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CART Codes 2024
CART Codes 2024 Hackathon
HACK HOW WE MOVE
The Climate Action Research for Transportation (CART) Network hosted it's inaugural CART Codes 2024 | Hack How We Move, in Halifax, NS, on February 10-11, 2024 as part of their five-year project funded by the Climate Action and Awareness Fund from Environment and Climate Change Canada. The event was spearheaded by Dalhousie Transportation Collaboratory (DalTRAC) and the Intelligent Automation in Software EngineeRing (RAISE) Lab. CART Codes was a fully in-person 24-hour event which generated collaboration amongst computer science, engineering, and planning disciplines. Students worked in teams of up to four individuals to hack and model how we move.
As of 2022, the transportation sectors is one of the major sources of greenhouse gas (GHG) emissions which comprises 24% of total GHG in Canada. Federal, provincial, and municipal governments have already adopted various climate action policies such as vehicle electrification, carbon trading, among others to tackle climate change impacts on residents and infrastructure. To strengthen Canada's effort in achieving 2050 net-zero emissions, the use of sustainable transportation and tracking individual level carbon footprints are critical. Data collection and evidence-based decision making are more important now than ever to establish benchmarks, quantify progress, and enact policy. The complexity of these problems demand collective action to enhance technical capacity of solving climate change challenged and inform proactive decisions and policies at the individual, local, and national levels.
CART Codes focused on climate change actions within Nova Scotia by producing valuable data tools that aided in the advancement of activity-based travel data collection and modelling that had already been established by DalTRAC. Hackers were tasked with developing methods/tools/games/applications as a solution to the project streams identified:
STREAM 1: Mobile Application Development
STREAM 2: Data Mining & Predictive Modelling
STREAM 3: City Modelling for Urban Games
STREAM 4: Design your Sustainable City
The 24-hour hackathon showcased team work, hard work, and innovation. Teams came up with exciting and innovation solutions to track users mode choices, distance and time travelled, and created accessible systems to incentives users to make eco-friendly choices.
CART Codes 2024 judges were carefully selected to reflect the computer science, engineering, and planning disciplines participating in the event. Hackers had the opportunity Judges included:
Dr. Lisa Bergland
School of Planning, Dalhousie University
Dr. Hamid Afshari
Dept. Industrial Engineering, Dalhousie University
Usmi Mukherjee
PhD Student, Faculty of Computer Science, Dalhousie University
Dr. Jahed Alam
School of Planning & Dept. Civil Engineering, Dalhousie University
Dr. Masud Rahman
Faculty of Computer Science, Dalhousie University
Congratulations to our Top 3 Teams!
1st Prize Winner: Team Gamma
Rahul Saliya, Lokeshwar Kumar Tabjula, and Darshil Patel
ECONAV is an Android app designed for user navigation. Its main objective is to determine the optimal route based on the user's preferred mode of transportation. In addition to basic directions, ECONAV offers comprehensive information to aid users in selecting the most suitable route, considering factors like CO2 emissions, calories burned, and estimated travel time. The application collects data for future use in machine learning models, aiding in predicting outcomes and generating business intelligence insights. Its future scope involves maintaining a travel history database, tracking vehicle usage and travel statistics, which can be leveraged by machine learning models to anticipate user behavior patterns.
2nd Prize Winner: Team Epsilon
Naqueeb Shamsi, Sri Ramya Basam, Ashpak Rakeeb Mohammad, and Nikhil Reddy Aileni
PathFinder is an AI-powered solution designed to provide extensive guidance on public transit, focusing specifically on bus routes in Halifax. Users can initiate conversations through multiple interfaces, including web chatbots, phone calls, or text messages, effortlessly accessing optimal bus route information on their device. The core objective of PathFinder is to streamline the retrieval of public transit details through conversational interactions, utilizing AWS services to guarantee scalability, reliability, and user satisfaction. Its architecture is intricately crafted on AWS, leveraging managed services to support scalability, ensure reliability, and simplify maintenance tasks.
3rd Prize Winner: Team Zeta
Mina Valaei and Ali Mohmoudi Jabdaragh
The integration of renewable energy into transportation systems holds significant implications for sustainability. This includes preprocessing and cleaning data related to renewable energy adoption, crucial for ensuring accuracy and reliability in subsequent analyses. Predicting CO2 emissions under various scenarios aids in understanding the environmental impact of renewable energy transition, guiding policymakers in decision-making. Additionally, developing mathematical models to analyze future congestion patterns enables stakeholders to anticipate challenges and assess the impact on traffic flow and overall transportation efficiency.
