Long-term vehicle movement prediction using Machine Learning methods

Sammanfattning: The problem of location or movement prediction can be described as the task of predicting the future location of an item using the past locations of that item. It is a problem of increasing interest with the arrival of location-based services and autonomous vehicles. Even if short term prediction is more commonly studied, especially in the case of vehicles, long-term prediction can be useful in many applications like scheduling, resource managing or traffic prediction. In this master thesis project, I present a feature representation of movement that can be used for learning of long-term movement patterns and for long-term movement prediction both in space and time. The representation relies on periodicity in data and is based on weighted n-grams of windowed trajectories. The algorithm is evaluated on heavy transport vehicles movement data to assess its ability to from a search index retrieve vehicles that with high probability will move along a route that matches a desired transport mission. Experimental results show the algorithm is able to achieve a consistent low prediction distance error rate across different transport lengths in a limited geographical area under business operation conditions. The results also indicate that the total population of vehicles in the index is a critical factor in the algorithm performance and therefore in its real-world applicability.