SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations

Liu, Dongyu; Weng, Di; Li, Yuhong; Bao, Jie; Zheng, Yu; Qu, Huamin; Wu, Yingcai

doi:10.1109/tvcg.2016.2598432

Cited by 158 publications

(86 citation statements)

References 41 publications

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“…However, their approach is limited to daily check‐in distributions for certain POIs. While most of these works focus on techniques and systems, Liu presents a designs‐study using movement data for better advertisement planning [LWL∗17].…”

Section: Related Workmentioning

confidence: 99%

Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data

Krueger

Han

Иванов

et al. 2019

Computer Graphics Forum

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The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large‐scale and up‐to‐date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up‐to‐date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal‐and‐task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back‐end and an interactive visual analytics interface. The monitoring component uses intelligent pre‐aggregation to allow dynamic queries in near real‐time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.

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Section: Related Workmentioning

confidence: 99%

Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data

Krueger

Han

Иванов

et al. 2019

Computer Graphics Forum

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“…[15] future movements under various conditions but also for spatial planning applications. For example, the system SmartAdP [50] finds suitable locations for billboard placement using taxi trajectories. SmartAdP allows the user to select subsets of trajectories and areas of interest depending on the target audience and applies special algorithms for selecting optimal locations based on the traffic volumes and velocities.…”

Section: Modeling and Planningmentioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

118

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This is the accepted version of the paper.This version of the publication may differ from the final published version. City Research Online  Abstract-Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions including social transportation, recommender systems, and policy implications. Permanent repository link

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“…In addition, a simple map visualization (See Sect. 5.1) is used for analysis interpretation, inspired by some visual techniques designed for time-series data (Xie et al 2014;Xu et al 2017) and taxi trajectories (Liu et al 2017;Weng et al 2018). …”

Section: Unsupervised Modelmentioning

confidence: 99%

Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

Lin

Zhu

Guo

et al. 2018

Data Min Knowl Disc

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Anomaly detection in multidimensional data is a challenging task. Detecting anomalous mobility patterns in a city needs to take spatial, temporal, and traffic information into consideration. Although existing techniques are able to extract spatiotemporal features for anomaly analysis, few systematic analysis about how different factors contribute to or affect the anomalous patterns has been proposed. In this paper, we propose a novel technique to localize spatiotemporal anomalous events based on tensor decomposition. The proposed method employs a spatial-feature-temporal tensor model and analyzes latent mobility patterns through unsupervised learning. We first train the model based on historical data and then use the model to capture the anomalies, i.e., the mobility patterns that are significantly different from the normal patterns. The proposed technique is evaluated based on the yellow-cab dataset collected from New York City. The results show several interesting latent mobility patterns and traffic anomalies that can be deemed as anomalous events in the city, suggesting the effectiveness of the proposed anomaly detection method.

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SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations

Cited by 158 publications

References 41 publications

Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data

Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

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