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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

A supervised learning approach for transport mode detection using GPS tracking data

Ivanov, Stepan, Sakellariou, Stefanos January 2022 (has links)
The fast development in telecommunication is producing a huge amount of data related to how people move and behave over time. Nowadays, travel data are mainly collected through Global Positioning Systems (GPS) and can be used to identify human mobility patterns and travel behaviors. Transport mode detection (TMD) aims to identify the means of transport used by an individual and is a field that has become more popular in recent years as it can be beneficial for various applications. However, developing travel models requires different types of information that can be extracted from raw travel data. Although many useful features like speed, acceleration and bearing rate can be extracted from raw GPS data, detecting transport modes requires further processing. Some previous studies have successfully applied machine learning algorithms for detecting the transport mode. Despite achieving high performance in their models, many of these studies have used rather small datasets generated from a limited number of users or identified a small number of different transport modes. Furthermore, in most of these studies more complex methodologies have been applied, where extra information like GIS layers or road and railway networks were required. The purpose of this study is to propose a simple supervised learning model to identify five common transport modes on large datasets by only using raw GPS data. In total, six commonly used supervised learning algorithms are tested on seven selected features (extracted from raw GPS data). The Random Forest (RF) algorithm proves to perform better in detecting five transport modes from the dataset utilized in this study, with an overall accuracy of 82.7%.
2

A contemporary machine learning approach to detect transportation mode - A case study of Borlänge, Sweden

Golshan, Arman January 2020 (has links)
Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.
3

Användning av sensordata för att detektera smartphoneanvändares transportmedel

Johansson, Jonas, Jonsson Ewerbring, Marcus January 2019 (has links)
Ett sätt att informera smartphone-användare om deras klimatpåverkan är genom att automatiskt identifiera vilket transportmedel användaren nyttjat och använda informationen för att uppskatta användarens utsläpp av växthusgaser. Målet med det här projektet var att sammanställa en översikt av befintliga lösningar och metoder för att detektera smartphone-användares transportmedel och utvärdera hur ett system presterar då testdata är samlad i ett annat geografiskt område än datan som använts för att träna systemet. Utvärdering av systemet skedde via kvantitativa metoder där sensordata samlades in och användes för att testa systemet. Sensordata samlades vid gång, stilla, tåg, buss och bil. Resultatet är ett system som med varierande sannolikhet kan avgöra transportmedel i Sverige. Systemets totala precision var 29 procentenheter lägre då data som samlats i Sverige användes i testerna jämfört med data insamlad i samma geografiska område som träningsdatan. Slutsatsen är att det kan vara problematiskt att applicera en lösning i ett annat geografiskt område än lösningen utvecklats för. Genom testerna framkom att fordonstransport verkar särskilt känsligt vid byte av geografisk kontext. / A way to inform smartphone users about their climate impact is by automatically identifying their means of transport and use the information to estimate the user's emissions of greenhouse gases. The aim of this project was to create an overview of existing solutions and methods for detecting smartphone users' means of transport and evaluating how a system performs when test data is collected in a different geographical area than the data used to train the system. Evaluation of the system was done via quantitative methods where sensor data was collected and used to test the system. Sensor data was collected by walking, still, train, bus and car. The result is a system that, with varying probability, can determine the means of transport in Sweden. The system's total accuracy was 29 percentage points lower when data collected in Sweden was used in the tests compared to data collected in the same geographical area as the training data. The conclusion is that it can be problematic to apply a solution in a different geographical area than where the solution was developed for. The tests showed that vehicle detection seems particularly sensitive to changing geographical context.
4

Human mobility behavior : Transport mode detection by GPS data

Sadeghian, Paria January 2021 (has links)
GPS tracking data are widely used to understand human travel behavior and to evaluate the impact of travel. A major advantage with the usage of GPS tracking devices for collecting data is that it enables the researcher to collect large amounts of highly accurate and detailed human mobility data. However, unlabeled GPS tracking data does not easily lend itself to detecting transportation mode and this has given rise to a range of methods and algorithms for this purpose. The algorithms used vary in design and functionality, from defining specific rules to advanced machine learning algorithms. There is however no previous comprehensive review of these algorithms and this thesis aims to identify their essential features and methods and to develop and demonstrate a method for the detection of transport mode in GPS tracking data. To do this, it is necessary to have a detailed description of the particular journey undertaken by an individual. Therefore, as part of the investigation, a microdata analytic approach is applied to the problem areas, including the stages of data collection, data processing, analyzing the data, and decision making. In order to fill the research gap, Paper I consists of a systematic literature review of the methods and essential features used for detecting the transport mode in unlabeled GPS tracking data. Selected empirical studies were categorized into rule-based methods, statistical methods, and machine learning methods. The evaluation shows that machine learning algorithms are the most common. In the evaluation, I compared the methods previously used, extracted features, types of dataset, and model accuracy of transport mode detection. The results show that there is no standard method used in transport mode detection. In the light of these results, I propose in Paper II a stepwise methodology to detect five transport modes taking advantage of the unlabeled GPS data by first using an unsupervised algorithm to detect the five transport modes. A GIS multi-criteria process was applied to label part of the dataset. The performance of the five supervised algorithms was evaluated by applying them to different portions of the labeled dataset. The results show that stepwise methodology can achieve high accuracy in detecting the transport mode by labeling only 10% of the data from the entire dataset.  For the future, one interesting area to explore would be the application of the stepwise methodology to a balanced and larger dataset. A semi-supervised deep-learning approach is suggested for development in transport mode detection, since this method can detect transport modes with only small amounts of labeled data. Thus, the stepwise methodology can be improved upon for further studies.

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