Pedestrian Dynamics in Smart Cities: Ubiquitous Sensing, Interactions, and Models
Bibliographic record
Abstract
The dissertation explores the influence of emerging technologies on the dynamics of urban areas from a pedestrian-oriented perspective. To evaluate this influence, novel data sources and modern data-driven approaches replace traditional data collection methods and modelling techniques. Part of the dissertation establishes new tools and techniques to predict and explain pedestrian behaviour that concerns the future dynamics in urban areas. Specifically, this dissertation considers: (i) what are the novel data sources that can capture detailed information on pedestrian behaviour and how can they be used efficiently? (ii) how to extract valuable information from these new high- dimensional data sources? (iii) how to utilize the data and tools to predict pedestrian behaviour in the future urban environment? and (iv) how to interpret and explain pedestrian behaviour in the context of smart cities? This thesis is based on four articles introduced in Chapters 3 to 6. Chapter 3 introduces a semi-supervised residual network for transportation mode detection using passively collected labelled and unlabelled Wi-Fi signal data. Chapter 4 provides a survival analysis to model the wait time behaviour of a crossing pedestrian and analyze the effect of smartphone distraction on pedestrian behaviour. Virtual reality is used in this chapter as a means of data collection in a controlled environment. Chapter 5 highlights pedestrian crossing behaviour in the presence of automated vehicles. A large virtual reality data collection campaign is conducted to understand pedestrian behaviour in futuristic scenarios. Data-driven survival analysis is developed to analyze pedestrian wait time before mid-block unsignalized crossings. By using a post-hoc model interpretation, the contributing factors to pedestrian wait time are assessed. In Chapter 6, a neural network architecture is developed to incorporate sequential time-series data and contextual information to predict pedestrian trajectory. The proposed framework is applied to the virtual reality dataset. Methodological and data collection frameworks explored in this dissertation provide solutions for detecting, modelling and predicting pedestrian behaviour in a futuristic context. This dissertation con- tributes to the field of transportation by proposing alternative data collection methods, developing novel data-driven methodologies and analyzing pedestrian behaviour in the context of automated vehicles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".