Abstract
This paper presents a case study on the codification of temporal expert knowledge to enhance Dijkstra’s route planning algorithm to reduce the time to recover stolen vehicles in the UK. The power of the predictive algorithm lies in the route costings where integration of expert knowledge on offender criminal behaviours overcomes the lack of this captured data by technology alone. According to Home Office statistics vehicle theft rates in the UK have been increasing year on year since 2014 [1]. Part of this rise can be attributed to keyless vehicle entry and ignition becoming more popular however, this does not explain the full story. Although vehicle telematics hardware is improving rapidly this has not been supported by an equal increase in use of data for vehicle tracking. This project has explored the feasibility of future innovations in policing with the focus on assisting police officers in their decision making and increasing the probability of success in apprehending offenders of stolen vehicles through codifying their knowledge. Through exploiting the growth in Internet of Things (IoT) technologies, this paper presents a novel approach to vehicle tracking with an integrated path predictive algorithm. Using Dijkstra’s route planning algorithm, with real time geospatial data augmented with information from historical route data and expert knowledge, modified route costings are generated. The algorithm calculates the highest probability escape route that a thief would take and identifies an area ahead of the thief’s current position where the police can position a roadblock to apprehend the criminal.
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MacCallum, E., Jackson, L., Coxhead, J., Jackson, T. (2023). Innovations in Future Crime Decision Making Through the Codification of Temporal Expert Knowledge. In: Uden, L., Ting, IH. (eds) Knowledge Management in Organisations. KMO 2023. Communications in Computer and Information Science, vol 1825. Springer, Cham. https://doi.org/10.1007/978-3-031-34045-1_27
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