Smart Urban Traffic Solutions

Team QWERTY 2.0
Artem Dolhiy & Diana Shakhova
Industry 4.0

Project Overview

Urban centers like Toronto face escalating traffic congestion, impacting daily commutes, public safety, and the environment. Our project leverages real-world traffic data (2020–2029) from Toronto to analyze congestion patterns and develop data-driven, actionable recommendations. We aimed to transform Toronto’s transportation network into a smarter, more efficient, and environmentally friendly system.

Problem Statement

As outlined in the University Competition Mission Brief :contentReference[oaicite:0]{index=0}, our challenge was to address urban congestion by:

Analyzing traffic patterns: Identifying peak congestion times and bottleneck locations.
Optimizing transit systems: Proposing improvements such as retimed traffic signals, enhanced bus routes, and alternative solutions to reduce delays.
Enhancing sustainability and safety: Developing recommendations that lower emissions and improve pedestrian and cyclist safety.

Our Approach

1. Data Exploration & Analysis

Dataset Processing: We cleaned and processed a comprehensive dataset covering Toronto traffic volumes, intersection counts, and time-based trends.
Time-Series & Spatial Analysis: Using Python libraries (Pandas and Matplotlib), we analyzed the data to identify rush-hour peaks (7–9 AM and 4–6 PM) and pinpointed high-congestion areas across the city.

2. Data Visualization

We developed a suite of visualizations to uncover and communicate key insights:

Heatmaps & Flow Diagrams: Highlighting congestion hotspots and travel patterns.
Interactive Folium Maps: Displaying the top congested intersections in Toronto.
Time-Series Plots: Comparing weekday versus weekend traffic trends and identifying peak periods.
Additional Charts: Bar charts and pie charts that break down the modal split between cars, buses, trucks, pedestrians, and cyclists.

3. Data-Driven Solutions

Based on our findings, we devised a set of targeted interventions:

Traffic Signal Optimization: Extend green phases on major commuter routes during peak hours to improve flow.
Public Transit Enhancements: Increase bus frequency and add dedicated bus/HOV lanes near downtown and major corridors (Scarborough/Etobicoke).
Logistics Management: Incentivize off-peak deliveries for trucks in core commercial districts.
Pedestrian & Cyclist Infrastructure: Improve crosswalks, sidewalks, and bike lanes in congested areas to boost safety and encourage modal shift.

Key Findings

Commuting Patterns: Our analysis confirmed that peak congestion is driven by morning and evening rush hours, particularly in downtown Toronto.
Spatial Concentration: Major intersections and corridors are the primary congestion hotspots.
Modal Dependence: A heavy reliance on private vehicles was evident, highlighting the need to boost public transit and non-motorized options.

Our results, including a Moran’s I value of 0.834 (p-value = 0.001), reinforce that commuting patterns significantly shape traffic flow.
(Qwerty Slidedeck)

Policy Recommendations

Adaptive Traffic Signal Control

Implement sensor-driven systems that dynamically adjust signal timings during peak hours.

Enhanced Public Transit

Increase Bus Frequency: Particularly during rush hours, with new bus lanes/HOV corridors in key areas.
Promote Alternatives: Incentivize off-peak truck deliveries and encourage staggered work hours to reduce commuter load.