10.1145/2812802Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11. doi:10.1145/2020408.2020579Estevez, P. A., Tesmer, M., Perez, C. A., & Zurada, J. M. (2009). Normalized Mutual Information Feature Selection. IEEE Transactions on Neural Networks, 20(2), 189-201. doi:10.1109/tnn.2008.2005601Zheng, X., Han, J., & Sun, A. (2018). A Survey of Location Prediction on Twitter. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1652-1671. doi:10.1109/tkde.2018.2807840Assam, R., & Seidl, T. (2014). Context-based location clustering and prediction using conditional random fields. Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia - MUM ’14. doi:10.1145/2677972.2677989Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017). Random Forests for Big Data. Big Data Research, 9, 28-46. doi:10.1016/j.bdr.2017.07.003Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., … Lv, W. (2017). The Simpler The Better. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/3097983.3098018Markou, I., Rodrigues, F., & Pereira, F. C. (2018). Real-Time Taxi Demand Prediction using data from the web. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). doi:10.1109/itsc.2018.8569015Zhou, Y., Wu, Y., Wu, J., Chen, L., & Li, J. (2018). Refined Taxi Demand Prediction with ST-Vec. 2018 26th International Conference on Geoinformatics. doi:10.1109/geoinformatics.2018.8557158Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013). Predicting Taxi–Passenger Demand Using Streaming Data. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1393-1402. doi:10.1109/tits.2013.2262376Jiang, S., Chen, W., Li, Z., & Yu, H. (2019). Short-Term Demand Prediction Method for Online Car-Hailing Services Based on a Least Squares Support Vector Machine. IEEE Access, 7, 11882-11891. doi:10.1109/access.2019.289182
The Role of Urban Mobility in Retail Business Survival.
Economic and urban planning agencies have strong interest in tackling the hard problem of predicting the odds of survival of
individual retail businesses. In this work, we tap urban mobility data available both from a location-based intelligence platform,
Foursquare, and from public transportation agencies, and investigate whether mobility-derived features can help foretell the
failure of such retail businesses, over a 6 month horizon, across 10 distinct cities spanning the globe. We hypothesise that the
survival of such a retail outlet is correlated with not only venue-specific characteristics but also broader neighbourhood-level
effects. Through careful statistical analysis of Foursquare and taxi mobility data, we uncover a set of discriminative features,
belonging to the neighbourhood’s static characteristics, the venue-specific customer visit dynamics, and the neighbourhood’s
mobility dynamics. We demonstrate that classifiers trained on such features can predict such survival with high accuracy,
achieving approximately 80% precision and recall across the cities. We also show that the impact of such features varies
across new and established venues and across different cities. Besides achieving a significant improvement over past work on
business vitality prediction, our work demonstrates the vital role that mobility dynamics plays in the economic evolution of a
city
Predicting the temporal activity patterns of new venues
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners
Predicting the temporal activity patterns of new venues.
Estimating revenue and business demand of a newly opened venue is paramount
as these early stages often involve critical decisions such as first rounds of staffing
and resource allocation. Traditionally, this estimation has been performed through
coarse-grained measures such as observing numbers in local venues or venues at
similar places (e.g., coffee shops around another station in the same city). The
advent of crowdsourced data from devices and services carried by individuals on a
daily basis has opened up the possibility of performing better predictions of
temporal visitation patterns for locations and venues. In this paper, using mobility
data from Foursquare, a location-centric platform, we treat venue categories as
proxies for urban activities and analyze how they become popular over time. The
main contribution of this work is a prediction framework able to use characteristic
temporal signatures of places together with k-nearest neighbor metrics capturing
similarities among urban regions, to forecast weekly popularity dynamics of a new
venue establishment in a city neighborhood. We further show how we are able to
forecast the popularity of the new venue after one month following its opening by
using locality and temporal similarity as features. For the evaluation of our
approach we focus on London. We show that temporally similar areas of the city
can be successfully used as inputs of predictions of the visit patterns of new
venues, with an improvement of 41% compared to a random selection of wards as
a training set for the prediction task. We apply these concepts of temporally
similar areas and locality to the real-time predictions related to new venues and
show that these features can effectively be used to predict the future trends of a
venue. Our findings have the potential to impact the design of location-based
technologies and decisions made by new business owners
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Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications
The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation.
Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban
infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability.
All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area
Mining open datasets for transparency in taxi transport in metropolitan environments.
Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban commuters, we provide a direct price comparison between Uber and the Yellow Cab company in New York. We discover that Uber, despite its lower standard pricing rates, effectively charges higher fares on average, especially during short in length, but frequent in occurrence, taxi journeys. Building on this insight, we develop a smartphone application, OpenStreetCab, that offers a personalized consultation to mobile users on which taxi provider is cheaper for their journey. Almost five months after its launch, the app has attracted more than three thousand users in a single city. Their journey queries have provided additional insights on the potential savings similar technologies can have for urban commuters, with a highlight being that on average, a user in New York saves 6 U.S. Dollars per taxi journey if they pick the cheapest taxi provider. We run extensive experiments to show how Uber's surge pricing is the driving factor of higher journey prices and therefore higher potential savings for our application's users. Finally, motivated by the observation that Uber's surge pricing is occurring more frequently that intuitively expected, we formulate a prediction task where the aim becomes to predict a geographic area's tendency to surge. Using exogenous to Uber data, in particular Yellow Cab and Foursquare data, we show how it is possible to estimate customer demand within an area, and by extension surge pricing, with high accuracy.This is the final version of the article. It was first available from Springer via http://dx.doi.org/10.1140/epjds/s13688-015-0060-
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Modeling Urban Venue Dynamics through Spatio-Temporal Metrics and Complex Networks
The ubiquity of GPS-enabled devices, mobile applications, and intelligent transportation systems have enabled opportunities to model the world at an unprecedented scale. Urban environments, in particular, have benefited from new data sources that provide granular representations of activities across space and time. As cities experienced a rise in urbanization, they also faced challenges in managing vehicle levels, congestion, and public transportation systems. Modeling these fast-paced changes through rich data from sources such as taxis, bikes, and trains has enabled prediction models capable of characterizing trends and forecasting future changes. Data-driven studies of urban mobility dynamics have been instrumental in helping deliver more contextual services to cities, support urban policy, and inform business decisions. This dissertation explores how novel algorithmic architectures and techniques reveal and predict business trends and urban development patterns.
The research informing this dissertation harnesses principles from network science, modeling cities as connected networks of venues. Building upon a foundation of research in complex network theory, urban computing, and machine learning, we propose algorithms tailored for three computing tasks focused on modeling venue dynamics, characteristics, and trends. First, we predict the demand for newly opened businesses using insights from movement patterns across different regions of the city. Through this analysis we demonstrate how temporally similar areas can be successfully used as inputs to predict the visitation patterns of new venues. Next, we forecast the likelihood of business failure through a supervised learning model. We analyze the value of varying features in predicting business failure and explore their impact across new and established venues and across different cities worldwide. Finally, we present a deep learning architecture which integrates both spatial and topological features to predict the future demand for a venue. These works highlight the power of complex network measures to quantify the structure of a city and inform prediction models.
This dissertation leverages vast amounts of data from spatio-temporal networks to model venue dynamics. The research puts forward evidence to support a data-driven study of geographic systems applied to fundamental questions in urban studies, retail development, and social science.Gates Cambridge Trus
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
Origin-destination~(OD) flow modeling is an extensively researched subject
across multiple disciplines, such as the investigation of travel demand in
transportation and spatial interaction modeling in geography. However,
researchers from different fields tend to employ their own unique research
paradigms and lack interdisciplinary communication, preventing the
cross-fertilization of knowledge and the development of novel solutions to
challenges. This article presents a systematic interdisciplinary survey that
comprehensively and holistically scrutinizes OD flows from utilizing
fundamental theory to studying the mechanism of population mobility and solving
practical problems with engineering techniques, such as computational models.
Specifically, regional economics, urban geography, and sociophysics are adept
at employing theoretical research methods to explore the underlying mechanisms
of OD flows. They have developed three influential theoretical models: the
gravity model, the intervening opportunities model, and the radiation model.
These models specifically focus on examining the fundamental influences of
distance, opportunities, and population on OD flows, respectively. In the
meantime, fields such as transportation, urban planning, and computer science
primarily focus on addressing four practical problems: OD prediction, OD
construction, OD estimation, and OD forecasting. Advanced computational models,
such as deep learning models, have gradually been introduced to address these
problems more effectively. Finally, based on the existing research, this survey
summarizes current challenges and outlines future directions for this topic.
Through this survey, we aim to break down the barriers between disciplines in
OD flow-related research, fostering interdisciplinary perspectives and modes of
thinking.Comment: 49 pages, 6 figure
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