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Analysis of Bicycle Sharing Data for Decision Support to Expanding Tartu Cycling Infrastructure
(Tartu Ülikool, 2024) Tarro, Martti; Pourmoradnasseri, Mozhgan, juhendaja; Tera, Helen, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Public bicycle sharing systems have seen an increase in use across many cities in the world, and their expansion is partially dependent on the state of the infrastructure. Traditional road planning procedures rely on empirical evidence and surveys. The widespread availability of GPS data from micromobility sharing systems have seen the approaches enhanced by the analysis of movement patterns. Tartu has strategic plans for expansion of its cycling network to make the city as well as its surroundings more accessible by cycling. This thesis examines the patterns of Tartu Bike Share users using their geolocation data, and compares the planned networks to proposed paths from four strategies for prioritisation of new road sections. The strategies focus on evaluating current cycling patterns, estimating optimal paths, finding ways to make the current network more cohesive, and a combination of these strategies through MULTIMOORA modelling. The prioritised gaps from the model include multiple potential cycling paths, that had not been included in the planning of the main and auxiliary networks. The cycling network in Tartu is already expansive, but identifying significant gaps in the current and planned networks has the chance to improve it yet more.
Veebipõhine koondpaneel Tartu rattaringluse andmete visualiseerimiseks ja analüüsimiseks
(Tartu Ülikool, 2025) Talioja, Rasmus; Tera, Helen, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Tartu bike-sharing has collected a large volume of ride and GPS data over five years but lacking a comprehensive interactive analysis tool. The objective of this thesis was to develop a web-based dashboard for visualizing and analyzing this data. The application was built using Svelte/SvelteKit, MapLibre and DuckDB. The main challenge was visualizing large spatial datasets. This was solved by processing data into vector tiles using PMTiles format. The result is a functional dashboard enabling temporal and spatial analysis of bike-sharing usage, including key metrics, station-based data and map visualizations of routes and heatmaps.