Trajectory-driven Influential Billboard Placement

Zhang, Ping; Bao, Zhifeng; Li, Yuchen; Li, Guoliang; Zhang, Yipeng; Peng, Zhiyong

doi:10.1145/3219819.3219946

Cited by 65 publications

(32 citation statements)

References 27 publications

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“…Liu et al [197] try to select optimal placements (a vertex or edge who contains many traffic flows) on road networks to place outdoor billboards. Zhang et al [198] consider the constraint of the total budget. They design a model on range and one-time impressions to solve the problem.…”

Section: Business Locationmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

Self Cite

215

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Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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Section: Business Locationmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

Self Cite

215

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“…Measurement of reaching the audience or audience frequency is also a key problem of outdoor advertising and marketing, and it is handled in Lichtenthal et al [1]. Trajectories of the billboards and the campaigns to be assigned to them may also be taken into account in order to maximize the influence of an advert [15] by comparing a greedy algorithm, a partition-based framework, and a LazyProbe method. In another work, data collected from the mobile phones of users are used to construct an optimal assignment for the digital advert-billboard assignment problem [16].…”

Section: Related Workmentioning

confidence: 99%

Optimization of real-world outdoor campaign allocations

Akdoğan¹,

Altan²,

Peker³

et al. 2020

Turk J Elec Eng & Comp Sci

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In this paper, we investigate the outdoor campaign allocation problem (OCAP), which asks for the distribution of campaign items to billboards considering a number of constraints. In particular, for a metropolitan city with a large number of billboards, the problem becomes challenging. We propose a genetic algorithm-based method to allocate campaign items effectively, and we compare our results with those of nonlinear integer programming and greedy approaches. Real-world data sets are collected with the given constraints of the price class ratios of billboards located in İstanbul and the budgets of the given campaigns. The methods are evaluated in terms of the efficiency of the constructed plans and the construction time of the planning. The results reveal that the genetic algorithm-based approach gives close to optimal results in the shortest scheduling time for the OCAP, and it scales linearly with the increasing data sizes.

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“…Thanks to the recent advancements of location-aware technologies, a mount of new geo-located data is available now, such as mobile phone data, GPS data and so on, which provides the possibility to solve this problem. As a result, there are a growing number of studies using trajectory data for outdoor advertising in recent years [10], [12]. For example, Liu et al [10] combined billboard location selection and visualization together using large-scale taxi GPS trajectory data.…”

Section: Related Work a Trajectory Data For Outdoor Advertisingmentioning

confidence: 99%

“…For example, Liu et al [10] combined billboard location selection and visualization together using large-scale taxi GPS trajectory data. Zhang et al [12] proposed a trajectory-driven model for billboard placement. But few studies combine the user interest and the outdooring advertising.…”

Section: Related Work a Trajectory Data For Outdoor Advertisingmentioning

confidence: 99%

Interest-Driven Outdoor Advertising Display Location Selection Using Mobile Phone Data

et al. 2019

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The modern outdoor advertising industry is introducing new technologies to attract attention and enhance effective reach. However, displaying the pertinent advertisements (ads) and maximizing their coverage are still a challenge, as little is known about the interests of passersby. In this paper, we attempted to fill this gap by 1) identifying the individuals' interests extracted from the mobile phone internet usage data, and 2) analyzing the individuals' mobility patterns acquired from the mobile phone location data. Based on these data, the problem of distributing the outdoor ads to the maximum reach of the potentially interested users was further formulated as a maximal covering location problem (MCLP). Specifically, a continuous space maximal coverage model, the MCLP-complementary coverage (MCLP-CC), was utilized to search for the optimal locations for a given category of advertisements. Finally, the proposed methodology was applied to Wuxue, a city in central China, as a case study. The calculation results show that when setting the same number of places for outdoor advertising, our approach achieves an average of 69% improvement compared with the current dominant ads placement approach (selecting the most crowded location) in reaching the potential target audiences. An average of 15% improvement compared with the classic MCLP model. Overall, this paper demonstrates the value of the mobile phone data for interest-driven outdoor advertising and its potential applications in other mobile services. INDEX TERMS Interest-driven outdoor advertising, maximal covering location problem, mobile phone data.

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Trajectory-driven Influential Billboard Placement

Cited by 65 publications

References 27 publications

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Optimization of real-world outdoor campaign allocations

Interest-Driven Outdoor Advertising Display Location Selection Using Mobile Phone Data

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