This thesis explores the possibility of identifying risk behaviour patterns among forklift drivers through the analysis of telemetry data using unsupervised clustering algorithms. The objective is to predict whether certain behaviour patterns increase the risk of accidents. With the increasing accessibility of Internet of Things technology, data from forklifts has become more available, allowing for the study of driver behaviour. The telemetry data utilised is sourced from Toyota Material Handling Manufacturer Sweden’s internal database, collected from Data Handling Units that are installed on forklifts across Europe. This data, referred to as shock data, is triggered when a force is applied to the forklift, such as a collision. The thesis investigates combinations of various clustering algorithms and dataset modifications. The evaluation of the results is conducted using several quantitative measures and visualisation, along with analysis of time distribution, geographical placement, comparison of forklift models, and comparison with "no-shock" data. The evaluation yields K-Prototypes and K-Means as the best performing algorithms, while indicating that soft clustering and density-based clustering are not well-suited for the data. The identified best performing algorithms reveal two recurring driver behaviour patterns: the first one being driving forward at high speed with the lift motor idle, and the second pattern being driving backward at low speed while lowering the forks. Furthermore, a majority of the data points remain unclassified into specific behaviour patterns, suggesting that the dataset or methods used may not be sufficient enough. The inclusion of additional featuers, such as steering angle and forklift height, should be considered for exploration in future work. The thesis demonstrates the feasibility of identifying risk behaviour patterns, with potential for future research expanding on the findings to further contribute to the prevention of workplace accidents involving forklifts.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-203477 |
| Date | January 2024 |
| Creators | Zachrison, Unn, Winqvist, Victoria |
| Publisher | Linköpings universitet, Artificiell intelligens och integrerade datorsystem |
| 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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