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. / Lokaliserings- eller rörelseprognosering kan beskrivas som uppgiften att förutsäga ett objekts framtida placering med hjälp av de tidigare platserna för objektet. Intresset för problemet ökar i och med införandet av platsbaserade tjänster och autonoma fordon. Även om det är vanligare att studera kortsiktiga förutsägelser, särskilt när det gäller fordon, kan långsiktiga förutsägelser vara användbara i många applikationer som schemaläggning, resurshantering eller trafikprognoser. I detta masterprojekt presenterar jag en feature-representation av rörelse som kan användas för att lära in långsiktiga rörelsemönster och för långsiktig rörelseprediktion både i rymden och tiden. Representationen bygger på periodicitet i data och är baserad på att dela upp banan i fönster och sedan beräkna viktade n-grams av banorna från de olika fönstren. Algoritmen utvärderas på transportdata för tunga transportfordon för att bedöma dess förmåga att från ett sökindex hämta fordon som med stor sannolikhet kommer att röra sig längs en rutt som matchar ett önskat transportuppdrag. Experimentella resultat visar att algoritmen kan uppnå ett konsekvent lågt fel i relativt predikterat avstånd över olika transportlängder i ett begränsat geografiskt område under verkliga förhållanden. Resultaten indikerar även att den totala populationen av fordon i indexet är en kritisk faktor för algoritmens prestanda och därmed även för dess applicerbarhet för verklig användning.

This work deals with the and preemptive road users behaviour safety analysis problem. Safety analysis is based on a processing of road users trajectories obtained from processed aerial videos captured by drons. A system for traffic conflicts detection from spatial-temporal data is presented in this work. The standard approach for pro-active traffic conflict indicators evaluation was extended by simulating traffic objects movement in the scene using Ackerman steering geometry in order to get more accurate results.

Stock market analysis is a hot-button topic, especially with the growth of online communities surrounding trading and investment. The goal of this paper is to examine the sentiment of r/wallstreetbets and the Google Trends score for a number of stocks – and then understanding whether the herding nature of investors on r/wallstreetbets is better at predicting the movement of the stock market than the WOC nature of Google Trends scores. Some combination of the herding and WOC values will also be used in predicting stock market fluctuations. Analysis will be done through the machine learning algorithms RFC and MLP. Through the mean and median precisions presented by the different machine learning algorithms the effectiveness of the predictor can be understood. This paper finds no real connection between either r/wallstreetbets sentiment or Google Trends data regarding predicting stock value fluctuations – with r/wallstreetbets yielding approximately 51%-52% mean precision depending on the machine learning algorithm used, and Google Trends precisions sitting at around 51%. The combination of r/wallstreetbets data and Google Trends data did not produce any significantly higher precision either, being between 51%-52%.