Transportation

Predicting vehicle speed at critical road segments, such as pedestrian crossings during left-turn maneuvers at signalized intersections, is essential for improving traffic safety and supporting autonomous driving systems. This thesis presents a novel two-stage hybrid deep learning framework enhanced with reinforcement learning to forecast vehicle left-turn speed at pedestrian crossings.

Using a multivariate time series dataset of vehicle speed and acceleration, the final three seconds of data are intentionally removed to simulate real-world decision-making prior to reaching pedestrian crossings. In stage one, a Convolutional Neural Network (CNN) imputes the removed values. Stage two uses the imputed data to forecast speed, combining Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) networks as feature extractors, followed by a Random Forest Regressor (RFR) for robust speed predictions.

Reinforcement learning is employed to dynamically adjusts the TCN's dilation rate, improving temporal pattern capture. Experimental results show the proposed framework outperforms standalone, hybrid, and state-of-the-art models.

Author Keywords: Data Imputation, Dynamic Dilation, Left-Turn Maneuver, Reinforcement Learning, Temporal Convolutional Network, Time series Forecasting

This research considers the historic context of power that planning operates within, and looks at the ways in which certain community members are marginalized by traditional planning processes. Participatory planning, which has theoretical roots in communicative planning theory, may have the potential to shift the legacy of power and marginalization within planning processes, resulting in improved planning outcomes, more social cohesion, and a higher quality of urban life. I used a community-based research approach to evaluate approaches to participatory urban planning in Peterborough, Ontario. I worked with a community-based active transportation planning project called the Stewart Street Active Neighbourhoods Canada project. This thesis evaluates the participatory planning approaches employed in the project, and determines if they are effective methods of engaging marginalized community members in planning. The research also identifies the professional benefits of participatory planning, and examines the barriers and enablers to incorporating participatory approaches into municipal planning processes. Finally, I developed a set of recommendations to implement participatory planning approaches more broadly in the city of Peterborough, Ontario.

Author Keywords: active transportation, communicative planning theory, community-based research, community engagment, participatory planning, public participation