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Public transport origin-destination matrices: pattern recognition and short-term prediction / Origin-destination matriser i kollektivtrafiken: Mönsterigenkänning och kortsiktiga prognoserFerranti, Francesco January 2020 (has links)
Origin-Destination (OD) matrices are an essential tool in transport planning and management to model user travel patterns. An OD matrix is a picture of the public transport passengers demand in a specific temporal window The use of the metropolitan transportation system as an alternative to private cars enables a decrease of CO2 emissions, air pollution and traffic noise. Public opinion and governments are giving more and more attention to these environmental issues. The overtaking of the public transports on private means of transportation is one of our best weapons to fight global warming. Efficiency is particularly important when we analyse the passenger behaviour in the transport networks of large cities. There we deal with large and growing populations, and it is required a methodical response to the transport system critical events,like congestion of the network during the peak hours. Recently, modern technologies and data driven initiatives enabled large-scale data collection of travel patterns. In particular, smart card validation data can now be stored and used for evaluation and planning purposes. From these data, anonymous spatio-temporal travel patterns can be revealed and studied, and OD matrices with various aggregation levels can be easily generated. With this work, we gather different techniques used in the practice, and propose a methodological framework for the study of large-scale OD matrices. We focus our attention on the use of dimensionality reduction techniques (Principal Component Analysis (PCA), Singular Value Decomposition (SVD)), day clustering algorithms (K-Means, Affinity Propagation), and short-term flow prediction models (Vector Autoregression (VAR), Autoregressive Integrated Moving Average (ARIMA)) on the public transport usage data. Our case study considers the OD matrices for the whole metro and commuter train network in the region of Stockholm, Sweden. With this thesis, we want to reveal and discuss the potential of day clustering methods to detect trends of the passenger flows, and their combination with dimensionality reduction techniques to perform short-term prediction of the flows. / Modern teknik och datadrivna initiativ har nyligen möjliggjort storskalig datainsamling av resmönster. I synnerhet kan nu valideringsdata från smartkort lagras och användas för utvärdering och planering. Från denna data kan anonyma spatiala och tidsberoende resmönster hittas och studeras, och Origin-Destination (OD) matriser med olika aggregeringsnivåer enkelt genereras. OD-matriser är grundläggande verktyg inom transportplanering och förvaltning för att modellera resmönster och ger en bild av kollektivtrafikens efterfrågan inom ett specifikt tidsfönster. När passagerarbeteenden analyseras i transportnätverk i större städer är optimeringen av den operativa effektiviteten ett särskilt viktigt fokus. Eftersom det inte bara finns en stor och växande befolkning som sätter hög belastning på nätverket utan också för att kritiska händelser i transportsystemet, såsom trängsel i nätet under rusningstrafik, kräver ofta en långsamt systematiskt respons. Dessutom kan ett effektivt transportsystem minska utsläppen av CO2, luftföroreningar och trafikbuller genom en minskad användning av privata transportmedel. Detta arbete tittar på olika tekniker som används i praktiken för att modellera resmönster och föreslår ett metodisk ramverk för studier av storskaliga OD-matriser. Särskild uppmärksamhet läggs på användningen av dimensionsreduktionstekniker (Principal Component Analysis (PCA), Singular Value Decomposition (SVD)), klustringsalgoritmer (K-Means, Affinity Propagation) och prediktionsmodeller för kortsiktiga trafikflöden (Vector Autoregression (VAR), Autoregressive Integrated Moving Average (ARIMA)) på data från kollektivtrafiken. Vår fallstudie kollar på OD-matriserna för hela tunnelbane- och pendeltågsnätet i Region Stockholm, Sverige. Datan används sedan för att visa potentialen i klustringsmetoderna för att upptäcka trender i passagerarflöden och hur de presterar tillsammans med dimensionsreduktionstekniker för att utföra kortsiktiga flödesprognoser.
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Developing transport interaction macromodels to simulate traffic patterns : Case of Oslo, NorwayParishwad, Omkar January 2022 (has links)
Predicting the passenger flow inside a city is a vital component of the intelligent transportation management system. The proposal for a new residential area, an office space, postpandemic policy implications for work from home, behavioral changes for revised traffic patterns, infrastructural improvements, require a visual and analytical backing which can be provided through a macro simulation model. This research explores the performance of the Machine learning (ML) based transport model against the predictions provided by the traditional Spatial Interaction Models (SIM) for the city of Oslo. The transport models and their parameters are analyzed for sensitivity analysis and scenario analysis to derive city character. Furthermore, the derived model is deployed over an interactive dashboard for analytical and their practical visualizations through infographics. The results show that the ML model outperforms the SIM. Although the traditional SIM has a clear advantage of being interpreted by design and requiring a few parameters, it suffers from its inability to accurately capture the structure of real flows and greater variability as compared to the ML model. Extensive statistical analyses are conducted to obtain significant results and realize the pros and cons of both the models which question the validity of results for the ML model over SIM. With this thesis, we discuss the potential of ML model detected trends of passenger flows, andtheir capacity to simulate city developmentrelated scenarios for the traffic flows within the city.
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