Toronto Metropolitan University
Kalatian, Arash.pdf (15.42 MB)

Pedestrian Dynamics in Smart Cities: Ubiquitous Sensing, Interactions, and Models

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posted on 2023-08-25, 20:12 authored by Arash Kalatian

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.





  • Doctor of Philosophy


  • Civil Engineering

Granting Institution

Ryerson University

LAC Thesis Type

  • Dissertation

Thesis Advisor

Dr. Bilal Farooq



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