Leveraging Novel Data Sources for Travel Behavior Modeling

QC 20250410

Abstract

Travel behavior models are essential for transportation planning and policydevelopment, addressing challenges like traffic congestion, environmentalimpact, and equitable access. By analyzing how individuals make travelchoices, these models support decisions related to infrastructure investmentand resource allocation. These models cover various aspects of travel, including activity planning, route selection and travel time and areconstantly being revised. One of the key ground of improvements are theemergence of novel data sources, significantly advancing the understandingof travel behavior and overall transportation planning. This thesis fitswithin the stream of studies that investigates travel behavior modelsusing novel data sources to guide policies that enhance mobility, supportsustainability, and promote equity in transportation systems, by means of 4distinct papers.

Paper 1 focuses on adapting mobile network data to Scaper, a dynamicdiscrete choice model. The Scaper framework, originally designed foractivity generation and scheduling based on travel survey data, is tailoredto handle the big data source and adapt accordingly. The study developsprobabilistic models by integrating observed and latent states to infertrip attributes from cell tower observations. It employs a backwardinduction method to compute the expected value function, using StochasticExpectation-Maximization for parameter estimation. This paper offersa methodological contribution, demonstrating the potential of how toeffectively adapt an activity based model Scaper to new data sources. Toillustrate the usefulness of this framework, we emphasize its application in Paper 2. This new framework is used for assessing mobility inequality andsegregation before and during COVID-19 in Stockholm. This shows howwe can use these models and data to further investigate mobility patternsduring times of crisis and to envision a more resilient transport system thatpromotes equity.

In line with the thesis’s scope of integrating sustainability into research,we use route choice models and GPS traces to investigate cycling behavior. Paper 3 primarily focuses on cyclists’ route preferences in the Netherlands.Notably, cyclists, including commuters, do not always choose the shortestpath. Instead, various factors influence their decisions, raising the importantquestion: how can we design infrastructure that aligns with cyclists’preferences and encourages more frequent cycling? the detailed GPS tracesallowed for to investigate various aspects of the route beyond distance,for instance, number of junctions, traffic lights, presence of nature, etc.This paper utilizes two approaches to address this question. The firstis theory-driven based on logit models, the Path Size Logit (PSL) andthe Pairwise Combinatorial Logit (PCL), both rooted in random utilitymaximization principles and designed to account for route overlap amongchoices. The second is a data-driven approach using deep learning topredict route choices through a one-dimensional Convolutional NeuralNetwork. We conducted a sensitivity analysis to uncover key patternsin the deep learning model, offering insights into the factors influencingroute preferences. By comparing these two approaches, we emphasize theirstrengths and limitations while showing how GPS data integrates with themto uncover key factors influencing cyclists’ route choices. This paper guidespolicymakers in designing efficient and appealing cycling routes.

Paper 4 expands the scope by incorporating GPS data alongsidesociodemographic information to examine cycling behaviors, particularlyin a cross-border context. Data were collected from three cities, namely,Braga, Istanbul and Tallinn. The focus is on travel time: What are theaverage and range of travel time for cyclists in different cities? How dofactors such as age, and gender influence travel time? Are there differencesbetween different cities? Travel time is a crucial variable for travel demandiimodeling but more so for cyclists, as they do not always prioritize speed. Alonger trip isn’t necessarily worse; it might even be preferred if the shorteralternative is more exhausting. Novel data sources like GPS traces collectedover period of months in three different cities provides the opportunity tounderstand these complex and comparative behavioral contexts. Cyclingunderscores not only the value of time but also the quality of time spentengaging in the activity. It’s within this context that travel time modelingbecomes particularly important to investigate. Using a survival analysisapproach, specifically the Latent Class Accelerated Failure Time (LCAFT)model, Paper 4 reveals how distance, trip purpose and bike type influencethe travel time of cycling and identifies potential latent classes in differentage groups and gender.

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