Optimizing Impression Counts for Outdoor Advertising

Zhang, Yipeng; Li, Yuchen; Bao, Zhifeng; Mo, Songsong; Ping, Zhang

doi:10.1145/3292500.3330829

Cited by 28 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These works aim at improving the influence of advertisements by selecting the advertisement on the billboards. In [18], Zhang, Yipeng et al optimize the influence of outdoor advertising (ad) with the consideration of impression counts. They propose a tangent line based algorithm to select roadside billboards for maximizing the influence of outdoor advertising.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Lou

Yang

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

show abstract

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Lou

Yang

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…In addition to the literature above, there are some other domains which are slightly related to our work, such as spatial object selection [7,8,37,38] and maximizing bichromatic reverse k nearest neighbor (MaxR𝑘NN) problem [4,19,42]. Specifically, Guo et al [7] define an influence maximization problem on trajectories, which aims to find a subset of trajectories with the maximum expected influence among a group of audiences.…”

Section: Other Related Areasmentioning

confidence: 99%

“…Zhang et al [38] extend the billboard problem of Zhang et al [37] to support the inclusion of impression counts. Guo et al [8] study how to select a set of representative spatial objects from the current region of users' interest.…”

mentioning

confidence: 99%

Let Trajectories Speak Out the Traffic Bottlenecks

Luo¹,

Bao²,

Cong³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification: Given a road network 𝑅, a trajectory database 𝑇 , find a representative set of seed edges of size 𝐾 of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF, with an approximation ratio of 1 − 1/𝑒. To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG. Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

show abstract

“…Trajectories are obtained from real-world taxi trip records ([7] for Chicago and [12] for NYC) as below: each trip record consists of a pickup and a drop-off location, travel time and travel distance; for each trip we find its shortest path, and if it has a similar travel distance and time (within 5% error rate) with this trip, we treat it as an approximation of the trip's real trajectory. The above way is also used in trajectory-driven site selection problem [69,70]. Detailed dataset statistics can be found in Table 5.…”

Section: Experiments 71 Setupmentioning

confidence: 99%

Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand

Wang,

Sun,

Musco

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we make a first attempt to incorporate both commuting demand and transit network connectivity in bus route planning (CT-Bus), and formulate it as a constrained optimization problem: planning a new bus route with 𝑘 edges over an existing transit network without building new bus stops to maximize a linear aggregation of commuting demand and connectivity of the transit network. We prove the NP-hardness of CT-Bus and propose an expansion-based greedy algorithm that iteratively scans potential candidate paths in the network. To boost the efficiency of computing the connectivity of new networks with candidate paths, we convert it to a matrix trace estimation problem and employ a Lanczos method to estimate the natural connectivity of the transit network with a guaranteed error bound. Furthermore, we derive upper bounds on the objective values and use them to greedily select candidates for expansion. Our experiments conducted on real-world transit networks in New York City and Chicago verify the efficiency, effectiveness, and scalability of our algorithms.

show abstract

Optimizing Impression Counts for Outdoor Advertising

Cited by 28 publications

References 27 publications

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Let Trajectories Speak Out the Traffic Bottlenecks

Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand

Contact Info

Product

Resources

About