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| Content Provider | ACM Digital Library |
|---|---|
| Author | Teusner, Ralf Richly, Keven |
| Abstract | Detailed information about the flow of potential customers in a city is extremely relevant for strategic decisions of various service providers such as taxi companies or advertising agencies. The knowledge about highly frequented regions as well as peak times in specific areas provides a crucial business advantage to competitors. Today, business relevant decisions about the positioning of service providers and advertising spaces or the balancing of capacity are primarily based on experience only. In this paper, we present a novel approach to gain knowledge about the distribution of potential customers over time and space based on the data of taxi rides, which have been recorded for documentation purposes. By leveraging the performance of in-memory databases, we build an application, which allows the user to analyze about 700 million taxi rides in real-time. The application allows companies to get an impression in which areas and in what timeframes they can reach a large audience of potential customers. Additionally, we demonstrate that the developed visualization concept enables the comparison of different regions and allows to analyze trends in the customer flow over time. |
| Starting Page | 43 |
| Ending Page | 46 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450342049 |
| DOI | 10.1145/2962735.2962760 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-05-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Spatio-temporal data analysis In-memory data management Urban analytics |
| Content Type | Text |
| Resource Type | Article |
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