Towards predictive crowd based transport infrastructure monitoring system
Fatjon Seraj

TL;DR
This paper proposes a comprehensive methodology and system architecture for crowd sensing-based transport infrastructure monitoring, addressing challenges posed by hardware heterogeneity, participant inexperience, and environmental uncertainties.
Contribution
It introduces a detailed methodology and system design for crowd sensing in transport infrastructure monitoring, considering hardware/software heterogeneity and dynamic conditions.
Findings
System architecture accommodates heterogeneous smartphones.
Methodology addresses uncertainties and unreliability in data collection.
System satisfies key hardware and software requirements for effective monitoring.
Abstract
To be able to measure relevant data for transport infrastructure monitoring and to obtain maintenance indicators in a crowd sensing-based fashion, a set of requirements (both from hardware and software points of view) needs to be satisfied. Heterogeneity of smartphones and various uncertainties associated with the mainstream \textit{off-the-shelf} hardware combined with the fact that inexperienced participants will take an active part in the data collection process necessitates that the system accounts for dynamicity, uncertainty, unreliability, and heterogeneity of the environment, participants, and technology. Modern Software Development Kits (SDK) and Application Programming Interfaces (API) provided by the mobile Operating Systems allow us to build applications for the smartphones ecosystem and to interact with components, services, and applications. They also provide specifications…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Traffic Prediction and Management Techniques
