Modern traffic management systems often rely on static technologies, such as sensors and CCTV-cameras, in the gathering of data regarding the current traffic situation. Recent reports have shown that this method can result in a lack of coverage in Stockholm, Sweden. In addressing this issue, an alternative strategy to installing more sensors and CCTV-cameras could be to utilize crowdsourced traffic data from other sources, such as Waze. In order to examine the usage and potential utility of crowdsourced data in traffic management, the Swedish Transport Administration’s center in Stockholm, Trafik Stockholm, developed a web application which visualizes traffic data from both official sources and Waze. While the application was successful in doing so, it revealed the problem of integrating the traffic data from these two sources, as a significant portion of the data was redundant, and the reliability occasionally was questionable. This study aims at determining how issues regarding redundancy and reliability can be resolved in the integration of crowdsourced and official traffic data. Conducted using a design science research strategy, the study investigates these issues by designing and developing an artifact that implements integration methods to match alerts from the data sources based on temporal and spatial proximity constraints. The artifact was evaluated through test sessions in which real-time traffic data from all over Sweden was processed, and through acceptance testing with the stakeholders of the application. Analysis of the results from the evaluations shows that the artifact is effective in reducing the redundancy in the crowdsourced data and that it can provide a more solid ground for reliability assessment. Furthermore, the artifact met its expectations and requirements, demonstrating a proof-of-concept and a proof-of-acceptance. Based on these results, the study concludes that by analyzing temporal and spatial factors in crowdsourced data, redundancy issues in the integration of crowdsourced and official traffic can be resolved to a large extent. Furthermore, it is concluded that reliability issues in the same context can be resolved to a high degree by managing redundancy factors in combination with general traffic management factors. While the study is focused on traffic management, the issues of redundancy and reliability are not restricted to crowdsourced data in this context specifically. Thus, the results of the study are potentially of interest to researchers investigating other areas of application for crowdsourcing as well.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-389349 |
| Date | January 2019 |
| Creators | Eriksson, Isak |
| Publisher | Uppsala universitet, Informationssystem |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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