Developing a dynamic/adaptive geofencing algorithm for HVTT cargo security in road transport

Abstract Cargo security is one of the most critical issues in modern logistics. For high-value theft-targeted (HVTT) cargo the driving phase of transportation takes up a major part of thefts. Dozen fleet management solutions based on GNSS positioning were introduced in recent years. Existing tracking solutions barely meet the requirements of TAPA 2020. Map-matching algorithms present valuable ideas on handling GNSS inaccuracy, however, universal map-matching methods are overcomplicated. Commercial map data providers require additional fees for the use of real-time map-matching functionality. In addition, at the map-matching stage, information on the actual distance from which the raw data was captured is lost. In HVTT security, the distance between the raw GNSS position and map-matched position can be used as a quantitative security factor. The goal of this research was to provide empirical data for TAPA TSR 2020 Level 1 certification in terms of tracking vehicles during typical operating conditions (cargo loading, routing, transportation, stopover, unloading) as well as detecting any geofencing violations. The Dynamic Geofencing Algorithm (DGA) presented in this article was developed for this specific purpose and this is the first known pulication to examine TAPA Standarization in terms of cargo positioning and fleet monitoring. The DGA is adaptive geometric-based matching (alternately curve-to-curve, point-to-curve, point-to-point). The idea behind the algorithm is to detect and eliminate the atypical matching circumstances—namely if the raw position is registered at one of the exceptions described in the paper. The problem of dynamic/adaptive cartographic projection is also addressed so that the robus Euclidean calculactions could be used in global scale.